Reviewerly/exHarmony huggingface dataset:
A positive and unlabeled learning algorithm for mineral prospectivity mapping

Application of supervised machine learning algorithms for mineral prospectivity mapping (MPM) requires positive and negative training samples. Typically, known mineral deposits are considered as positive training samples. However, the selection of negative training samples in the process of MPM is challenging. The one-class classification methods require positive and unlabeled samples or only positive samples; while without requiring negative training samples. In this study, the positive and unlabeled learning (PUL) algorithm was employed to produce a potential map for Fe polymetallic mineralization in southwestern Fujian province, China. This study first examined the sensitivity of the PUL algorithm to different training sets of labeled and unlabeled locations. The predictive results on 10 random unlabeled datasets confirm that PUL modeling with different training sets is reproducible and stable. In addition, the trained model provides a strong spatial correlation between the predictive variables and the locations of known mineral deposits. Finally, the performance of PUL1algorithm is compared to one-class support vector machine (OCSVM), artificial neural networks (ANN), and logistic regression (LR). Comparative results indicate that the PUL model can achieve a better performance in terms of both fitting-rate, prediction-rate, and AUC value compared with OCSVM, ANN and LR. The labelling efforts can be significantly reduced because the PUL algorithm requires only a small number of positive samples and utilizes unlabeled data in training.
Spying with your robot vacuum cleaner

Eavesdropping on private conversations is one of the most common yet detrimental threats to privacy. A number of recent works have explored side-channels on smart devices for recording sounds without permission. This paper presents LidarPhone, a novel acoustic side-channel attack through the lidar sensors equipped in popular commodity robot vacuum cleaners. The core idea is to repurpose the lidar to a laser-based microphone that can sense sounds from subtle vibrations induced on nearby objects. LidarPhone carefully processes and extracts traces of sound signals from inherently noisy laser reflections to capture privacy sensitive information (such as speech emitted by a victim's computer speaker as the victim is engaged in a teleconferencing meeting; or known music clips from television shows emitted by a victim's TV set, potentially leaking the victim's political orientation or viewing preferences). We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and evaluate the feasibility of the attack through comprehensive real-world experiments. We use the prototype to collect both spoken digits and music played by a computer speaker and a TV soundbar, of more than 30k utterances totaling over 19 hours of recorded audio. LidarPhone achieves approximately 91% and 90% average accuracies of digit and music classifications, respectively.
The virtual stages of hate: Using Goffman’s work to conceptualise the motivations for online hate

Cyber hate is increasing. Every 30 seconds, a woman, somewhere, receives an abusive comment on Twitter (Amnesty International). And, it is estimated that around 20% of college students in the United States have been cyber-bullied. This article explores the motivational factors encouraging online hate and abuse. It will draw on Goffman’s seminal work, The Presentation of the Self in Everyday Life, to critically understand online communication, interaction and behaviour. It will define virtual frontstages and virtual backstages. By critically understanding the different characteristics of online and offline communication, it will help us comprehend how Goffman’s dramaturgical model is compromised when applied to online communication. Therefore, the work attempts to update this model, illustrating that virtual stages have blurred which affects behaviours, and exacerbates performances of hate online. As a result, many online platforms have become Virtual Stages of Hate.
A Balanced and Uncertainty-Aware Approach for Partial Domain Adaptation

This work addresses the unsupervised domain adaptation problem, especially in the case of class labels in the target domain being only a subset of those in the source domain. Such a partial transfer setting is realistic but challenging and existing methods always suffer from two key problems, negative transfer and uncertainty propagation. In this paper, we build on domain adversarial learning and propose a novel domain adaptation method BA $$^3$$ US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS), respectively. On one hand, negative transfer results in misclassification of target samples to the classes only present in the source domain. To address this issue, BAA pursues the balance between label distributions across domains in a fairly simple manner. Specifically, it randomly leverages a few source samples to augment the smaller target domain during domain alignment so that classes in different domains are symmetric. On the other hand, a source sample would be denoted as uncertain if there is an incorrect class that has a relatively high prediction score, and such uncertainty easily propagates to unlabeled target data around it during alignment, which severely deteriorates adaptation performance. Thus we present AUS that emphasizes uncertain samples and exploits an adaptive weighted complement entropy objective to encourage incorrect classes to have uniform and low prediction scores. Experimental results on multiple benchmarks demonstrate our BA $$^3$$ US surpasses state-of-the-arts for partial domain adaptation tasks. Code is available at https://github.com/tim-learn/BA3US .
Classroom complexity affects student teachers’ behavior in a VR classroom

Student teachers often struggle to keep track of everything that is happening in the classroom, and particularly to notice and respond when students cause disruptions. The complexity of the classroom environment is a potential contributing factor that has not been empirically tested. In this experimental study, we utilized a virtual reality (VR) classroom to examine whether classroom complexity affects the likelihood of student teachers noticing disruptions and how they react after noticing. Classroom complexity was operationalized as the number of disruptions and the existence of overlapping disruptions (multidimensionality) as well as the existence of parallel teaching tasks (simultaneity). Results showed that student teachers (n = 50) were less likely to notice the scripted disruptions, and also less likely to respond to the disruptions in a comprehensive and effortful manner when facing greater complexity. These results may have implications for both teacher training and the design of VR for training or research purpose. This study contributes to the field from two aspects: 1) it revealed how features of the classroom environment can affect student teachers’ noticing of and reaction to disruptions; and 2) it extends the functionality of the VR environment—from a teacher training tool to a testbed of fundamental classroom processes that are difficult to manipulate in real-life.
Xanadu

Organization of tasks as workflows are an essential feature to expand the applicability of the serverless computing framework. Existing serverless platforms are either agnostic to function chains (workflows as a composition of functions) or rely on naive provisioning and management mechanisms of the serverless framework---an example is that they provision resources after the trigger to each function in a workflow arrives thereby forcing a setup latency for each function in the workflow. In this work, we focus on mitigating the cascading cold start problem--- the latency overheads in triggering a sequence of serverless functions according to a workflow specification. We first establish the nature and extent of the cascading effects in cold start situations across multiple commercial server platforms and cloud providers. Towards mitigating these cascading overheads, we design and develop several optimizations, that are built into our tool Xanadu. Xanadu offers multiple instantiation options based on the desired runtime isolation requirements and supports function chaining with or without explicit workflow specifications. Xanadu's optimizations to address the cascading cold start problem are built on speculative and just-in-time provisioning of resources. Our evaluation of the Xanadu system reveals almost complete elimination of cascading cold starts at minimal cost overheads, outperforming the available state of the art platforms. For even relatively short workflows, Xanadu reduces platform overheads by almost 18x compared to Knative and 10x compared to Apache Openwhisk.
Hybridization of Grasshopper Optimization Algorithm With Genetic Algorithm for Solving System of Non-Linear Equations

A novel algorithm for optimization in this article, called hybrid grasshopper optimization algorithm (GOA) with genetic algorithm (GA): hybrid-GOA-GA, is proposed for solving the system of non-linear equations (SNLEs). First, the SNLEs are converted into an optimization problem. Then, the optimization problem is solved by hybrid-GOA-GA. In the hybrid-GOA-GA, a population of randomized solutions is initialized. These solutions, by GOA, are looking for an optimal solution for SNLE in the domain of optimization problem. During this process, the evolution of these solutions is carried out by GA. Hybrid-GOA-GA integrates the merits of both GOA and GA; where GOA's exploitability and GOA's exploration potential are combined. Furthermore, hybrid-GOA-GA has good capability for escaping from local optima with faster convergence. The hybrid-GOA-GA has been tested by eight benchmarks problems which include different applications. Additionally, the effect of changing the initial intervals of the variables on the efficiency of the proposed algorithm is discussed. Also, the computational cost of the proposed algorithm is studied and compared with other methods. The results show that the hybrid-GOA-GA algorithm is superior to other algorithms, and return the best solution of SNLEs. Finally, in terms of accuracy, the effect of changing initial intervals and computational cost, the proposed approach is competitive and better in most benchmark problems compared to other methods. So, we can say that hybrid-GOA-GA is effective to solve a SNLEs.
Ontology learning: Grand tour and challenges

Ontologies are at the core of the semantic web. As knowledge bases, they are very useful resources for many artificial intelligence applications. Ontology learning, as a research area, proposes techniques to automate several tasks of the ontology construction process to simplify the tedious work of manually building ontologies. In this paper we present the state of the art of this field. Different classes of approaches are covered (linguistic, statistical, and machine learning), including some recent ones (deep-learning-based approaches). In addition, some relevant solutions (frameworks), which offer strategies and built-in methods for ontology learning, are presented. A descriptive summary is made to point out the capabilities of the different contributions based on criteria that have to do with the produced ontology components and the degree of automation. We also highlight the challenge of evaluating ontologies to make them reliable, since it is not a trivial task in this field; it actually represents a research area on its own. Finally, we identify some unresolved issues and open questions.
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images

In the era of open science, public datasets, along with common experimental protocol, help in the process of designing and validating data science algorithms; they also contribute to ease reproductibility and fair comparison between methods. Many datasets for image segmentation are available, each presenting its own challenges; however just a very few exist for radiotherapy planning. This paper is the presentation of a new dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiotherapy. This dataset is called SegTHOR (Segmentation of THoracic Organs at Risk). In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist. Along with the dataset, we present some baseline results, obtained using both the original, state-of-the-art architecture U-Net and a simplified version. We investigate different configurations of this baseline architecture that will serve as comparison for future studies on the SegTHOR dataset. Preliminary results show that room for improvement is left, especially for smallest organs.
TouchID

Due to the widespread use of mobile devices, it is essential to authenticate users on mobile devices to prevent sensitive information leakage. In this paper, we propose TouchID, which combinedly uses the touch sensor and the inertial sensor for gesture analysis, to provide a touch gesture based user authentication scheme. Specifically, TouchID utilizes the touch sensor to analyze the on-screen gesture while using the inertial sensor to analyze the device's motion caused by the touch gesture, and then combines the unique features from the on-screen gesture and the device's motion for user authentication. To mitigate the intra-class difference and reduce the inter-class similarity, we propose a spatial alignment method for sensor data and segment the touch gesture into multiple sub-gestures in space domain, to keep the stability of the same user and enhance the discriminability of different users. To provide a uniform representation of touch gestures with different topological structures, we present a four-part based feature selection method, which classifies a touch gesture into a start node, an end node, the turning node(s), and the smooth paths, and then select effective features from these parts based on Fisher Score. In addition, considering the uncertainty of user's postures, which may change the sensor data of same touch gesture, we propose a multi-threshold kNN based model to adaptively tolerate the posture difference for user authentication. Finally, we implement TouchID on commercial smartphones and conduct extensive experiments to evaluate TouchID. The experiment results show that TouchID can achieve a good performance for user authentication, i.e., having a low equal error rate of 4.90%.
RSAM: Robust Self-Attention Based Multi-Horizon Model for Solar Irradiance Forecasting

With the widespread adoption of renewable energy sources in the smart grid era, there is an utmost requirement to develop prediction models that can accurately forecast solar irradiance. The stochastic nature of solar irradiance considerably affects photo-voltaic (PV) power generation. Since weather conditions have a high impact on solar irradiance; therefore, we need weather-conscious forecasting models to boost predictive accuracy. Although Recurrent Neural Networks (RNNs) has shown considerable performance in time-series forecasting problems, its sequential nature prohibits parallelized computing. Recently, architectures based on self-attention mechanism have shown remarkable success in natural language programming (NLP), while being computationally superior. In this paper, we propose an RSAM (Robust Self-Attention Multi-horizon) forecasting architecture, which mainly works in two parts: First, multi-horizon forecasting of solar irradiance using multiple weather parameters; Second, prediction interval analysis for model robustness using quantile regression. A self-attention based Transformer model belonging to the family of deep learning models has been utilized for multi-variate solar time-series forecasting. Using the National Renewable Energy Laboratory (NREL) benchmark datasets of two different sites, we demonstrate that the proposed approach exhibit enhanced performance in comparison to RNN models in terms of RMSE, MAE, MBE, and Forecast skill at each forecasted interval.
Will Algorithms Blind People? The Effect of Explainable AI and Decision-Makers’ Experience on AI-supported Decision-Making in Government

Computational artificial intelligence (AI) algorithms are increasingly used to support decision making by governments. Yet algorithms often remain opaque to the decision makers and devoid of clear explanations for the decisions made. In this study, we used an experimental approach to compare decision making in three situations: humans making decisions (1) without any support of algorithms, (2) supported by business rules (BR), and (3) supported by machine learning (ML). Participants were asked to make the correct decisions given various scenarios, while BR and ML algorithms could provide correct or incorrect suggestions to the decision maker. This enabled us to evaluate whether the participants were able to understand the limitations of BR and ML. The experiment shows that algorithms help decision makers to make more correct decisions. The findings suggest that explainable AI combined with experience helps them detect incorrect suggestions made by algorithms. However, even experienced persons were not able to identify all mistakes. Ensuring the ability to understand and traceback decisions are not sufficient for avoiding making incorrect decisions. The findings imply that algorithms should be adopted with care and that selecting the appropriate algorithms for supporting decisions and training of decision makers are key factors in increasing accountability and transparency.
HeteGCN

We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features. Existing state-of-the-art graph embedding based methods such as predictive text embedding (PTE) and TextGCN have shortcomings in terms of predictive performance, scalability and inductive capability. To address these limitations, we propose a heterogeneous graph convolutional network (HeteGCN) modeling approach that unites the best aspects of PTE and TextGCN together. The main idea is to learn feature embeddings and derive document embeddings using a HeteGCN architecture with different graphs used across layers. We simplify TextGCN by dissecting into several HeteGCN models which (a) helps to study the usefulness of individual models and (b) offers flexibility in fusing learned embeddings from different models. In effect, the number of model parameters is reduced significantly, enabling faster training and improving performance in small labeled training set scenario. Our detailed experimental studies demonstrate the efficacy of the proposed approach.
Non-Interactive Zero-Knowledge for Blockchain: A Survey

We survey the state-of-the-art non-interactive zero-knowledge argument schemes and their applications in confidential transactions and private smart contracts on blockchain. The main goal of this paper is to serve as a reference for blockchain application developers in finding the most suitable scheme for a particular use case. We give an overview and compare the state-of-the-art protocols for confidential transactions and private smart contracts regarding the protection of the transaction graph and amounts, data and functionality. However, our main focus is on state-of-the-art zero-knowledge argument schemes. We briefly describe their backgrounds, proof lengths and computational complexities and discuss their cryptographic security models. Our focus is on seminal, otherwise notable and, especially, implemented methods that are ready to be applied in practice. We also survey the existing implementations of transforming computations into circuit representations required by those methods. We note that the existing schemes have different strengths and drawbacks regarding usability, setup, proof length and proving and verification costs.
MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection

Differential Power Analysis (DPA) has been an active area of research for the past two decades to study the attacks for extracting secret information from cryptographic implementations through power measurements and their defenses. The research on power side-channels have so far predominantly focused on analyzing implementations of ciphers such as AES, DES, RSA, and recently post-quantum cryptography primitives (e.g., lattices). Meanwhile, machine-learning applications are becoming ubiquitous with several scenarios where the Machine Learning Models are Intellectual Properties requiring confidentiality. Expanding side-channel analysis to Machine Learning Model extraction, however, is largely unexplored. This paper expands the DPA framework to neural-network classifiers. First, it shows DPA attacks during inference to extract the secret model parameters such as weights and biases of a neural network. Second, it proposes the first countermeasures against these attacks by augmenting masking. The resulting design uses novel masked components such as masked adder trees for fully-connected layers and masked Rectifier Linear Units for activation functions. On a SAKURA-X FPGA board, experiments show that the first-order DPA attacks on the unprotected implementation can succeed with only 200 traces and our protection respectively increases the latency and area-cost by 2.8× and 2.3×.
Domain-Adaptive Few-Shot Learning

Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples. However, in practice, this assumption is often invalid -the target classes could come from a different domain. This poses an additional challenge of domain adaptation (DA) with few training samples. In this paper, the problem of domain-adaptive few-shot learning (DA-FSL) is tackled, which is expected to have wide use in real-world scenarios and requires solving FSL and DA in a unified framework. To this end, we propose a novel domain-adversarial prototypical network (DAPN) model. It is designed to address a specific challenge in DA-FSL: the DA objective means that the source and target data distributions need to be aligned, typically through a shared domain-adaptive feature embedding space; but the FSL objective dictates that the target domain per class distribution must be different from that of any source domain class, meaning aligning the distributions across domains may harm the FSL performance. How to achieve global domain distribution alignment whilst maintaining source/target per-class discriminativeness thus becomes the key. Our solution is to explicitly enhance the source/target per-class separation before domain-adaptive feature embedding learning, to alleviate the negative effect of domain alignment on FSL. Extensive experiments show that our DAPN outperforms the state-of-the-arts. The code is available at https://github.com/dingmyu/DAPN.