Reviewerly/exHarmony huggingface dataset:
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management

Agriculture faces unprecedented challenges due to climate change, population growth, and water scarcity. These challenges highlight the need for efficient resource usage to optimize crop production. Conventional techniques for forecasting hydrological response features, such as soil moisture, rely on physics-based and empirical hydrological models, which necessitate significant time and domain expertise. Drawing inspiration from traditional hydrological modeling, a novel temporal graph convolution neural network has been constructed. This involves grouping units based on their time-varying hydrological properties, constructing graph topologies for each cluster based on similarity using dynamic time warping, and utilizing graph convolutions and a gated recurrent neural network to forecast soil moisture. The method has been trained, validated, and tested on field-scale time series data spanning 40 years in northeastern United States. Results show that using domain-inspired clustering with time series graph neural networks is more effective in forecasting soil moisture than existing models. This framework is being deployed as part of a pro bono social impact program that leverages hybrid cloud and AI technologies to enhance and scale non-profit and government organizations. The trained models are currently being deployed on a series of small-holding farms in central Texas.
Reliable Neuro-Symbolic Abstractions for Planning and Learning

Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.
Research on the Construction of Computer High Level Professional Groups in Higher Vocational Colleges under the Background of the Greater Bay Area

With the deepening of the construction of the Guangdong Hong Kong Macao Greater Bay Area, the goal of building a high-level professional group in vocational colleges is to optimize the professional structure to meet industrial demands, take industrial demands as the fundamental basis for forming professional groups, and cultivate high-quality technical and skilled talents. Our school closely focuses on the construction and development plan of the Greater Bay Area, builds a high-level computer professional group, and strives to promote the integration of industry and education. In order to improve the quality of teaching and promote the high-quality development of talent cultivation in vocational colleges, we have proposed construction ideas and measures.
Levin Tree Search with Context Models

Levin Tree Search (LTS) is a search algorithm that makes use of a policy (a probability distribution over actions) and comes with a theoretical guarantee on the number of expansions before reaching a goal node, depending on the quality of the policy. This guarantee can be used as a loss function, which we call the LTS loss, to optimize neural networks representing the policy (LTS+NN). In this work we show that the neural network can be substituted with parameterized context models originating from the online compression literature (LTS+CM). We show that the LTS loss is convex under this new model, which allows for using standard convex optimization tools, and obtain convergence guarantees to the optimal parameters in an online setting for a given set of solution trajectories --- guarantees that cannot be provided for neural networks. The new LTS+CM algorithm compares favorably against LTS+NN on several benchmarks: Sokoban (Boxoban), The Witness, and the 24-Sliding Tile puzzle (STP). The difference is particularly large on STP, where LTS+NN fails to solve most of the test instances while LTS+CM solves each test instance in a fraction of a second. Furthermore, we show that LTS+CM is able to learn a policy that solves the Rubik's cube in only a few hundred expansions, which considerably improves upon previous machine learning techniques.
Explainable Multi-Agent Reinforcement Learning for Temporal Queries

As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments. This work presents an approach for generating policy-level contrastive explanations for MARL to answer a temporal user query, which specifies a sequence of tasks completed by agents with possible cooperation. The proposed approach encodes the temporal query as a PCTL* logic formula and checks if the query is feasible under a given MARL policy via probabilistic model checking. Such explanations can help reconcile discrepancies between the actual and anticipated multi-agent behaviors. The proposed approach also generates correct and complete explanations to pinpoint reasons that make a user query infeasible. We have successfully applied the proposed approach to four benchmark MARL domains (up to 9 agents in one domain). Moreover, the results of a user study show that the generated explanations significantly improve user performance and satisfaction.
Research on the Evaluation System of Scientific Research Ethics Based on AHP-Fuzzy Comprehensive Evaluation

Scientific research ethics is the value concept and code of conduct to be followed in scientific research, technological development and other scientific and technological activities. However, in recent years, some researchers have ignored ethical constraints in scientific research activities, resulting in more prominent ethical issues in scientific research. This study takes China Southern Power Grid Corporation as an example, based on ISO9000 standards, uses AHP-fuzzy comprehensive evaluation theory to establish a scientific research ethics evaluation system, aiming at improving the timeliness and targeting of scientific research ethics management.
Vision

This chapter presents the primary sense of the human: the vision. The main hardware corresponding to this sense is the display, very common for almost everybody using a computer or a smartphone. But for VR, there are more specific equipments that bring the essential aspect of immersion. We emphasize the head-mounted display and the CAVE-like projection systems.
Accelerating AlphaFold2 Inference of Protein Three-Dimensional Structure on the Supercomputer Fugaku

The three-dimensional (3D) structure of proteins provides essential information for understanding life phenomena and drug discovery. Recently, the AI model AlphaFold2 for predicting 3D structures from amino acid sequences has been proposed and has shown very high prediction accuracy. AlphaFold2 is also expected to have high throughput by using accelerators such as GPUs, but its computational performance in a massively parallel computing environment has not been fully discussed. This paper proposes optimization methods to perform inference with AlphaFold2 on the supercomputer Fugaku, the largest CPU-based supercomputer at present. For the multiple sequence alignment (MSA) and template search, a dynamic resource assignment method is proposed and 8.5-fold throughput is achieved for processing 10,000 input sequences. For the computation of the network, a tensor rearrangement for efficient batch matrix multiplication and a memory-efficient attention modules for CPU system are implemented, achieving a 1.3-fold speedup. 6.3-fold throughput is achieved for the entire inference pipeline, demonstrating that Fugaku is a practical CPU system for performing large-scale protein structure inference.
Postulate satisfaction for inconsistency measures in monotonic logics and databases

AbstractInconsistency measures for propositional logic have been investigated in great detail for the past 20 years. Many such measures have been proposed. Rationality postulates, conditions about inconsistency measures, were defined to distinguish the measures that satisfy intuitively desirable properties. The satisfaction or violation of various postulates is known for many inconsistency measures. But propositional logic has many limitations and in many applications more complex logics are appropriate. We consider monotonic extensions of propositional logic such as modal, temporal, description, and first-order logic. We focus on a class of inconsistency measures whose computation depends only on the structure of the minimal inconsistent subsets. The definition of these inconsistency measures is the same for these logics as for propositional logic. The situation is similar for the rationality postulates. This paper studies the connection between the satisfaction or violation of the postulates for propositional logic and monotonic extensions of propositional logic. We show that the results are the same for the most prominent postulates. Additionally, we consider the same question for databases. We show that although the setting is substantially different from these logics, in general the results are the same.Keywords: Inconsistency measurespostulate satisfactionmonotonic logicsdatabases AcknowledgmentsI wish to thank the reviewer for many helpful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).
Improving Graph Neural Networks by combining active learning with self-training

Abstract In this paper, we propose a novel framework, called STAL, which makes use of unlabeled graph data, through a combination of Active Learning and Self-Training, in order to improve node labeling by Graph Neural Networks (GNNs). GNNs have been shown to perform well on many tasks, when sufficient labeled data are available. Such data, however, is often scarce, leading to the need for methods that leverage unlabeled data that are abundant. Active Learning and Self-training are two common approaches towards this goal and we investigate here their combination, in the context of GNN training. Specifically, we propose a new framework that first uses active learning to select highly uncertain unlabeled nodes to be labeled and be included in the training set. In each iteration of active labeling, the proposed method expands also the label set through self-training. In particular, highly certain pseudo-labels are obtained and added automatically to the training set. This process is repeated, leading to good classifiers, with a limited amount of labeled data. Our experimental results on various datasets confirm the efficiency of the proposed approach.
The Feasibility Study of Moodle-Based E-Learning to Analyse the Scientific Literacy of Elementary School Students

scientific literacy in science learning. Building generations with strong scientific attitudes and understanding that can successfully communicate knowledge and research findings to the broader public is closely tied to scientific literacy. Students will be able to pursue higher education while still residing in a contemporary culture that is presently greatly influenced by advances in science and technology. This study's objective is to determine whether e-learning for primary school pupils using the moodle platform is feasible. The Borg &amp; Gall Research &amp; Development (R&amp;D) model's eight development stages are utilized in this study. Using instrument validation sheets customized for each expert, the resulting product was then validated by media, content, and learning experts. The information about the viability of moodle-based e-learning in the form of notes from experts and the percentage of e-learning validity. Descriptive analysis was used to examine the viability of moodle-based online learning. The outcome of this development research is a moodle-based e-learning program designed for fifth-grade elementary school students that has received very favorable validity ratings from three experts: a media expert (93%), a material expert (97,14%), and a learning expert (94,54%). These findings demonstrate that the generated e-learning meets the extremely practicable standards and can be used with some minor material adjustments. Keywords: E-Learning, Feasibility, Moodle, Scientific Literacy
Variational Pruning of Medial Axes of Planar Shapes

Abstract Medial axis (MA) is a classical shape descriptor in graphics and vision. The practical utility of MA, however, is hampered by its sensitivity to boundary noise. To prune unwanted branches from MA, many definitions of significance measures over MA have been proposed. However, pruning MA using these measures often comes at the cost of shrinking desirable MA branches and losing shape features at fine scales. We propose a novel significance measure that addresses these shortcomings. Our measure is derived from a variational pruning process, where the goal is to find a connected subset of MA that includes as many points that are as parallel to the shape boundary as possible. We formulate our measure both in the continuous and discrete settings, and present an efficient algorithm on a discrete MA. We demonstrate on many examples that our measure is not only resistant to boundary noise but also excels over existing measures in preventing MA shrinking and recovering features across scales.
Blockchain enabled task offloading based on edge cooperation in the digital twin vehicular edge network

Abstract The rapid development of the Internet of Vehicles (IoV) along with the emergence of intelligent applications have put forward higher requirements for massive task offloading. Even though Mobile Edge Computing (MEC) can diminish network transmission delay and ease network congestion, the constrained heterogeneous resources of a single edge server and the highly dynamic topology of vehicular edge networks may compromise the efficiency of task offloading, including latency and energy consumption. Vehicular edge networks are also vulnerable to malicious outside attacks. In this paper, we propose a new blockchain-enabled digital twin vehicular edge network (DTVEN) where digital twin (DT) is exploited to monitor network communication, computation, and caching (3C) resources management in real time to provide rich data for offloading decision-making, and blockchain is utilized to secure fair and decentralized offloading transactions among DTs. To ensure 3C resources sharing across edge servers, we design a DT-assisted edge cooperation scheme, which makes full use of edge resources in vehicular networks. Furthermore, a DT-based smart contract is built to achieve a quick and effective consensus process. Then, we apply a task offloading algorithm based on an improved cuckoo algorithm (ICA) and a resource allocation scheme based on greedy strategy to minimize network cost by comprehensively taking into account latency and energy consumption. Numerical results demonstrate that our proposed scheme outperforms the existing schemes in terms of network cost.
SDAUNet: A simple dual attention mechanism UNet for mixed noise removal

Abstract Convolutional neural networks (CNNs) have demonstrated impressive results in additive white Gaussian noise removal due to their strong fitting ability. However, their performance in mixed noise removal remains unsatisfactory, primarily due to their limited receptive field that focuses only on the local features of images and disregards global information. To ameliorate this issue, recent state‐of‐the‐art approaches employ attention mechanism (AM) to capture the global information. However, most AM based methods still suffer from low computational efficiency. In this paper, a novel model named simple dual attention mechanism UNet (SDAUNet) for mixed noise removal is proposed. In SDAUNet, the UNet architecture is used to gradually acquire multi‐scale image features and provide a more comprehensive and accurate representation of the image features than other CNNs. A simple dual attention convolutional block is presented to acquire the global image features that can successfully capture image details with a low burden. The experimental results demonstrate that the SDAUNet model can achieve better measurement metrics and visual performance than other state‐of‐the‐art methods.
Factored Neural Representation for Scene Understanding

Abstract A long‐standing goal in scene understanding is to obtain interpretable and editable representations that can be directly constructed from a raw monocular RGB‐D video, without requiring specialized hardware setup or priors. The problem is significantly more challenging in the presence of multiple moving and/or deforming objects. Traditional methods have approached the setup with a mix of simplifications, scene priors, pretrained templates, or known deformation models. The advent of neural representations, especially neural implicit representations and radiance fields, opens the possibility of end‐to‐end optimization to collectively capture geometry, appearance, and object motion. However, current approaches produce global scene encoding, assume multiview capture with limited or no motion in the scenes, and do not facilitate easy manipulation beyond novel view synthesis. In this work, we introduce a factored neural scene representation that can directly be learned from a monocular RGB‐D video to produce object‐level neural presentations with an explicit encoding of object movement ( e.g., rigid trajectory ) and/or deformations ( e.g., nonrigid movement ) . We evaluate ours against a set of neural approaches on both synthetic and real data to demonstrate that the representation is efficient, interpretable, and editable ( e.g., change object trajectory ). Code and data are available at : http://geometry.cs.ucl.ac.uk/projects/2023/factorednerf/ .
Properties of marginal sequential Monte Carlo methods

We provide a framework which admits a number of "marginal" sequential Monte Carlo (SMC) algorithms as particular cases — including the marginal particle filter (Klaas et al., 2005), the independent particle filter (Lin et al., 2005) and linear-cost Approximate Bayesian Computation SMC (Sisson et al., 2007). We provide conditions under which such algorithms obey laws of large numbers and central limit theorems and provide some further asymptotic characterizations. Finally, it is shown that the asymptotic variance of a class of estimators associated with certain marginal SMC algorithms is never greater than that of the estimators provided by a standard SMC algorithm using the same proposal distributions.
Planogram compliance control via object detection, sequence alignment, and focused iterative search

Smart retail stores are becoming the fact of our lives. Several computer vision and sensor based systems are working together to achieve such a complex and automated operation. Besides, the retail sector already has several open and challenging problems which can be solved with the help of pattern recognition and computer vision methods. One important problem to be tackled is the planogram compliance control. In this study, we propose a novel method to solve it. The proposed method is based on object detection, planogram compliance control, and focused and iterative search steps. The object detection step is formed by local feature extraction and implicit shape model formation. The planogram compliance control step is formed by sequence alignment via the modified Needleman-Wunsch algorithm. The focused and iterative search step aims to improve the performance of the object detection and planogram compliance control steps. We tested all these steps on two different datasets. The results show that our proposed method achieves a 0.992 F1 score in object detection and a 0.935 F1 score in planogram compliance control. We further analyzed the strengths and weaknesses of the proposed method from different perspectives. We finally summarized possible extensions to our work.
FPGA design and implementation for adaptive digital chaotic key generator

Abstract Background Information security is very important in today’s digital world, especially cybersecurity. The most common requirement in securing data in all services: confidentiality, digital signature, authentication, and data integrity is generating random keys. These random keys should be tested for randomness. Hardware security is more recommended than software. Hardware security has more speed and less exposure to many attacks than software security. Software security is vulnerable to attacks like buffer overflow attacks, side-channel attacks, and Meltdown–Spectre attacks. Results In this paper, we propose an FPGA Implementation for the adaptive digital chaotic generator. This algorithm is proposed and tested before. We introduce its implementation as hardware. This algorithm needs a random number seed as input. We propose two designs. The first one has an input random number. The second one has PRNG inside. The target FPGA is Xilinx Spartan 6 xc6slx9-2-cpg196. We used MATLAB HDL Coder for the design. We propose a configurable Key block’s length. For 32 bit the maximum frequency is 15.711 MHz versus 11.635 MHz for the first and second designs respectively. The area utilization of the Number of Slice Registers is 1% versus 2%. The number of Slice Look Up Tables is 40% versus 59%. number of bonded input output blocks is 64% versus 66%. otherwise are the same for the two designs. Conclusions In this paper, we propose an efficient and configurable FPGA Design for adaptive digital chaotic key generator. Our design has another advantage of storing the output keys internally and reading them later.
IMPLEMENTASI METODE ADASYN DALAM DETEKSI URL BERBAHAYA MENGGUNAKAN MACHINE LEARNING: DEMI MENINGKATKAN KEAMANAN SIBER DI ERA DIGITAL

Cybercriminals exploit malicious URLs as a distribution channel to spread harmful software across the internet. They take advantage of vulnerabilities in browsers to install malicious software with the aim of gaining remote access to the victims' computers. Typically, this malicious software aims to gain access to networks, steal sensitive information, and silently monitor targeted computer systems. In this research, a data mining approach known as Classification Based on Association (CBA) is employed to detect malicious URLs using both the URL itself and the features of the presented web pages. The CBA algorithm utilizes a training dataset of URLs as historical data to discover association rules that can be used to create an accurate classifier. By detecting dangerous URLs and malicious software, this contribution can assist organizations and individual users in enhancing the security of their computer systems and networks, thereby protecting sensitive data and reducing the risk of security incidents. The experimental results demonstrate that CBA achieves performance on par with tested classification algorithms, achieving an accuracy of 99% and low rates of false positives and false negatives. Future research could expand its focus to detect malicious URLs and software on mobile devices and embedded systems, as they have become significant targets for cybercriminals.