Reviewerly/exHarmony huggingface dataset:
Extreme learning machine for soil temperature prediction using only air temperature as input

Soil temperature (TS) is a crucial parameter in many fields, especially agriculture. In developing countries like Algeria, the soil temperatures (TS) and the meteorological data are limited. This study investigates the use of Extreme Learning Machine (ELM) for the accurate prediction of daily ST at three different depths (30 cm, 60 cm, and 100 cm) using a minimal number of climatic inputs. The inputs used in this study include maximum and minimum air temperatures, relative humidity, and day of the year (DOY) as a representative of the temporal component. Five different combinations of inputs were used to develop ELM models and determine the best set of input variables. The ELM models were then compared with traditional methods such as multiple linear regression, artificial neural networks, and adaptive neuro-fuzzy inference system. Based on evaluation metrics such as R, RMSE, and MAPE, the ELM models with air temperatures and DOY as inputs (ELM-M0 and ELM-M3) demonstrated superior performance at all depths when compared to the other techniques. The most accurate predictions were found at a depth of 100 cm using the ELM-M3 model, which employed inputs of minimum and maximum air temperatures and DOY, with R value of 0.98, RMSE of 0.68 °C, and MAPE of 3.4%. The results demonstrate that the inclusion of DOY in the climatic dataset significantly enhances the performance and accuracy of machine learning models for ST prediction. The ELM was found to be a fast, simple, effective, and useful tool for TS prediction.
Verifying the Verifier: eBPF Range Analysis Verification

Abstract This paper proposes an automated method to check the correctness of range analysis used in the Linux kernel ’s eBPF verifier. We provide the specification of soundness for range analysis performed by the eBPF verifier. We automatically generate verification conditions that encode the operation of the eBPF verifier directly from the Linux kernel ’s C source code and check it against our specification. When we discover instances where the eBPF verifier is unsound, we propose a method to generate an eBPF program that demonstrates the mismatch between the abstract and the concrete semantics. Our prototype automatically checks the soundness of 16 versions of the eBPF verifier in the Linux kernel versions ranging from 4.14 to 5.19. In this process, we have discovered new bugs in older versions and proved the soundness of range analysis in the latest version of the Linux kernel.
Certified Verification for Algebraic Abstraction

Abstract We present a certified algebraic abstraction technique for verifying bit-accurate non-linear integer computations. In algebraic abstraction, programs are lifted to polynomial equations in the abstract domain. Algebraic techniques are employed to analyze abstract polynomial programs; SMT QF_BV solvers are adopted for bit-accurate analysis of soundness conditions. We explain how to verify our abstraction algorithm and certify verification results. Our hybrid technique has verified non-linear computations in various security libraries such as Bitcoin and OpenSSL . We also report the certified verification of Number-Theoretic Transform programs from the post-quantum cryptosystem Kyber .
Symbolic Quantum Simulation with Quasimodo

Abstract The simulation of quantum circuits on classical computers is an important problem in quantum computing. Such simulation requires representations of distributions over very large sets of basis vectors, and recent work has used symbolic data-structures such as Binary Decision Diagrams (BDDs) for this purpose. In this tool paper, we present Quasimodo , an extensible, open-source Python library for symbolic simulation of quantum circuits. Quasimodo is specifically designed for easy extensibility to other backends. Quasimodo allows simulations of quantum circuits, checking properties of the outputs of quantum circuits, and debugging quantum circuits. It also allows the user to choose from among several symbolic data-structures—both unweighted and weighted BDDs, and a recent structure called Context-Free-Language Ordered Binary Decision Diagrams (CFLOBDDs)—and can be easily extended to support other symbolic data-structures.
Efficient Sensitivity Analysis for Parametric Robust Markov Chains

Abstract We provide a novel method for sensitivity analysis of parametric robust Markov chains. These models incorporate parameters and sets of probability distributions to alleviate the often unrealistic assumption that precise probabilities are available. We measure sensitivity in terms of partial derivatives with respect to the uncertain transition probabilities regarding measures such as the expected reward. As our main contribution, we present an efficient method to compute these partial derivatives. To scale our approach to models with thousands of parameters, we present an extension of this method that selects the subset of k parameters with the highest partial derivative. Our methods are based on linear programming and differentiating these programs around a given value for the parameters. The experiments show the applicability of our approach on models with over a million states and thousands of parameters. Moreover, we embed the results within an iterative learning scheme that profits from having access to a dedicated sensitivity analysis.
Few-shot person re-identification based on Feature Set Augmentation and Metric Fusion

Person re-identification identifies pedestrians by analyzing image information from surveillance videos. However, it faces challenges like occlusion, changing lighting, and costly annotation. Thus, it is often performed in a few-shot environment with limited images. In response to the problem of insufficient available pedestrian images in person re-identification, a few-shot person re-identification method based on feature set augmentation and metric fusion is proposed. In this work, firstly, a feature augmentation method is introduced into the feature embedding module. This method introduces multi-head self-attention in different feature extraction layers and spatial attention in feature fusion of different feature extraction layers, which can extract more diverse and discriminative pedestrian features. Secondly, a dual metric method combining Euclidean and cosine distance is proposed in the metric module to comprehensively measure the absolute spatial distance and directional difference of pedestrian features. In this way, the reliability of pedestrian similarity measurement is improved. Then, pedestrian feature similarity scores are obtained separately using the dual metric and relation metric methods. Finally, the combined metric score is obtained by weighted fusion, and the combined metric score is used to construct the joint loss to realize the overall optimization and training of the network. Experimental results on three small datasets, Market-mini, Duke-mini, and MSMT17-mini, show that the proposed method significantly improves recognition performance compared to other few-shot learning algorithms. Specifically, in scenarios 5-way 1-shot and 5-way 5-shot, the average recognition accuracies are 92.54% and 96.99%, 87.93% and 96.08%, and 71.68% and 84.51%, respectively.
Machine learning-based prediction of in-ICU mortality in pneumonia patients

Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models for mortality prediction in patients with severe pneumonia. This retrospective study evaluated patients admitted to the ICU for severe pneumonia between January 2016 and December 2021. The predictive performance was analyzed by comparing the area under the receiver operating characteristic curve (AU-ROC) of ML models to that of conventional severity-of-illness scoring systems. Three ML models were evaluated: (1) logistic regression with L2 regularization, (2) gradient-boosted decision tree (LightGBM), and (3) multilayer perceptron (MLP). Among the 816 pneumonia patients included, 223 (27.3%) patients died. All ML models significantly outperformed the Simplified Acute Physiology Score II (AU-ROC: 0.650 [0.584-0.716] vs 0.820 [0.771-0.869] for logistic regression vs 0.827 [0.777-0.876] for LightGBM 0.838 [0.791-0.884] for MLP; P < 0.001). In the analysis for NRI, the LightGBM and MLP models showed superior reclassification compared with the logistic regression model in predicting in-ICU mortality in all length of stay in the ICU subgroups; all age subgroups; all subgroups with any APACHE II score, PaO2/FiO2 ratio < 200; all subgroups with or without history of respiratory disease; with or without history of CVA or dementia; treatment with mechanical ventilation, and use of inotropic agents. In conclusion, the ML models have excellent performance in predicting in-ICU mortality in patients with severe pneumonia. Moreover, this study highlights the potential advantages of selecting individual ML models for predicting in-ICU mortality in different subgroups.
Quasi self-dual codes over non-unital rings from three-class association schemes

&lt;abstract&gt;&lt;p&gt;Let $ E $ and $ I $ denote the two non-unital rings of order 4 in the notation of (Fine, 93) defined by generators and relations as $ E = \langle a, b \mid 2a = 2b = 0, a^2 = a, b^2 = b, ab = a, ba = b\rangle $ and $ I = \langle a, b \mid 2a = 2b = 0, a^2 = b, ab = 0\rangle $. Recently, Alahmadi et al classified quasi self-dual (QSD) codes over the rings $ E $ and $ I $ for lengths up to 12 and 6, respectively. The codes had minimum distance at most 2 in the case of $ I $, and 4 in the case of $ E $. In this paper, we present two methods for constructing linear codes over these two rings using the adjacency matrices of three-class association schemes. We show that under certain conditions the constructions yield QSD or Type Ⅳ codes. Many codes with minimum distance exceeding 4 are presented. The form of the generator matrices of the codes with these constructions prompted some new results on free codes over $ E $ and $ I $.&lt;/p&gt;&lt;/abstract&gt;
Research on Chinese Text Classification Based on Improved RNN

The traditional RNN model uses the hidden state of the time step for information storage in dealing with the Chinese text classification problem, which causes the subsequent vocabulary to have a greater impact on the classification results, and the classification accuracy is thus affected. To improve the model structure, two improvement methods are proposed in this paper. First, the word vector embedding is performed and then the BiLSTM model is connected to extract the global text features in both directions. secondly, the Attention calculation module is added to the traditional structure for deep feature interaction, or the pooling module of CNN is connected to perform feature dimension reduction. The classification efficiency and accuracy of the model are improved. In the experiment, the classification accuracy rate on the Chinese news dataset reached a maximum of 91.08%, which was 1.73% higher than the highest value of the benchmark model, which effectively improved the performance of the model for Chinese text classification.
Code2Img: Tree-Based Image Transformation for Scalable Code Clone Detection

Code clone detection is an active research domain of software engineering. There are two core demands for clone detection: scalable detection and complicated clone detection. For scalable detection, existing approaches treat the source code as a text or token sequence and then calculate their similarity. However, the text-based and token-based approaches are difficult to detect complicated clone types due to the lack of consideration of code structure. The methods based on intermediate representations of code can effectively achieve complex clone types detection but are limited by the complexity of representations to be scalable. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Code2Img</i> , a tree-based code clone detector, which satisfies scalability while detecting complicated clones effectively. Given the source code, we first perform clone filtering by the inverted index to locate the suspected clones. For each suspected clone, we create the adjacency image based on the adjacency matrix of the normalized abstract syntax tree (AST). Then we design an image encoder to highlight the structural details further and refine pixels of the image. Specifically, we employ the Markov model to encode the adjacency image into a state probability image and remove its useless pixels. By this, the original complex tree can be transformed into a one-dimensional vector while preserving the structural feature of the AST. Finally, we detect clones by calculating the Jaccard Similarity of these vectors. We conduct comparative evaluations on effectiveness and scalability with eight other state-of-the-art clone detectors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SourcererCC</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NIL</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LVMapper</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nicad</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Siamese</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCAligner</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deckard</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Yang2018</i> ). The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Code2Img</i> achieves the best performance among all the comparative tools in terms of both detection effectiveness and scalability. It indicates that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Code2Img</i> can be applicable to scalable complicated clone detection.
Exploiting Synergies between Augmented Reality and RFIDs for Item Localization and Retrieval

Locating RFID-tagged items in the environment and guiding humans to retrieve the tagged items is an important problem in the RFID community. This paper explores how to exploit synergies between Augmented Reality (AR) headsets and RFID localization to help solve this problem by improving both user experience and localization accuracy. Using fundamental mathematical formulations for RFID localization, we derive confidence metrics and display guidance to the user to improve their experience and enable them to retrieve items faster. We build our primitives into an end - to-end system, RF - AR, and show that it achieves 8.6 cm median localization accuracy within 76 seconds and enables 55% faster retrieval than state-of-the-art past systems. Our results demonstrate that AR-based “human-in-the-loop” designs can make the localization task more accurate and efficient, and thus holds the potential to improve processes where items need to be retrieved quickly, such as in manufacturing, retail, and warehousing.
An Efficient Load Prediction-Driven Scheduling Strategy Model in Container Cloud

The rise of containerization has led to the development of container cloud technology, which offers container deployment and management services. However, scheduling a large number of containers efficiently remains a significant challenge for container cloud service platforms. Traditional load prediction methods and scheduling algorithms do not fully consider interdependencies between containers or fine-grained resource scheduling, leading to poor resource utilization and scheduling efficiency. To address these challenges, this paper proposes a new load prediction model CNN-BiGRU-Attention and a container scheduling strategy based on load prediction. The prediction model CNN and BiGRU focus on the local features of load data and long sequence dependencies, respectively, as well as introduce the attention mechanism to make the model more easily capture the features of long distance dependencies in the sequence. A container scheduling strategy based on load prediction is also designed, which first uses the load prediction model to predict the load state and then generates a scheduling strategy based on the load prediction value to determine the change of the number of container replicas in a fine-grained manner based on the load prediction value in the next time window, while the established domain-based container selection method is employed to facilitate the coarse-grained online migration of containers. Experiments conducted using public datasets and open-source simulation platforms demonstrate that the proposed approach achieves a 37.4% improvement in container load prediction accuracy and a 21.7% improvement in container scheduling efficiency compared to traditional methods. These results highlight the effectiveness of the proposed approach in addressing the challenges faced by container cloud service platforms.
Quantification of the stress field in extremely complex pores by digital photoelasticity

Quantification of stress fields in complex pores is essential for understanding the failure mechanisms of engineering geomaterials. Photoelasticity has been widely used to analyze stress fields in engineering. However, difficulties in model fabrication and stress field extraction limit its application for models with extremely complex structures, such as pores and fractures in rocks. In this study, the regional phase unwrapping algorithm was improved to efficiently determine the isoclinics by dividing the regions of the isoclinics in the interval (0, π/2). Furthermore, a photoelastic model with extremely complex pore structures in conformity with natural rocks was fabricated using a 3D printing technique. The isoclinics and isochromatics were calculated using the improved method. The good accuracy was verified by comparing the photoelastic fringe orders with the darkest and brightest fringes. The fabrication of complex photoelastic models and the efficient extraction of full-field stress will expand the application of photoelasticity in broader fields.
Interactive visual analytics of parallel training strategies for DNN models

Understanding and optimizing the parallel training strategies in the training of large-scale Deep Neural Network (DNN) models is crucial to enhance training efficiency. Existing works tried to demonstrate the layer-level information of the computational graph to support parallel training strategy selection. Whereas, the overall parallel execution logic is rarely considered by previous methods. In this paper, we proposed a novel visual analytics approach for parallel training strategies, demonstrating the execution logic of the distributed computing from up to bottom via explaining communication operators. Specifically, a computation-communication bipartite construction algorithm is designed for the computational graph visualization. Furthermore, a system is developed to help users easily access the proposed approach and explore the parallel training strategies interactively. With empirical evaluation through a quantitative user study and a qualitative expert interview, the practicality and superiority of the proposed approach is verified.
High-Performance and Low-Cost Approximation of ANN Sigmoid Activation Functions on FPGAs

Neural network implementations on FPGAs have received significant attention from the research community due to their superior performance in tasks such as computer vision and the need to perform them on edge devices. Most efforts have focused on architecture optimizations such as pruning and operations such as the multiplications and additions with less attention on the ANN activation function. In this paper a hardware implementation of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. A digital system architecture is designed with the purpose of reducing the delay of implementation and minimizing the LUTs in the FPGA. The designed architecture is described using Very High-Speed Integrated Circuits Hardware Description Language (VHDL). The proposed method utilizes a known piecewise linear approximation of the sigmoid function, and additionally exploits properties of fixed-point binary number representation to efficiently reduce delay and the amount of LUTS required Compared with previously published implementations of the same function and FPGA technology, the proposed method is shown to achieve a reduction between 33% and 95% in LUTs with no reduction in accuracy.
On the Applicability of Reinforced Learning in the Route Selection Task of an Unmanned Vehicle

This paper presents the process of describing and investigating methods for solving the optimal route search problem, choosing the means for developing a software module, including consideration of the required tools, description of the project structure, and features of the algorithm used. An introduction to the field of optimal route finding is given through the basic terms of graph theory, which is a leading framework for the field of reinforcement learning. The process of developing a learning environment is reviewed, starting with the selection of the environment and an explanation of its possible properties. Then the research of selected multi-agent learning algorithms is described in order to make a final comparison according to the most important criteria. Neural network architectures, hyperparameter learning tables and quality graphs of the learning models are plotted.
Safe and Effective Recommendation of Drug Combinations based on Matrix Co-Factorization

Nowadays the recommender systems represent an essential component in a huge variety of applications, and their continuous and rapid spread has allowed them to reach relevant fields like that of the healthcare. In such a delicate environment, it is extremely important to take into account all the variables that could occur, as the main objective is to support the doctors in prescribing accurate and safe treatments to patients affected by complex diseases. To address the aforementioned challenge, we decided to extend the Matrix Co-Factorization (MCF) method so that to allow it to include additional auxiliary data. A Post Hoc Re-Ranking technique has also been implemented to penalise the drugs that occur frequently in the adversarial drug-drug interactions knowdledge graph, therefore, to reduce the number of drugs in the final recommendations that could be dangerous for the patients' health. Different experiments have been used to assess the performances of our extended MCF. The results accomplished by our proposed method have also been compared against state-of-the-art models (such as C2PF, and PCRL) with a real-life dataset (MIMIC III). Our experiments depict that our method outperforms sufficiently the other methods in terms of accuracy and safety.