Reviewerly/exHarmony huggingface dataset:
Autonomous Navigation of <i>AGV</i>s in Unknown Cluttered Environments: <i>Log-MPPI</i> Control Strategy

Sampling-based model predictive control ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MPC</i> ) optimization methods, such as Model Predictive Path Integral ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MPPI</i> ), have recently shown promising results in various robotic tasks. However, it might produce an infeasible trajectory when the distributions of all sampled trajectories are concentrated within high-cost even infeasible regions. In this study, we propose a new method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log-MPPI</i> equipped with a more effective trajectory sampling distribution policy which significantly improves the trajectory feasibility in terms of satisfying system constraints. The key point is to draw the trajectory samples from the normal log-normal ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NLN</i> ) mixture distribution, rather than from Gaussian distribution. Furthermore, this work presents a method for collision-free navigation in unknown cluttered environments by incorporating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2D</i> occupancy grid map into the optimization problem of the sampling-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MPC</i> algorithm. We first validate the efficiency and robustness of our proposed control strategy through extensive simulations of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2D</i> autonomous navigation in different types of cluttered environments as well as the cartpole swing-up task. We further demonstrate, through real-world experiments, the applicability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log-MPPI</i> for performing a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2D</i> grid-based collision-free navigation in an unknown cluttered environment, showing its superiority to be utilized with the local costmap without adding additional complexity to the optimization problem. A video demonstrating the real-world and simulation results is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/_uGWQEFJSN0</uri> .
Smart-contract enabled decentralized knowledge fusion for blockchain-based conversation system

Knowledge fusion can be used for global knowledge sharing and maintenance, and it has proven to benefit the performance of conversation systems. In the process of knowledge fusion in conversation systems, knowledge and contributions from multiple experts are easily tampered with, which results in biases in fusion results and their incentives. Blockchains promise tamper-proof, secure, and transparent systems. This study explores the feasibility and effectiveness of knowledge fusion based on blockchain by adopting a smart contract. First, a blockchain-based framework for decentralised knowledge fusion for a conversation system is presented. Second, a concrete scheme of blockchain-based decentralised knowledge fusion is designed and implemented with smart contracts to ensure security execution and fairness. Finally, a use case-based evaluation to prove the effectiveness of the proposed decentralised knowledge fusion method is presented.
An Empirical Study on Bugs in Python Interpreters

Python is an interpreted programming language that has been widely used in many fields. The successful execution of a Python program depends on both the correctness of Python program and the correctness of Python interpreter. As an infrastructure software, there are many bugs in the Python interpreter. Exploring the bugs in Python interpreters can help developers and maintainers of Python interpreters detect and fix bugs and help users of Python avoid risks. In this article, we conduct an empirical study on the bugs in two mainstream Python interpreters: CPython and PyPy. By analyzing 25 958 fixed bugs, 18 824 revisions, 2 116 test cases, and root causes of randomly sampled 510 bugs, we have summarized the following findings. <list list-type="ordered" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><list-item><label>1)</label> The distribution of bugs in the Python interpreter is so uneven that the vast majority of bugs are distributed in a few components and source files. </list-item> <list-item><label>2)</label> The scales of the testing programs that reveal bugs are small. </list-item> <list-item><label>3)</label> The fixing works seem to be not complicated since the number of modified source files and lines of code are limited; however, most bugs need a long time to be fixed; nearly 15% of the bugs need more than one year to fix. </list-item> <list-item><label>4)</label> The priorities of bugs are independent of their locations, but they significantly correlate with duration of bugs. </list-item> <list-item><label>5)</label> Semantic bugs are the most frequent root causes of bugs, and their proportion exceeds other types of root causes. </list-item> </list> These results could indicate some potential problems during the detecting and fixing of Python interpreter’s bugs, and provide some assistance to developers and maintainers of Python interpreters, users of Python, as well as researchers in related fields.
A Spectral-Spatial Feature Extraction Method With Polydirectional CNN for Multispectral Image Compression

Convolutional neural networks (CNN) has been widely used in the research of multispectral image compression, but they still face the challenge of extracting spectral feature effectively while preserving spatial feature with integrity. In this article, a novel spectral-spatial feature extraction method is proposed with polydirectional CNN (SSPC) for multispectral image compression. First, the feature extraction network is divided into three parallel modules. The spectral module is employed to obtain spectral features along the spectral direction independently, and simultaneously, with two spatial modules extracting spatial features along two different spatial directions. Then all the features are fused together, followed by downsampling to reduce the size of the feature maps. To control the tradeoff between the rate loss and the distortion, the rate-distortion optimizer is added to the network. In addition, quantization and entropy encoding are applied in turn, converting the data into bit stream. The decoder is structurally symmetric to the encoder, which is convenient for structuring the framework to recover the image. For comparison, SSPC is tested along with JPEG2000 and three-dimensional (3-D) SPIHT on the multispectral datasets of Landsat-8 and WorldView-3 satellites. Besides, to further validate the effectiveness of polydirectional CNN, SSPC is also compared with a normal CNN-based algorithm. The experimental results show that SSPC outperforms other methods at the same bit rates, which demonstrates the validity of the spectral-spatial feature extraction method with polydirectional CNN.
Evolutionary Construction of Stories that Combine Several Plot Lines

Although the narrative structure of common entertainment products like Hollywood movies or TV series is generally composed of a number of different plot lines combined into a single narrative discourse, efforts on computational modeling of story generation have to this point focused mostly on the construction of stories with a single plot line. The present paper explores an evolutionary solution to the task of building a story that combines more than one plot line into a single linear discourse. This requires: a set of knowledge resources that capture the main features that influence the decisions involved, a representation suitable for evolutionary treatment for discourses with several plot lines, and a set of fitness functions based on metrics related to the quality of the resulting discourses. The proposed solution produces populations of stories with elaborate discourses that combine several subplots.
Hybrid Grouping Genetic Algorithm for Large-Scale Two-Level Resource Allocation of Containers in the Cloud

Cloud container resource allocation aims to find container placements in cloud Virtual Machines (VM) and Physical Machines (PM) such that overall energy consumption is minimised. A resource allocation architecture where application containers are consolidated into cloud VMs in a container-VM-PM model is common practise in data centers. The VM layer may provide additional administrative or security features, but adds complexity to the optimization problem when deploying containers initially on a large scale. Research addressing this two-level resource allocation is limited, some of the recent work try to optimise consolidation of containers to VM layer separately from consolidation of VMs to PMs, which results in large portions of the search space remaining unexplored. A Grouping Genetic Algorithm (GGA) framework that can simultaneously optimize consolidation on both levels is promising. However, for large instances of the two-level optimisation, it may suffer from premature convergence and limited population diversity. In this work, we propose a new fixed-length crossover operator that is designed to improve population diversity and exploration in GGA for container resource allocation optimisation. We also propose problem-specific Best-Fit and Largest VM heuristic operators to aid local search by rearranging containers from the lower fitness PMs at the chromosome tail into existing VMs and PMs with better utilization when possible. We demonstrate that with the newly developed operators, the proposed GGA can significantly reduce energy consumption in large-scale test cases.
Embedding Smart Contract in Blockchain Transactions to Improve Flexibility for the IoT

In recent years, the blockchain technology has been widely used in the Internet of Things (IoT). One of the major concerns is how to adopt a smart contract to process data from IoT devices flexibly. While plenty of smart contract-based methods can be used, smart contracts are required to be deployed previously. This requires an additional step (to deploy a smart contract) and makes a smart contract separate from its data (transactions to trigger its interface), which generates limitations in IoT scenarios. In this article, we developed an approach to embed the smart contract and its data into the same transaction, eliminating the need for a predeployment step. Data is employed as parameters to invoke the interface of a smart contract, and the smart contract is used to process the data inside the transaction. With this method, a smart contract does not need other transactions from the user. Results indicate that the proposed method can eliminate the requirement of a separately deployed smart contract, saving costs, and waiting time for the predeployment.
From past to present

Artificial Intelligence (AI) is a buzz word, and anything associated with it catches attention today. An expert system is a branch of Artificial Intelligence which is impacting a lot of fields today, more popularly transport industry (automated driving). Although transport industry has made AI more popular to a common man today, it has been applied to many fields in the past and continues to be major component today.
Knowing What to Say: Towards knowledge grounded code-mixed response generation for open-domain conversations

Inculcating knowledge in the dialogue agents is an important step towards creating any agent more human-like. Hence, the use of knowledge while conversing is crucial for building interactive and engaging systems. Most existing works for developing social conversation systems focus on monolingual discussions, with little research on multilingual or code-mixed conversations. Therefore, in this work, we propose generating knowledge-aware code-mixed responses for building end-to-end code-mixed dialogue systems. We design a reinforced transformer framework that uses task-specific rewards for training the entire system. In addition, we utilize a knowledge selection module that captures the appropriate knowledge and generates responses using a deliberation decoder. We introduce a Knowledge aware Code-Mixed (KCM) dataset that consists of conversations grounded in knowledge for four Indian languages (Hindi, Bengali, Gujarati, and Telugu) and two European languages (Spanish and French). Quantitative and qualitative analysis show that the proposed framework on the newly created KCM dataset performs superior to the existing baselines for all the metrics.
A novel hierarchical feature selection method based on large margin nearest neighbor learning

The complexity of data has been increasing not only in the dimension of the data, but also in the interrelated class relationships. Pre-defined hierarchical structure of classes helps to model such relationships in a way, which divides the whole classification problem into a set of subtasks. Feature selection, known as a data preprocessing technique, aims to selecting useful feature to improve the performance of learning algorithms. However, the issue of close class relationships, especially under the same parent node in the hierarchical structure, is adverse to the feature selection. In this paper, we introduce a novel distance metric based feature selection method with large margin nearest neighbor strategy for the hierarchical classification problems. Different from other algorithms, our proposed method is to project the data into a low-dimensional feature space for learning correct metrics, and the weights of features will be indicated in the transformation matrix. A sparsity regularization and iterative optimization ensure that superior feature subsets can be selected. Experimental evaluation on several datasets with hierarchical class structure demonstrates that the proposed approach is comparable or better than some other state-of-the-art hierarchical feature selection methods.
Illumination-aware image fusion for around-the-clock human detection in adverse environments from Unmanned Aerial Vehicle

This study proposes a novel illumination-aware image fusion technique and a Convolutional Neural Network (CNN) called BlendNet to significantly enhance the robustness and real-time performance of small human objects detection from Unmanned Aerial Vehicles (UAVs) in harsh and adverse operation environments. The proposed solution is particular useful for mission-critical public safety applications such as search and rescue operations in rural areas. The operation environments of such missions are featured with poor illumination condition and complex background such as dense vegetation and undergrowth in diverse weather conditions, and the missions have to address the challenges of detecting humans from UAVs at high altitudes, with a moving platform and from various viewing angles. To overcome these challenges, the proposed solution register and fuse the images using Enhanced Correlation Coefficient (ECC) and arithmetic image addition with customised weights techniques. The result of this fusion is fuelled with our new BlendNet AI model achieving 95.01 % of accuracy with 42.2 Frames Per Second (FPS) on Titan X GPU with input size of 608 pixels. The effectiveness of the proposed fusion method has been evaluated and compared with other methods using the KAIST public dataset. The experimental results show competitive performance of BlendNet in terms of both visual quality as well as quantitative assessment of high detection accuracy at high speed.
Automatic Rule Generation for Decision-Making in Context-Aware Systems Using Machine Learning

With the increasing interest devoted to dynamic environments, a crucial aspect is revealed in context-aware systems to deal with the rapid changes occurring in users' surrounding environments at runtime. However, most context-aware systems with predefined context-aware rules may not support effective decision-making in dynamic environments. These context-aware rules, which take into account different context information to reach an appropriate decision, could lose their efficiency at runtime. Therefore, a growing need is emerging to address the decision-making issue leveraged by dynamic environments. To tackle this issue, we present an approach that relies on improving decision-making in the wake of dynamic environments through automatically enriching a rule knowledge base with new context-aware rules discovered at runtime. The major features of the presented approach are as follows: (i) a hybridization of two machine learning algorithms for rule generation, (ii) an extended genetic algorithm (GA) for rule optimization, and (iii) a rule transformation for the knowledge base enrichment in an automated manner. Furthermore, extensive experiments on different datasets are performed to assess the effectiveness of the presented approach. The obtained experimental results depict that this approach exhibits better effectiveness compared to some algorithms and state-of-the-art works.
Handling Internet Activism During the Russian Invasion of Ukraine: A Campus Network Perspective

The Russian invasion of Ukraine in 2022 raised an enormous wave of Internet activism and distributed denial-of-service (DDoS) attacks launched with the help of common users across the world. In this article, we describe the events of the first days after the invasion from the perspective of the cybersecurity incident response team of Masaryk University in the Czech Republic. We observed hundreds of users intentionally participating in DDoS attacks against Russia from the university’s network. The campus network faced only minor issues in terms of service unavailability, but alerts flooded the cybersecurity team. Two dimensions of the events are highlighted. First, the large-scale attacks in an unexpected direction were highly unusual and brought technical challenges in network monitoring and intrusion detection. Second, hacktivism still violates the campus network’s terms of use and requires the cybersecurity team to communicate the issues very carefully with the community.
An Improved Neural Network Model Based on Inception-v3 for Oracle Bone Inscription Character Recognition

Oracle bone inscription is the ancestor of modern Chinese characters. Character recognition is an essential part of the research of oracle bone inscription. In this paper, we propose an improved neural network model based on Inception-v3 for oracle bone inscription character recognition. We replace the original convolution block and add the Contextual Transformer block and the Convolutional Block Attention Module. We conduct character recognition experiments with the improved model on two oracle bone inscription character image datasets, HWOBC and OBC306, and the results indicate that the improved model can still achieve excellent results in the cases of blurred, occluded, and mutilated characters. We also select AlexNet, VGG-19, and Inception-v3 neural network models for the same experiments, and the comparison result shows that the proposed model outperforms other models in three evaluation indicators, namely, Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, which indicate the correctness and excellence of our proposed model.
Measuring cyber secure behavior of elementary and high school students in the Netherlands

School systems may pay attention to the fact that individuals and companies using smart devices are increasingly at risk of becoming victims of cybercrime. The literature on how effective students in developed countries such as the Netherlands are taught about cyber security skills during their school career is scarce. Although curriculum materials are available, scaling up computer science education is behind. Therefore, this study explores to what extent Dutch students develop cyber secure behavior at elementary and high school. A questionnaire was used for self-assessment of cyber security behavior. After the questionnaire was completed, two group interviews were conducted to improve the interpretation of the questionnaire results. The study findings revealed that the Dutch school curriculum hardly pays attention to this topic and that students acquire their online behavior mainly through experience, instructions on the internet, through parents, and through siblings. In addition, many students developed more reckless behavior over time. We recommend that cyber security education should start at elementary school as soon as children begin to use online equipment. A subject that deserves special attention is recognizing phishing emails and phishing websites. The learners should be convinced that risky behavior on the internet may turn against them and against the organization to which they belong.
Local spatial alignment network for few-shot learning

Few-Shot Learning (FSL) aims at recognizing the novel classes with extremely limited samples via transferring the learned knowledge from some base classes. Most of the existing metric-based approaches focus on measuring the instance-level feature similarity but neglect the spatial alignment between different instances, which would lead to poor adaptation with inaccurate local spatial matching, especially for he fine-grained classes. In this paper, we propose a model called Local Spatial Alignment Network (LSANet) to measure the instance-to-class similarity via aligning the local spatial regions in a traversal scanning way. Specifically, the local spatial alignment is achieved by continuously sampling the local patches from the query feature map, where each local patch performs as a kernel to filter the most similar local patches from the support feature maps, obtaining the patch-level similarities between the query instance and the support classes. Then, we propose an information aggregation module to aggregate the patch-level similarities into the class prediction score, where the important patches are highlighted and the backgrounds are diluted. In this way, our model is able to both align the local spatial patches and capture the discriminative information, which benefits in adapting for the novel few-shot classes. To evaluate the effectiveness of the proposed model, we conduct extensive experiments on both coarse-grained and fine-grained datasets. The experimental results show that the proposed LSANet performs competitively on the FSL benchmarks of different granularities.
A novel <scp>DeepML</scp> framework for multi‐classification of breast cancer based on transfer learning

Abstract In the automated diagnosis of breast cancer (BC), microscopic images based on multi‐classification play a prominent role. Multi‐classification of BC means to differentiate among the sub‐categories of BC (papillary carcinoma, ductal carcinoma, fibroadenoma, etc.). However, unpretentious contrasts in various sub‐categories of BC occur due to the wide fluctuation of 1) excessive coherency of malignant cells, 2) high definition image appearance, and 3) excessive heterogeneity in color distribution, which makes the task more crucial. Therefore, the automated sub‐category discrimination using microscopic images has great medical diagnostic significance yet has not much explored. Thus, the present paper proposes a framework based on machine learning (ML) and deep learning (DL) to multi‐classify BC cells into 8 sub‐categories. These 8 sub‐categories comprise four kinds that delineate benigncy, and the other four portray malignancy. More appropriately, both the ML and DL models with the concept of transfer learning have been proposed as DeepML framework to achieve multi‐classification of BC using histopathological images. The DeepML framework has achieved distinguished performance (approx. 98% &amp; 89% average accuracy for 90–10% and 80–20% train‐test split, respectively) on a wide scale dataset, which intimate the quality of the proposed framework among existing approaches.