Reviewerly/exHarmony huggingface dataset:
Preface

In this special issue of GEUS Bulletin, the many riddles regarding the platinum group elements and gold (PGE-Au) mineralisation of the East Greenland Skaergaard intrusion are untangled and discussed. The Skaergard PGE-Au mineralisation, as defined in this study, embodies an enigmatic and rich ore-formation that arguably could have been an economic resource, had it not been for its ice-locked position in central East Greenland. The authors of this study (Rudashevsky et al. 2023, this volume) characterise the systematic variability in the precious metal mineralogy from the contact towards the interior of the intrusion based on the analysis of more than 4000 individual PGE-Au grains. This variability is interpreted in the light of 90 years of research and over 1000 publications pertaining to magma chamber processes in the Skaergaard intrusion. With such an impressive library of knowledge, on a comparatively simple magmatic system such as the Skaergard intrusion, we should have discovered a few islands of truth in igneous petrology and ore-deposit formation. And indeed, we have. But we are also enriched with an evolving story, where answering one question only serves to raise three new questions. This study demonstrates the variability of PGE-Au phases throughout the ore-forming zone of the Skaergaard intrusion. As previously observed, PGE-Au mineralisation in the central parts is divided into several layers over 30–40 m of the cumulus stratigraphy with increasing Pd/Pt ratios upwards, an Au-rich upper part and a low sulphide content throughout all layers. Close to the contact the precious metal zonation is less pronounced, and it is significantly more sulphide rich. The PGE mineralogy deviates significantly from the centre to the margin. These complex lateral and vertical variations cannot be explained by one genetic model but require an intricate combination of igneous processes including silicate-melt liquid immiscibility, sulphide-melt immiscibility, sulphide-melt resorptions, precious metal transport by volatile-rich fluids and, finally, the solidification rate of the cumulus mushes. For other well-preserved PGE-Au deposits throughout the world, we observe a great variation of ore-forming models. Remarkably, most of these models may be applied to various parts of the Skaergaard mineralisation. The authors suggest that the Skaergaard intrusion preserves different steps in PGE-Au ore-genesis which, in many other intrusions are obliterated by later igneous events. Therefore, the legacy of the Skaergaard mineralisation is the preservation of igneous ore-forming events that may also precede the genesis of other PGE-Au deposits in the world. After the last conclusion, I guarantee that you will be confused and perhaps a bit triggered but hopefully also inspired and bursting with new questions on the genesis of PGE-Au deposits in mafic and ultramafic igneous complexes. In light of the recent study, you may even be encouraged to look at your favourite PGE-Au deposit with fresh eyes. This study beautifully demonstrates that turning the next page in the book of magma chamber processes is more important than seeing ‘The End’.
Jointly Learning Time-Structure-History Graph Embedding for Temporal Knowledge Graph Reasoning

The Temporal Knowledge Graph(TKG) reasoning is an imperative task for various applications of TKG. However, reasoning over TKGs which aims to predict future facts has been less explored. Intuitively, entities and relations in TKG change differently over time, and the variations are different. It also can be observed that what happens in the past may affect the future. Apart from these, structure information that exists in the neighborhood of a given entity in a KG has been proven to be important. Inspired by these, a novel graph embedding method for TKG reasoning, called time-structure-history graph embedding (TSHGE), is proposed in this paper. TSHGE consists of three progressive learning units. The first time information learning unit employs a novel time-aware mechanism to encode the time influence on entities and relations. Then a relation-aware GAT (Graph Attention Network) is used in the structure information learning unit to model the 1-hop neighborhoods of all entities in a KG at each timestamp in TKG. Finally, the history information learning unit utilizes GRU (Gated Recurrent Unit) to learn history information from various facts. Then the ultimate embedding representations of entities and relations can be obtained for the prediction of future facts. The evaluations on three public TKG datasets have demonstrated the effectiveness and superiority of TSHGE for predicting future facts.
Secure Decentralized Learning with Incentives on UTXO-based Blockchains

Increasing computational and economic costs in training artificial intelligence motivate the development of alternative decentralized training paradigms. Federated learning achieves decentralization by creating the final model combining local models trained on edge devices. This process does not require sharing local private data with the central service. The blockchain can be used to distribute and collect edge models. The benefits of relying on the blockchain are a native way to provide economic incentives to the local devices and the ability to log events and track training steps. The former incentivizes honest behavior, while the latter enables auditing and testing of the model. We present a peer-to-peer cloud protocol to outsource computations using an unspent transaction output blockchain relying on proof of work. In designing this protocol, we face three main challenges: how to communicate the computation to the computing party, how to make its execution safe for them, and how to ensure the computing party executed it faithfully. Our solution introduces a verification script in the outsourced computation. The computing party receives the computation as an obfuscated executable and provides the verification output to show they faithfully executed it. Data integrity and safety in the execution rely on proof of existence.
Micro-Expression Recognition Based on MAML Meta-Learning Algorithm

Micro-expression, an accurate and effective behavioral clue, can reveal true changes in people’s emotions. Nowadays, micro-expression recognition is widely used in education, interrogation, clinical diagnosis, etc. However, previous research on micro-expression recognition focused on deep-learning methods that depend on large amounts of training samples. Recently, meta-learning mainly improves performance by extracting meta-knowledge from a set of tasks and has been widely used in few-shot classification fields. In this paper, we propose a micro-expression recognition method based on Model-Agnostic meta-learning algorithm. This method meta-learned the initialization parameters of a three-dimensional convolutional neural network (3DCNN) to achieve fast adaptation to new tasks. We conducted experiments on two typical micro-expression datasets: CASME II and SMIC-HS. The results showed that the recognition accuracy of our proposed method can reach 92.97%, which significantly outperforms the state-of-the-art algorithms. Furthermore, the recognition accuracy of our approach is independent of the number of training samples.
Learning from Imperfect Demonstrations via Reweighting Confidence

Most imitation learning algorithms assume that the demonstrations come from the optimal policy of the expert, but this assumption is limited to real-world problems, collecting expert data is difficult and expensive and usually collects imperfect demonstrations with varying optimality. Prior works use ranking or confidence to represent optimality information for imperfect demonstrations, which require human-annotated rankings or ground-truth confidence, and these limited settings limit the availability and scale of dataset. Confidence-Aware Imitation Learning (CAIL) proposes a learning framework that combines ranking and confidence but still requires some manually annotated data as a ranking basis. In this paper, we propose Reweight Confidence Adversarial Imitation Learning (RWCAIL), a method to learn the optimality information of imperfect demonstrations via automatically reweighting confidence, which can eliminate the need for manual data annotation by combining CAIL’s bi-level optimization model. We conduct experiments in the MuJoCo environment and show that our algorithm performs on par with previous work without requiring manual data annotations.
Panoramic Short Video Marketing: an accessible application of metaverse at the preliminary stage

Panorama is a kind of metaverse technology accessible nowadays. It can be played on ordinary devices such as smartphones, giving the audience an engaging and interactive visual experience. Combining it with short-video platforms would be a fantastic tool for marketing. Panoramic Short Video (PSV) especially can help companies to facilitate the performance of their official accounts, mitigating the problem that they rely too much on influencers. The workflow of producing PSV contains four steps and needs to follow several tips. PSV matches all four principles of the 4I marketing theory, so it is expected to attain success.
Path Planning for Autonomous Driving with Curvature-considered Quadratic Optimization

Path planning is a crucial module in motion planning for autonomous driving, aiming at generating kinematically feasible and collision-free paths. Furthermore, the smoothness of generated path is significant for passengers’ comfortable feelings. In this paper, we propose an improved quadratic programming approach that generates optimal paths in urban structure scenarios with the Frenét frame, taking the cost of the path curvature into consideration explicitly. The proposed second-order Taylor-expansion estimation of the path curvature with the lateral spatial parameters can reflect the actual change of path curvature. Various simulated scenarios verify the effectiveness of our proposed method and the improvement of path quality by adding the curvature objective in the optimization procedure. The source code is released as an open-source package for the community.
End-to-end Evolutionary Neural Architecture Search for Microcontroller Units

Smart wearable devices require accurate, fast, and energy-efficient neural networks to allow for optimal application performance. To advance the field of neural architecture search (NAS), we introduce our end-to-end evolutionary NAS (EvoNAS) for microcontroller units that optimize both, pre-processing and neural network architectures. Each neural network architecture is assessed using the multi-objective accuracy, memory footprint, inference time, and energy consumption, to derive a common performance measure to be maximized. To ensure immediate use of all potential solutions on the microcontroller environment, we create a software-hardware chain in which each neural network is deployed to measure the inference time and power consumption directly. In a proof of concept study, we focused on the analysis of audio-based speech commands. Our experiments suggest that 2D convolutional layers with automatically set pre-processing (short-time Fourier transforms) outperform 1D convolutional layers with raw audio signals. We show that our end-to-end EvoNAS scales with the complexity of the classification task and is still able to find constraint-preserving, and thus deployable, Pareto-optimal neural network architectures even when the classification task is more complex. Our proposed EvoNAS approach is dataset and hardware-agnostic, allowing a universal use across a wide range of applications.
Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.
SoK: How Blockchain and Tokenization Will Transform the Energy Sector

Tokenization and blockchain technology might significantly improve the energy industry by making electricity production, transmission, and consumption more open, safe, and effective. Tokenization enables the establishment of digital assets that reflect ownership or access to energy resources and enables blockchain to trace the origin and flow of energy produced. By incorporating these elements, we can build a more distributed and egalitarian energy infrastructure and make it easier to use renewable energy. However, legal barriers, integration with current systems, and scalability remain obstacles to the widespread use of these technologies in the energy industry. Therefore, there is a need for more study and development before the energy industry can ultimately reap the benefits of blockchain and tokenization. Accordingly, in this work, we suggest a systematization of knowledge to capture recent development and summarize research in the blockchain domain and its application to the energy sector.
Beyond Reality: An Application of Extended Reality and Blockchain in the Metaverse

The convergence of Fourth Industrial Revolution (4IR) technologies and Web2 to Web3 transformation, presents significant benefits and opportunities for industry. This study explores the integrated benefits of Extended Reality (XR) and Distributed Ledger Technology (DLT) in a Metaverse application for virtual consulting. Since XR enhances collaboration using visualization, and DLT ensures transactional security and privacy, an overview of XR and DLT technology is provided, highlighting their specific benefits and challenges concerning Remote Health and Wellness consulting. The paper proposes a Metaverse solution to leverage these benefits, enabling more affordable and accessible healthcare consulting in developing economies. A key outcome of the research is to develop a metaverse prototype that bridges the gap between Web 2.0 and Web 3.0 technologies. By doing so, it aims to improve usability for end-users, drive the adoption of XR and Blockchain, generate new business models, and unlock new revenue streams for industry and underserved communities.
Textual Explanations for Automated Commentary Driving

The provision of natural language explanations for the predictions of deep-learning-based vehicle controllers is critical as it enhances transparency and easy audit. In this work, a state-of-the-art (SOTA) prediction and explanation model is thoroughly evaluated and validated (as a benchmark) on the new Sense–Assess–eXplain (SAX). Additionally, we developed a new explainer model that improved over the baseline architecture in two ways: (i) an integration of part of speech prediction and (ii) an introduction of special token penalties. On the BLEU metric, our explanation generation technique outperformed SOTA by a factor of 7.7 when applied on the BDD-X dataset. The description generation technique is also improved by a factor of 1.3. Hence, our work contributes to the realisation of future explainable autonomous vehicles.
FewFine: Few-shot Malware Traffic Classification Via Transfer Learning based on Fine-tuning Strategy

Malware traffic is constantly evolving and remains destructive. The detection and classification of malware traffic is crucial for maintaining cyberspace security. Only by swiftly and accurately detecting and classifying malware traffic can user privacy and cyberspace security be effectively protected.In this paper, we propose FewFine, an approach for few-shot malware traffic classification based on transfer learning. We initially pre-train a detection model and two classification models with substantial quantity of malware and application traffic samples. For classifying new types of malware traffic accurately and promptly, we utilize transfer learning based on fine-tuning strategy and freeze several blocks in the pre-trained model. Utilizing prior knowledge from the pre-trained models, we leverage few samples of novel classes to perform accurate malware detection and classification. We execute extensive experiments on publicly available datasets to evaluate the effectiveness of FewFine. In model pre-training, with considerable number of samples, the accuracy of malware detection and classification can reach 0.99. The pre-trained models are saved for fine-tuning. When detecting and classifying novel malware traffic, FewFine can achieve the accuracy of 0.95 leveraging only 10 samples per class through fine-tuning the pre-trained model. It outperforms methods under comparison in terms of accuracy and efficiency.
Space-Time Conflict Spheres for Constrained Multi-Agent Motion Planning

Multi-agent motion planning (MAMP) is a critical challenge in applications such as connected autonomous vehicles and multi-robot systems. In this paper, we propose a space-time conflict resolution approach for MAMP. We formulate the problem using a novel, flexible sphere-based discretization for trajectories. Our approach leverages a depth-first conflict search strategy to provide the scalability of decoupled approaches while maintaining the computational guarantees of coupled approaches. We compose procedures for evading discretization error and adhering to kinematic constraints in generated solutions. Theoretically, we prove the continuous-time feasibility and formulation-space completeness of our algorithm. Experimentally, we demonstrate that our algorithm matches the performance of the current state of the art with respect to both runtime and solution quality, while expanding upon the abilities of current work through accommodation for both static and dynamic obstacles. We evaluate our algorithm in various unsignalized traffic intersection scenarios using CARLA, an open-source vehicle simulator. Results show significant success rate improvement in spatially constrained settings, involving both connected and non-connected vehicles. Furthermore, we maintain a reasonable suboptimality ratio that scales well among increasingly complex scenarios.
Sequence Labelling Using Multi-Task Learning with Random Connections

Multi-Task Learning (MTL) has received much attention in research about its usage in sequence labelling due to its high generalisation performance and time-saving characteristics. The parameters in the shared layer of MTL models are often challenging to update since the gradients of the various tasks’ losses are typically not equal. However, most of the previous MTL approaches mainly concentrated on architectural optimisation to extract adequate shared knowledge while paying little attention to task balancing. To fill this gap, our approach is to introduce gate units into the MTL model so that it may dynamically learn to assign different weights to specific tasks. We also use a simple but effective random connection mechanism to ensure that our model learns enough task-specific features. Our model allows for the dynamic adjustment of each task weight when trained on several sequence labelling tasks. We evaluated our model’s performance using the CoNLL-2000 and CoNLL-2003 datasets. Compared to the previous baseline methods, our MTL model achieves state-of-the-art results on the named entity recognition task while also receiving competitive scores on part-of-speech tagging and chunking tasks.
Discourses of discrimination in Austrian criminal law

Discussions of diversity and homosexual acts in the context of criminal law often prove highly polarised. This paper is concerned with discourses of discrimination in criminal law, investigating the application of the legal concept of ‘same-sex fornication’ (‘gleichgeschlechtliche Unzucht’) by the Austrian Supreme Court in appeal cases between 1978 and 2014. It seeks to answer these research questions: (1) What are the underlying patterns of perception in legal discourse that legitimised the criminalisation of defendants? (2) How can critical applied legal linguistics contribute to revealing homophobic practices in criminal adjudication? It is found that the Supreme Court has largely maintained and legitimised the criminalisation of individuals engaging in sexual activity with the same sex. It is concluded that critical legal linguistics cannot solve legal problems in the domain of dogmatics, but it can, in conjunction with various other disciplines, allow for new avenues of critique based on authentic language use.
Fair-Siamese Approach for Accurate Fairness in Image Classification

Machine learning models are trained by iteratively fitting their parameters to the features of training data. These features may correlate to sensitive attributes such as race, age, or gender so they could introduce discrimination against minority groups. In a recent study, a fair Siamese network has been applied to discrete structured data under ‘accurate fairness’ constraints, showing promising results of improving fairness without sacrificing accuracy. However, the data augmentation strategy used in their paper cannot be applied to computer vision applications due to the reliance on a discrete perturbation method. In this paper, we adapt the structure of the fair Siamese approach for image classification and address the challenge of data augmentation using CycleGAN. We benchmark the performance of our approach in accuracy and fairness against the adversarial debiasing method. The results show that this adaptation of the fair Siamese approach outperform adversarial debiasing in accuracy and fairness for a variety of image classification tasks.
Underwater object tracker: UOSTrack for marine organism grasping of underwater vehicles

A visual single-object tracker is an indispensable component of underwater vehicles (UVs) in marine organism grasping tasks. Its accuracy and stability are imperative to guide the UVs to perform grasping behavior. Although single-object trackers show competitive performance in the challenge of underwater image degradation, there are still issues with sample imbalance and exclusion of similar objects that need to be addressed for application in marine organism grasping. This paper proposes Underwater OSTrack (UOSTrack), which consists of underwater image and open-air sequence hybrid training (UOHT), and motion-based post-processing (MBPP). The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions. The MBPP paradigm is proposed to exclude similar objects. It uses the estimation box predicted with a Kalman filter and the candidate boxes in the response map to relocate the lost tracked object in the candidate area. UOSTrack achieves an average performance improvement of 4.41% and 7.98% maximum compared to state-of-the-art methods on various benchmarks, respectively. Field experiments have verified the accuracy and stability of our proposed UOSTrack for UVs in marine organism grasping tasks. More details can be found at https://github.com/LiYunfengLYF/UOSTrack.