Reviewerly/exHarmony huggingface dataset:
Education in values: An analysis of teachers’ role in Mozambican military higher education

Now that the moral values preserved by the Mozambican society are contesting by insurgents and there are already allegations of collaboration between some military and insurgents (Habibe et al., 2019), the role of teachers in value education in military education is pertinent to examine. From this perspective, with a qualitative method approach and the involvement of 21 military personnel, the study analysed the role of military teachers in education values in Mozambican military higher education. Based on the analysis, it turned out that a teacher is one of the vectors that a military higher education uses to shape the new generations of officers. To this end, the teacher first adopts values about convictions, character, duty and tradition, preserved by the military institution. Then, it uses four modes of education to the values: active exposure class, supervision of the institution rules, exemplary demonstration, and exposure to professional experience; using these methods and the values they adopt, the teacher teaches students to discover and adjust to the military environment. But, factors related to the international situation, social, political and economic situations, and greed mean that some graduates in military higher education do not follow the values of Mozambican society.
Towards Multi-AUV Collaboration and Coordination: A Gesture-Based Multi-AUV Hierarchical Language and a Language Framework Comparison System

The underwater environment is a harmful environment, yet one of the richest and least exploited. For these reasons the idea of a robotic companion with the task of supporting and monitoring divers during their activities and operations has been proposed. However, the idea of a platoon of robots at the diver’s disposal has never been fully addressed in these proposals due to the high cost of implementation and the usability, weight and bulk of the robots. Nevertheless, recent advancements in swarm robotics, materials engineering, deep learning, and the decreasing cost of autonomous underwater vehicles (AUVs), have rendered this concept increasingly viable. Therefore, this paper introduces, in the first part, a novel framework that integrates a revised version of a gesture-based language for underwater human–robot interaction (Caddian) based on insights gained from extensive field trials. The newly introduced objective of this framework is to enable the cooperation and coordination of an AUV team by one or more human operators, while allowing a human operator to delegate a robot leader to instruct the other robotic team members. The work, in the second part, provides an evaluation of the new language proposed thanks to a fifty million sentence corpus and describes a comparison framework, which is used to estimate it with respect to other existing underwater human–robot interaction languages.
Function and status of structural geology in the Resource industry

Basic, nuts and bolts structural geology is the unheralded success story central to brownfields exploration that in Australia, and globally, has grown manyfold mineral deposits beyond the start-of-mine Reserve. Since the last (2012) meeting on applied structural geology and Resources, the topic has changed dramatically. Now, structural geology is practised daily and in house by most exploration and mining companies.KEY POINTSA diversity of settings, host rocks, control and geometry make it critical to document precisely the rocks in front of the geologist as distinct to what is in a preconceived conceptual model. It is detail that makes for new discoveries and deposit extensions.Three-dimensional computer visualisation helps with volumetric thinking, but there remains no computer alternative to the 3D cognitive geologist. Now essential structural data are integrated with other data, but the product is a hypothesis to be tested, not a silver bullet. Lineations matter and inform.The primary deliverable is images, be they hand-drawn or computer-generated showing the inter-relationships of structure, lithology, mineralisation and mine-site infrastructure/design. With their important embedded knowledge, the geologist, who took the readings, should also be entering, plotting data, synthesising, modelling and presenting to management and Directors.
Systematic review on blockchain research for sustainability accounting applying methodology coding and text mining

Blockchain and other Distributed Ledger Technologies (DLTs) are often considered major improvements in the capability of certain areas in sustainability accounting research. As such, it is argued that blockchains could be used for validation, data management, transactions settlements and objective measurement for corporate sustainability indicators, such as Greenhouse Gas (GHG) emissions during production processes. Following a Systematic Literature Review (SLR) methodology in data collection and database formation, the current work provides an analysis of the existing literature consisting of 59 contributions on blockchain applications in sustainability accounting. Several analysis steps containing bibliometric analyses were performed and presented, including novel coding of methodologies and industrial applications. Additionally, text mining analysis of the meta-data of contributions was conducted for the purpose of topic generation and retrieval of comparative insights. Results provide insights into the mapping of baseline technologies, regulatory environment, and practical factors outlined by previous contributions that influence the applications of blockchains for this cause. Future research areas include blockchains paired with other future-proof technologies, such as digital accounting tools (e.g. XBRL) or hardware IoT technologies in smart factories, to aid in the development of new pipelines of measurable and traceable sustainability information.
HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation

Data preparation is crucial in achieving optimized results for machine learning (ML). However, having a good data preparation pipeline is highly non-trivial for ML practitioners, which is not only domain-specific, but also dataset-specific. There are two common practices. Human-generated pipelines (HI-pipelines) typically use a wide range of any operations or libraries but are highly experience- and heuristic-based. In contrast, machine-generated pipelines (AI-pipelines), a.k.a. AutoML, often adopt a predefined set of sophisticated operations and are search-based and optimized. These two common practices are mutually complementary. In this paper, we study a new problem that, given an HI-pipeline and an AI-pipeline for the same ML task, can we combine them to get a new pipeline (HAI-pipeline) that is better than the provided HI-pipeline and AI-pipeline? We propose HAIPipe, a framework to address the problem, which adopts an enumeration-sampling strategy to carefully select the best performing combined pipeline. We also introduce a reinforcement learning (RL) based approach to search an optimized AI-pipeline. Extensive experiments using 1400+ real-world HI-pipelines (Jupyter notebooks from Kaggle) verify that HAIPipe can significantly outperform the approaches using either HI-pipelines or AI-pipelines alone.
Regularized Pairwise Relationship based Analytics for Structured Data

In line with the increasing machine learning model inference accuracy, deep learning (DL) models have been increasingly applied to structured data for a wide spectrum of real-world applications, including product recommendations, online advertisement, healthcare analytics and risk analysis. However, unlike unstructured data, structured data is high-dimensional and sparse and therefore engenders a large number of parameters in DL, making DL models more prone to overfitting. To alleviate the overfitting problem, various regularization methods have been designed to constrain the model parameters as a means to control the model complexity. Unfortunately, these methods are often restricted to regularizing the parameter values directly without considering the intrinsic correlations and dependencies between attribute fields of structured data which is however key to effective structured data modeling. In this paper, we re-examine DL for structured data from a new perspective of attribute interactions. In particular, we seek to explicitly model and regularize the pairwise relationships between attribute fields of structured data, in a field-adaptive manner, via a proposed attentive and interpretable framework called ATT-Reg. Specifically, in this framework, a set of attentive weight matrices are introduced to each attribute field for modeling obviously different relationships with its neighboring attribute fields. Further, we derive from the Bayesian viewpoint a novel Attentive Regularization method for imposing adaptive regularization strengths on different pairs of attribute fields, based on the informativeness of their relationship, which is calculated using both data-driven information and functional dependency (FD) knowledge. Such adaptive regularization facilitates each attribute field to learn discriminative and diversified representations for more effective predictive analytics. We also develop a feature attribution method for supporting more interpretable predictions We validate the effectiveness of our ATT-Reg on six real-world datasets. Extensive experimental results show that ATT-Reg achieves significant improvement over state-of-the-art graph models, attentive models as well as regularization methods and supports an excellent degree of interpretation.
T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, and possibly inaccurate results. In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based execution engine design to ensure high concurrency, bounded memory consumption, and effective load balancing. It also supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. Our experiments show that T-FSM is orders of magnitude faster than SOTA systems for frequent subgraph pattern mining. Our system code has been released at https://github.com/lyuheng/T-FSM.
Multi-sensor fusion 3D object detection for autonomous driving

Three-dimensional object detection is vital for understanding the autonomous vehicle driving environment. Different sensors are used for this purpose, such as cameras and LiDARs. Camera sensors are rich in color and texture information. However, cameras are unsuitable for 3D object detection due to the lack of depth information. Additionally, camera sensors are vulnerable to bad weather, such as snow, fog, and night driving. Autonomous driving needs a fast and accurate perception system for robust operation of the following pipeline, such as path planning and control. LiDAR is a commonly used sensor for 3D object detection because of its 3D information. However, its lack of color and texture information reduces the classification and detection performance. Therefore, there is no complete sensor that works for all situations. In this work, we propose a multi-modal fusion 3D object detection model for autonomous driving to use the best out of LiDAR and camera sensors. The model comprises a feature extraction network and a fusion network. Feature extraction networks transform the image and point cloud data into high-level features before fusion. Then, features from images and LiDAR data are fused. The experimental result on the nuScenes dataset shows the model’s competitive performance for 3D object detection.
Multiple Learning Features–Enhanced Knowledge Tracing Based on Learner–Resource Response Channels

Knowledge tracing is a crucial task that involves modeling learners’ knowledge levels and predicting their future learning performance. However, traditional deep knowledge tracing approaches often overlook the intrinsic relationships among learning features, treating them equally and failing to align with real learning scenarios. To address these issues, this paper proposes the multiple learning features, enhanced knowledge tracing (MLFKT) framework. Firstly, we construct learner–resource response (LRR) channels based on psychometric theory, establishing stronger intrinsic connections among learning features and overcoming the limitations of the item response theory. Secondly, we leverage stacked auto-encoders to extract low-dimensional embeddings for different LRR channels with denser representations. Thirdly, considering the varying impact of different LRR channels on learning performance, we introduce an attention mechanism to assign distinct weights to each channel. Finally, to address the challenges of memory retention and forgetting in the learning process and to handle long-term dependency issues, we employ a bidirectional long short-term memory network to model learners’ knowledge states, enabling accurate prediction of learning performance. Through extensive experiments on two real datasets, we demonstrate the effectiveness of our proposed MLFKT approach, which outperforms six traditional methods. The newly proposed method can enhance educational sustainability by improving the diagnosis of learners’ self-cognitive structures and by empowering teachers to intervene and personalize their teaching accordingly.
dMIL-Transformer: Multiple Instance Learning Via Integrating Morphological and Spatial Information for Lymph Node Metastasis Classification

Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and prognosis. However, it is very challenging to achieve satisfactory performance in LNM classification, because both the morphology and spatial distribution of tumor regions should be taken into account. To address this problem, this article proposes a two-stage dMIL-Transformer framework, which integrates both the morphological and spatial information of the tumor regions based on the theory of multiple instance learning (MIL). In the first stage, a double Max-Min MIL (dMIL) strategy is devised to select the suspected top-K positive instances from each input histopathology image, which contains tens of thousands of patches (primarily negative). The dMIL strategy enables a better decision boundary for selecting the critical instances compared with other methods. In the second stage, a Transformer-based MIL aggregator is designed to integrate all the morphological and spatial information of the selected instances from the first stage. The self-attention mechanism is further employed to characterize the correlation between different instances and learn the bag-level representation for predicting the LNM category. The proposed dMIL-Transformer can effectively deal with the thorny classification in LNM with great visualization and interpretability. We conduct various experiments over three LNM datasets, and achieve 1.79%-7.50% performance improvement compared with other state-of-the-art methods.
SiamBAG: Band Attention Grouping-Based Siamese Object Tracking Network for Hyperspectral Videos

A hyperspectral video contains frames with numerous spectral bands, providing fine reflectance information for object identification and tracking. Enriched features can be learned from spectral-spatial data using deep learning models. However, due to the difficulty in hyperspectral video collection, deep model training is often insufficient, causing reduced performance during the testing stage. To address this issue, we present a novel Band Attention Grouping-based Siamese framework (SiamBAG) for hyperspectral object tracking. SiamBAG employs massive color object tracking data to train a deep neural network. Band weights obtained by band attention module are used to group a hyperspectral image into multiple three-channel false-color images with approximate total group weights. Then multiple enhanced images obtained by histogram equalization are fed to the proposed SiamBAG network to generate a classification branch, a regression branch and a scale tuning branch. In the classification branch, the response maps of multiple groups are fused by regularized group weights to estimate the position of objects. Then the regression branch is used to obtain the initial object position of objects. The position offsets are fed back to the scale tune branch to relocate and fine-tune the object position by exploiting the similarity between template features and detection features. Experimental results demonstrate that the proposed tracker achieves superior tracking performance than other methods. The source codes of this paper will be released at https://github.com/zephyrhours/Hyperspectral-Object-Tracking-SiamBAG.
A survey of Kubernetes scheduling algorithms

Abstract As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research.
A Path Aggregation Network Based on Residual Feature Enhancement for Object Detection in Remote Sensing Imagery

The complex background and small scale of objects in remote sensing images (RSIs) lead to poor detection performance. To solve these problems, a new object detection method named the residual feature enhancement detection network (RFEDN) is proposed. First, to weaken the interference of irrelevant backgrounds, we introduce a common-and-differential attention network into the cross-stage partial darknet−53, which can refine features in both channel and spatial dimensions so that the network pays attention to meaningful features. After this, we design the residual feature enhancement-based path aggregation network (RFE-PANet), which can help alleviate the problem of information loss caused by channel reduction. We perform experiments on the dataset of object detection in aerial images (DOTA) series datasets and the HRSC2016 dataset. The experimental results reveal the superior performance of the proposed method.
Photonic Spiking Neural Networks with Highly Efficient Training Protocols for Ultrafast Neuromorphic Computing Systems

Photonic technologies offer great prospects for novel, ultrafast, energy-efficient, and hardware-friendly neuromorphic (brain-like) computing platforms. Moreover, neuromorphic photonic approaches based on ubiquitous, technology-mature, and low-cost vertical-cavity surface-emitting lasers (VCSELs) (devices found in fiber-optic transmitters, mobile phones, and automotive sensors) are of particular interest. Given that VCSELs have shown the ability to realize neuronal optical spiking responses (at ultrafast GHz rates), their use in spike-based information-processing systems has been proposed. In this study, spiking neural network (SNN) operation, based on a hardware-friendly photonic system of just one VCSEL, is reported alongside a novel binary weight “significance” training scheme that fully capitalizes on the discrete nature of the optical spikes used by the SNN to process input information. The VCSEL-based photonic SNN was tested with a highly complex multivariate classification task (MADELON) before its performance was compared using a traditional least-squares training method and an alternative novel binary weighting scheme. Excellent classification accuracies of &gt;94% were achieved by both training methods, exceeding the benchmark performance of the dataset in a fraction of the processing time. The newly reported training scheme also dramatically reduces the training set size requirements and the number of trained nodes (≤1% of the total network node count). This VCSEL-based photonic SNN, in combination with the reported “significance” weighting scheme, therefore grants ultrafast spike-based optical processing highly reduced training requirements and hardware complexity for potential application in future neuromorphic systems and artificial intelligence applications.
Relaxed Stabilization of Event-Triggered Interval Type-2 T–S Fuzzy Positive Systems With Stochastic Actuator Failure

This article studies the membership-function-dependent (MFD) stability and positivity analysis of positive interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy fault-tolerant control systems under the event-triggered control framework. Considering the fact that parameter uncertainties may exist in nonlinear plants, the IT2 T–S fuzzy model as an effective mathematical tool is employed to approximately express a complex positive system with nonlinearities and uncertainties. In order to save communication resources and improve the security of system operation, an event-triggered fault-tolerant (ETFT) control strategy is then designed based on the imperfectly premise matching concept. In addition, for capturing positivity characteristic and matching with the linear copositive Lyapunov function, a novel event-triggered condition in the 1-norm rather than in the 2-norm is developed to control the positive IT2 T–S fuzzy systems. Thereinto, the nonconvex problem as the first obstacle is required to be cleared since feasible solutions usually cannot be obtained based on the nonconvex conditions. For dealing with this problem, a valid approach is presented to conduct convexification with the help of a user-chosen constant vector. Besides, because of event-triggered action, the inherent mismatched issue of the premise variables of the fuzzy model and the ones of the fuzzy controller is encountered. Fortunately, the advanced MFD analysis, which is capable of bringing the information contained in the membership functions into the stability and positivity conditions, provides an effective strategy to remove this barrier. Consequently, relaxed results that can accomplish the stability and positivity for wider positive systems are developed and displayed in sum-of-squares form. Detailed simulation results are given to clarify the effectiveness of the designed ETFT control method.