Reviewerly/exHarmony huggingface dataset:
Multi-Class Prediction of Mineral Resources Based on Deep Learning

Big data-driven technologies, especially machine learning and deep learning technologies, have been extensively employed in mineral prospectivity prediction. Several approaches have been proposed to learn the deep characteristics of geoscience data, enhance the accuracy of prediction and reduce uncertainty. Nevertheless, the approaches always contain the following two limitations. Firstly, the formation of mineral resources often involves the coupling of multiple factors on a certain spatio-temporal scale, resulting in rare labelled deposits and insufficient number of training samples. Secondly, training Deep Neural Network (DNN) is very challenging. Many approaches are subject to weak interpretability and lack of organic combination with geoscience knowledge. To address these two problems, we propose Geo-Rnet and GCAE (Geological Convolutional Autoencoder). Geo-Rnet is a multi-class mineral prospectivity prediction approach based on improved DNN. GCAE is able to effectively augment multi-disciplinary geoscience data by constructing upon an optimized Convolutional Autoencoder. The experimental results show that most of prospective areas predicted by Geo-Rnet overlap with the labelled mineralization locations, with an average accuracy of 91.1%. In addition, 89.98% of the ore deposits are located in the predicted areas. The results indicate the effectiveness of Geo-Rnet and GCAE for multi-class prediction of mineral resources. Finally, we classify the target area into several mineral prospectiviy areas according to their different mineral types. The research provides an innovative approach for mineral prospectivity prediction in the target area.
Multi-Scale Auto-Encoder for Edge Detection

A multi-scale encoder algorithm is proposed for image edge detection, which takes the auto-encoder as basic backbone structure. Three auto-encoders, each is responsible for processing an image of one scale, are organized together to perform image-to-image prediction by combining all multi-scale convolutional features. Taking the advantage of the multi-scale strategy and self-attention mechanism, the algorithm detects image edges from coarse to fine gradually, and succeeds in detection the edges missed easily in other works. There are two types of skip connections designed in the model structure. One is connected between encoder and decoder within an auto-encoder, worked as the residual function, the other is between auto-encoders, providing multi-scale knowledge about the edges. The loss function is composed of cross-entropy function and Dice coefficient term which is used to handle imbalanced training data. Experimental results evaluated from BSDS500 and BIPED datasets show the algorithm achieves scores of 0.847, 0.858, 0.884 on ODS, OIS, AP indices and output high-quality edges.
Power System Monitoring, Control and protection using IoT and cyber security

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.
Detection of Social Media Hashtag Hijacking Using Dictionary-based and Machine Learning Methods

Nowadays, hashtags are widely utilized on all social media platforms since they deliver numerous benefits, particularly for corporations aiming to reach a larger audience. However, hashtag exploitation has resulted in the problem of hashtag hijacking, which is a type of cyber content threat that anyone or any organization can carry out. As a result, this research presents a framework for detecting social media hashtag hijacking through machine learning algorithms. This paper aims to identify methods to classify relevant and irrelevant hashtags to their contents. This paper demonstrates the unsupervised machine learning method, namely the dictionary-based approach, to classify the relevance of hashtags with the content of tweets on an unlabeled dataset, and also the implementation of supervised machine learning methods, including the Support Vector Machine (SVM), Naive Bayes classifier, and Decision Tree algorithms, to classify the relevance of hashtags used with their contents and compare the machine's performances on labeled datasets. Our results showed that the Support Vector Machine (SVM) performs the best in classifying the relevance of hashtags with an accuracy of 93.36%, an F1 score of 96.19% and ROC-AVC score of 97.22 %. The findings of the study present an automated detection framework for hashtag hijacking that can overcome the limitations of previous studies and adapt to external threats with high performance over time.
Benchmark Performance of Digital QKD Platform Using Quantum Permutation Pad

Quantum permutation pad or QPP is a set of quantum permutation gates.QPP has been demonstrated for quantum secure encryption in both classical and quantum computing systems recently, even at a noisy 5-qubit IBMQ systems.In a classical computing system, QPP encryption is implemented as a permutation gate matrix multiplication with information state vectors.In a quantum computing system, QPP is compiled into a quantum encryption circuit in a native quantum computer and encryption is performed through QPP circuit.Leveraging its quantum mechanical characteristics, we report a digital QKD or D-QKD platform using QPP as a quantum mechanical algorithm implemented in classical systems to distribute quantum entropy, generated from physical quantum random number generators or QRNG, and quantum key over the internet.D-QKD interfaces have been developed to support the photonic QKD standard ETSI-014.This makes any systems with ETSI QKD standards compatible with D-QKD.D-QKD offers point-to-point quantum entropy and quantum key distributions as well as point-to-multi-points quantum key synchronizations with speeds 1000x faster than photonic QKD.This paper reports benchmark performance tests and randomness quality tests for pure quantum entropy generated by a QRNG and expanded entropy using the QPP protocol.
Neural Predictive Monitoring for Collective Adaptive Systems

Reliable bike-sharing systems can lead to numerous environmental, economic and social benefits and therefore play a central role in the effective development of smart cities. Bike-sharing models deal with spatially distributed stations and interact with an unpredictable environment, the users. Monitoring the trustworthiness of such a collective system is of paramount importance to ensure a good quality of the delivered service, but this task can become computationally demanding due to the complexity of the model under study. Neural Predictive Monitoring (NPM) [5], a neural-network learning-based approach to predictive monitoring (PM) with statistical guarantees, can be employed to preemptively detect violations of a specific requirement – e.g. a station has no more bikes available or a station is full. The computational efficiency of NPM makes PM applicable at runtime even on embedded devices with limited computational power. The goal of this paper is to demonstrate the applicability of NPM on collective adaptive systems such as bike-sharing systems. In particular, we first analyze the performance of NPM over a collective system evolving deterministically. Then, following [7], we tackle a more realistic scenario, where sensors allow only for partial observability and where the system evolves in a stochastic fashion. We evaluate the approach on multiple bike sharing network topologies, obtaining highly accurate predictions and effective error detection rules.
Automatic Model-Based Dataset Generation for High-Level Vision Tasks of Autonomous Driving in Haze Weather

Improving the performance of high-level computer vision tasks in adverse weather (e.g., haze) is highly critical for autonomous driving safety. However, collecting and annotating training sets for various high-level tasks in haze weather are expensive and time-consuming. To address this issue, we propose a novel haze generation model called HazeGEN by coupling the variational autoencoder and the generative adversarial network to automatically generate annotated datasets. The proposed HazeGEN leverages a shared latent space assumption based on an optimized encoder–decoder architecture, which guarantees high fidelity in the cross-domain image translations. To ensure that the generated image can truly facilitate high-level vision task performance, a semisupervised learning strategy is developed for HazeGEN to efficiently learn the useful knowledge from both the real-world images (with unsupervised losses) and the synthetic images generated following the atmosphere scattering model (with supervised losses). Extensive experiments and ablation studies demonstrate that training the model with our generated haze dataset greatly improves accuracy in high-level tasks such as semantic segmentation and object detection. Furthermore, one important but under-exploited issue is investigated to find out whether the developed dataset can be a good substitute for the real ones. Results show that the generated dataset has the most similar performance to the real-world collected haze dataset on multiple challenging industrial scenarios compared with prior works.
State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS)

Semantics can be used to assess responses in question answering systems (QAS). The responses are typically short sentences. Assessing short sentences for similarity with the expected answer is a challenge for Artificial Intelligence. Unlike long paragraphs, short texts lacks the adequate and accurate semantic information. Existing algorithms don’t work well for short texts due to insufficient semantic information. This Paper provides the state of art on semantic similarity techniques and proposed the research framework to enhance the accuracy of short texts.
Respond to change or die

Scrum is best learned by doing, e.g. through simulation, as it is simple to understand but difficult to master. During the Covid-19 pandemic, we could not use the traditional face-to-face Scrum Lego simulation game, but had to utilize something workable in an online environment. In this paper we present an online Scrum simulation for distributed teams that was created in a multiplayer game "Don't Starve Together" (DST) through iterative reflective work and analysis. We ran the simulation with 25 Scrum teams on four different courses with participants from eight universities located in three countries. We collected feedback from 244 participants by analysing 196 student learning diaries and 84 student evaluation surveys, and by running a retrospective with 19 industry participants. The participants' feedback was highly positive. The main reported learning outcomes were communication, estimation, Scrum in practice, communication and collaboration with industrial Product Owners, Scrum events, work organisation, teamwork and prioritisation.
Discovering Human-Object Interaction Concepts via Self-Compositional Learning

A comprehensive understanding of human-object interaction (HOI) requires detecting not only a small portion of predefined HOI concepts (or categories) but also other reasonable HOI concepts, while current approaches usually fail to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable combinations of verbs and objects). In this paper, 1) we introduce a novel and challenging task for a comprehensive HOI understanding, which is termed as HOI Concept Discovery; and 2) we devise a self-compositional learning framework (or SCL) for HOI concept discovery. Specifically, we maintain an online updated concept confidence matrix during training: 1) we assign pseudo labels for all composite HOI instances according to the concept confidence matrix for self-training; and 2) we update the concept confidence matrix using the predictions of all composite HOI instances. Therefore, the proposed method enables the learning on both known and unknown HOI concepts. We perform extensive experiments on several popular HOI datasets to demonstrate the effectiveness of the proposed method for HOI concept discovery, object affordance recognition and HOI detection. For example, the proposed self-compositional learning framework significantly improves the performance of 1) HOI concept discovery by over 10% on HICO-DET and over 3% on V-COCO, respectively; 2) object affordance recognition by over 9% mAP on MS-COCO and HICO-DET; and 3) rare-first and non-rare-first unknown HOI detection relatively over 30% and 20%, respectively. Code is publicly available at https://github.com/zhihou7/HOI-CL .
Between JIRA and GitHub

Open-Source Software (OSS) projects have adopted various automations for repetitive tasks in recent years. One common type of automation in OSS is bots. In this exploratory case study, we seek to understand how the adoption of one particular bot (ASFBot) by the Apache Software Foundation (ASF) impacts the discussions in the issue-trackers of these projects. We use the SmartShark dataset to investigate whether the ASFBot affects (i) human comments mentioning pull requests and fixes in issue comments and (ii) the general human comment rate on issues. We apply a regression discontinuity design (RDD) on nine ASF projects that have been active both before and after the ASFBot adoption. Our results indicate (i) an immediate decrease in the number of median comments mentioning pull requests and fixes after the bot adoption, but the trend of a monthly decrease in this comment count is reversed, and (ii) no effect in the number of human comments after the bot adoption. We make an effort to gather first insights in understanding the impact of adopting the ASFBot on the commenting behavior of developers who are working on ASF projects.
Identification of Industrial Alarm Floods Using Time Series Classification and Novelty Detection

Alarm flood classification (AFC) methods are used to support human operators to identify and assess recurring alarm floods in industrial process plants. State-of-the-art AFC methods, however, show shortcomings in handling an ambiguity of the activations and order of alarms and the detection of previously unobserved alarm floods. To solve these limitations, we present a novel three-tier AFC method that uses alarm series as input. In the classification stage, a linear ridge regression classifier with a convolutional kernel-based transformation (MultiRocket) is used to classify alarm floods according to their dynamic properties. In the detection stage, a novelty detection method based on the "local outlier probability" (LoOP) is used to decide whether an unknown alarm flood belongs to a known class or a novel one. Finally, we improve the classification results using an ensemble approach. Our proposed method is compared to two naïve baselines and three relevant methods from the literature using a publicly available dataset based on the "Tennessee-Eastman" process. It is evident that our method shows the highest overall classification performance and robustness of all of the considered methods and effectively overcomes existing challenges in AFC.
Threshold-Adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation

Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning based methods require large amounts of labeled data for training. Manual annotation is expensive, while simulators can provide accurate annotations. However, the performance of the semantic segmentation model trained with synthetic datasets will significantly degenerate in the actual scenes. Unsupervised domain adaptation (UDA) for semantic segmentation is used to reduce the domain gap and improve the performance on the target domain. Existing adversarial-based and self-training methods usually involve complex training procedures, while entropy-based methods have recently received attention for their simplicity and effectiveness. However, entropy-based UDA methods have problems that they barely optimize hard samples and lack an explicit semantic connection between the source and target domains. In this paper, we propose a novel two-stage entropy-based UDA method for semantic segmentation. In stage one, we design a threshold-adaptative unsupervised focal loss to regularize the prediction in the target domain. It first introduces unsupervised focal loss into UDA for semantic segmentation, helping to optimize hard samples and avoiding generating unreliable pseudo-labels in the target domain. In stage two, we employ cross-domain image mixing (CIM) to bridge the semantic knowledge between two domains and incorporate long-tail class pasting to alleviate the class imbalance problem. Extensive experiments on synthetic-to-real and cross-city benchmarks demonstrate the effectiveness of our method. It achieves state-of-the-art performance using DeepLabV2, as well as competitive performance using the lightweight BiSeNet with great advantages in training and inference time.
Detecting Fine-Grained Airplanes in SAR Images With Sparse Attention-Guided Pyramid and Class-Balanced Data Augmentation

Airplane detection in synthetic aperture radar (SAR) images has drawn much attention owing to the success of deep learning methods.However, the development of fine-grained airplane detection in SAR images is still in a dilemma due to the small inter-class variance and the large intra-class variance in complex scenes with strong interference from the background.In addition, the class imbalance problem in multi-class finegrained airplane recognition also significantly limits the direct application of general deep-learning-based airplane detectors.This paper proposes two effective methods to tackle the above two problems, respectively.First, we propose a sparse attentionguided fine-grained pyramid (SA-FP) module to simultaneously sample discriminative local features scattered in multi-scale layers and adaptively aggregate them with fine-grained attention to better classify subordinate-level airplanes with multiple scales.Second, a simple class-balanced copy-paste data augmentation (CC-DA) strategy, which randomly copies an airplane of one category and pastes it onto an image according to the classwise probability, is proposed for class balance.Finally, extensive experiments on one public dataset and three representative deeplearning-based detection benchmarks are conducted to show the effectiveness and generalization of the two proposed methods.The combination of these two methods based on the Cascade R-CNN benchmark also won fifth place in fine-grained airplane detection in SAR images in the 2021 Gaofen challenge.
Rethinking Image Cropping: Exploring Diverse Compositions from Global Views

Existing image cropping works mainly use anchor evaluation methods or coordinate regression methods. However, it is difficult for pre-defined anchors to cover good crops globally, and the regression methods ignore the cropping diversity. In this paper, we regard image cropping as a set prediction problem. A set of crops regressed from multiple learnable anchors is matched with the labeled good crops, and a classifier is trained using the matching results to select a valid subset from all the predictions. This new perspective equips our model with globality and diversity, mitigating the shortcomings but inherit the strengthens of previous methods. Despite the advantages, the set prediction method causes inconsistency between the validity labels and the crops. To deal with this problem, we propose to smooth the validity labels with two different methods. The first method that uses crop qualities as direct guidance is designed for the datasets with nearly dense quality labels. The second method based on the self distillation can be used in sparsely labeled datasets. Experimental results on the public datasets show the merits of our approach over state-of-the-art counterparts.
The Role of Quantum-enhanced Support Vector Machine using Multiparametric MRI Parameters in Differentiating Medulloblastoma from Ependymoma

Brain tumors are the leading cause of death from solid tumors in the childhood. The most common pediatric brain cancers are posterior fossa tumors, with Medulloblastoma (MB) and Ependymoma (EP) being the most common. Although the treatment and prognosis of MB and EP are different, visual characteristics of these tumors are often overlapping, sometimes making the diagnostic process difficult. Therefore, the aim of the current study is twofold: (i) to conduct a comparative analysis with support vector machine (SVM) algorithm and quantum-enhanced support vector machine (QSVM) using multiparametric (mp) MRI of tissue characteristics of whole lesion outlining its diffusivity, cellularity, and vascularity in differentiating MB from EP; and (ii) to explore the effect of noise data on the QSVM. The screening MR examinations of 49 children; MB (n=40) and EP (n=9) were included in the analysis. We used semi quantitative MR imaging features, which give information about the vascularity characteristics of lesion. We utilized precomputed quantum kernel and fed it into classical SVM to classify these tumors. The data gets mapped to high dimensional Hilbert space with PauliFeatureMap. In addition, we further compared the classification performance of QSVM with classical SVM. In the next part of the study, Synthetic Minority Oversampling Technique (SMOTE) method was used for balancing the imbalanced data points of MB and EP. Here, we investigated the performance of QSVM on noisy data. QSVM gave the superior performance with 90% test score value. Classical SVM compared to QSVM, gave the equal performance with 90% for linear, polynomial, radial basis function and sigmoid kernels and lower performance with 80% for poly kernel. In the case of noisy data, the performance of QSVM fell to 50%. Our study confirms that the QSVM method could be used for posterior fossa brain tumor classification problems with higher performance.