Reviewerly/exHarmony huggingface dataset:
Applying Blockchain to the Australian Carbon Market

Blockchain is a distributed digital ledger system that establishes transparent contract processes and facilitates secure but trusted business transactions. Policy‐makers around the world are intrigued by the potential of this emerging technology to solve policy problems, including the challenges of the transition away from centralised, linear models of energy generation and consumption towards decentralised and distributed energy systems. Blockchain has also been promoted as a mechanism to transform carbon markets, yet the focus in this area to date has been on using blockchain to create new carbon market schemes. This paper addresses an important research gap by asking how blockchain could be applied to an existing carbon market. To answer this question, the study uses an established design process to develop an Australian carbon market blockchain design. The paper finds that this design could improve the efficiency, equity and effectiveness of the Australian carbon market. This paper makes an important research contribution to carbon market policy development by developing a blockchain design that could improve how an existing carbon market functions, and the findings presented here are relevant to government and industry stakeholders globally.
Predicting day-ahead solar irradiance through gated recurrent unit using weather forecasting data

In the solar power industry, irradiance forecasts are needed for planning, scheduling, and managing of photovoltaic power plants and grid-combined generating systems. A widely used method is artificial intelligence (AI), in particular, artificial neural networks, which can be trained over both historical values of irradiance and meteorological variables such as temperature, humidity, wind speed, pressure, and precipitation. In this paper, a novel version of the gated recurrent unit (GRU) method is combined with weather forecasts in order to predict solar irradiance. This method is used to forecast irradiance over a horizon of 24 h. Experiments show that the proposed method is able to outperform other AI methods. In particular, GRU using weather forecast data reduces the root mean squared error by 23.3% relative to a backpropagation neural network and 11.9% relative to a recurrent neural network. Compared to long short-term memory, the training time is reduced by 36.6%. Compared to persistence, the improvement in the forecast skill of the GRU is 42.0%. In summary, GRU is a promising technology which can be used effectively in irradiance forecasting.
Rewards Prediction-Based Credit Assignment for Reinforcement Learning With Sparse Binary Rewards

In reinforcement learning (RL), a reinforcement signal may be infrequent and delayed, not appearing immediately after the action that triggered the reward. To trace back what sequence of actions contributes to delayed rewards, e.g., credit assignment (CA), is one of the biggest challenges in RL. This challenge is aggravated under sparse binary rewards, especially when rewards are given only after successful completion of the task. To this end, a novel method consisting of key-action detection, among a sequence of actions to perform a task under sparse binary rewards, and CA strategy is proposed. The key-action defined as the most important action contributing to the reward is detected by a deep neural network that predicts future rewards based on the environment information. The rewards are re-assigned to the key-action and its adjacent actions, defined as adjacent-key-actions. Such re-assignment process enables increased success rate and convergence speed during training. For efficient re-assignment, two CA strategies are considered as part of proposed method. Proposed method is combined with hindsight experience replay (HER) for experiments in the OpenAI gym suite robotics environment. In the experiments, it is demonstrated that proposed method can detect key-actions and outperform the HER, increasing success rate and convergence speed, in the Fetch slide task, a type of task that is more exacting as compared to other tasks, but is addressed by few publications in the literature. From the experiments, a guideline for selecting CA strategy according to goal location is provided through goal distribution analysis with dot map.
Performance Envelopes of Virtual Keyboard Text Input Strategies in Virtual Reality

Virtual and Augmented Reality deliver engaging interaction experiences that can transport and extend the capabilities of the user. To ensure these paradigms are more broadly usable and effective, however, it is necessary to also deliver many of the conventional functions of a smartphone or personal computer. It remains unclear how conventional input tasks, such as text entry, can best be translated into virtual and augmented reality. In this paper we examine the performance potential of four alternative text entry strategies in virtual reality (VR). These four strategies are selected to provide full coverage of two fundamental design dimensions: i) physical surface association; and ii) number of engaged fingers. Specifically, we examine typing with index fingers on a surface and in mid-air and typing using all ten fingers on a surface and in mid-air. The central objective is to evaluate the human performance potential of these four typing strategies without being constrained by current tracking and statistical text decoding limitations. To this end we introduce an auto-correction simulator that uses knowledge of the stimulus to emulate statistical text decoding within constrained experimental parameters and use high-precision motion tracking hardware to visualise and detect fingertip interactions. We find that alignment of the virtual keyboard with a physical surface delivers significantly faster entry rates over a mid-air keyboard. Also, users overwhelmingly fail to effectively engage all ten fingers in mid-air typing, resulting in slower entry rates and higher error rates compared to just using two index fingers. In addition to identifying the envelopes of human performance for the four strategies investigated, we also provide a detailed analysis of the underlying features that distinguish each strategy in terms of its performance and behaviour.
Low-Latency Tracking of Multiple Permanent Magnets

Magnetic target tracking is a low-cost, portable, and passive method for tracking materials wherein magnets are physically attached or embedded without the need for line of sight. Traditional magnet tracking techniques use optimization algorithms to determine the positions and orientations of permanent magnets from magnetic field measurements. However, such techniques are constrained by high latencies, primarily due to the numerical calculation of the gradient. In this study, we derive the analytic gradient for multiple-magnet tracking and show a dramatic reduction in tracking latency. We design a physical system comprising an array of magnetometers and one or more spherical magnets. To validate the performance of our tracking algorithm, we compare the magnet tracking estimates with state-of-the-art motion capture measurements for each of four distinct magnet sizes. We find comparable position and orientation errors to state-of-the-art magnet tracking, but demonstrate increased maximum bandwidths of 336%, 525%, 635%, and 773% for the simultaneous tracking of 1, 2, 3, and 4 magnets, respectively. We further show that it is possible to extend the analytic gradient to account for disturbance fields, and we demonstrate the simultaneous tracking of 1 to 4 magnets with disturbance compensation. These findings extend the use of magnetic target tracking to high-speed, real-time applications requiring the tracking of one or more targets without the constraint of a fixed magnetometer array. This advancement enables applications such as low-latency augmented and virtual reality interaction, volitional or reflexive control of prostheses and exoskeletons, and simplified multi-degree-of-freedom magnetic levitation.
BIoMT: Blockchain for the Internet of Medical Things

The Internet of Things promises to connect more than 50 billion devices in a multitude of application domains. However, user privacy and security remain a major challenge in IoMT. In this paper, we present a work in progress for a lightweight blockchain based scheme aiming to secure the Internet of Medical Things (IoMT). The proposed approach consists of four main components: a cloud server, network cluster, medical facility, and smart medical devices. Each medical facility contains a “bolster”, a powerful computing device that operates as a gateway/server to support in-range smart medical devices. The bolster holds a private and secure block role. It is used to securely communicate with other blocks in the same blockchain. Experimental analysis shows that the proposed scheme presents a non-significant overhead; yet it brings major advantages to meet the standard security and privacy requirements in IoMT.
Geo-ecological evaluation of mineral, major and trace elemental composition in waste rocks, soils and sediments of a gold mining area and potential associated risks

An investigation has been undertaken on the distribution of mineral, major and trace elements in mine wastes of a gold mining area from geo-ecological perspective and its association with potential human health risks. Mine waste samples consisting of waste rocks, soils and sediments (including borrow pit, waste dump, stockpile and tailings) were collected in the vicinity of Selinsing gold mine in Malaysia. Major elements in terms of their oxide contents such as SiO2, Al2O3, Fe2O3, K2O and MgO were mainly derived from their mineralogical compositions that were dominated by quartz and muscovite (in waste rocks), kaolinite and illite (in soils) and illite and chlorite-serpentine (in mine tailings). Metallic elements (Al, Fe, Mn, Zn, Sr, Cr, Cd, Ni, Cu, Co, and Pb) were found in the range of acceptable values except for metalloid arsenic. Arsenic was found in the range of 1.84–1915 mg/kg (the highest in the waste rocks of stockpile). Geochemical assessment indicated that some locations were classified as extremely contaminated, highly enriched and having high contamination with respect to arsenic according to geo-accumulation index, enrichment factor and contamination factor. In view of ecological perspective, arsenic contamination was noticeable i.e. some samples were classified as having considerable to high potential ecological risk with respect to arsenic, while contamination with regard to all other metals were classified as having low risk. In terms of health aspect, the hazard index as indicated by the lifetime cancer risk for arsenic was found in tolerable range for regulatory purposes. Other metals possess no significant non-carcinogenic or carcinogenic risks both for adults and children. The arsenic concentrations were comparable with other mining-related sites worldwide, e.g. Spain, China, South Korea, Poland and Mongolia among others. Iron, As and Cd in the tailings and discharges from treatment facilities within the mine have been removed by 82.9–94.7%. Overall, this paper has highlighted the geo-ecological importance and implication of mining exploration to avoid ecological damages so as to sustain mining sector without inflicting the environment.
Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models

Machine learning techniques typically rely on large datasets to create accurate classifiers. However, there are situations when data is scarce and expensive to acquire. This is the case of studies that rely on state-of-the-art computational models which typically take days to run, thus hindering the potential of machine learning tools. In this work, we present a novel classifier that takes advantage of lower fidelity models and inexpensive approximations to predict the binary output of expensive computer simulations. We postulate an autoregressive model between the different levels of fidelity with Gaussian process priors. We adopt a fully Bayesian treatment for the hyper-parameters and use Markov Chain Mont Carlo samplers. We take advantage of the probabilistic nature of the classifier to implement active learning strategies. We also introduce a sparse approximation to enhance the ability of themulti-fidelity classifier to handle large datasets. We test these multi-fidelity classifiers against their single-fidelity counterpart with synthetic data, showing a median computational cost reduction of 23% for a target accuracy of 90%. In an application to cardiac electrophysiology, the multi-fidelity classifier achieves an F1 score, the harmonic mean of precision and recall, of 99.6% compared to 74.1% of a single-fidelity classifier when both are trained with 50 samples. In general, our results show that the multi-fidelity classifiers outperform their single-fidelity counterpart in terms of accuracy in all cases. We envision that this new tool will enable researchers to study classification problems that would otherwise be prohibitively expensive. Source code is available at https://github.com/fsahli/MFclass.
Successive Refinement of Images with Deep Joint Source-Channel Coding

We introduce deep learning based communication methods for successive refinement of images over wireless channels. We present three different strategies for progressive image transmission with deep JSCC, with different complexity-performance tradeoffs, all based on convolutional autoencoders. Numerical results show that deep JSCC not only provides graceful degradation with channel signal-to-noise ratio (SNR) and improved performance in low SNR and low bandwidth regimes compared to state-of-the-art digital communication techniques, but can also successfully learn a layered representation, achieving performance close to a single-layer scheme. These results suggest that natural images encoded with deep JSCC over Gaussian channels are almost successively refinable.
Speech Emotion Recognition Based on Speech Segment Using LSTM with Attention Model

Automatic speech emotion recognition has become popular as it enables natural interaction between human-machine interaction. One modality of recognizing emotion is speech. However, the speech also contains silence that may not relevant to emotion. Two ways to improve performance is by removing silence and/or paying more attention to speech segment while ignoring the silence. In this paper, we propose both, a combination of silence removal and attention model to improve speech emotion recognition performance. The results show that utilizing combination silence removal and attention model outperforms the use of either noise removal only or attention model only.
On the Robustness of Information-Theoretic Privacy Measures and Mechanisms

Consider a data publishing setting for a dataset composed by both private and non-private features. The publisher uses an empirical distribution, estimated from n i.i.d. samples, to design a privacy mechanism which is applied to new fresh samples afterward. In this paper, we study the discrepancy between the privacy-utility guarantees for the empirical distribution, used to design the privacy mechanism, and those for the true distribution, experienced by the privacy mechanism in practice. We first show that, for any privacy mechanism, these discrepancies vanish at speed O(1/√n) with high probability. These bounds follow from our main technical results regarding the Lipschitz continuity of the considered information leakage measures. Then we prove that the optimal privacy mechanisms for the empirical distribution approach the corresponding mechanisms for the true distribution as the sample size n increases, thereby establishing the statistical consistency of the optimal privacy mechanisms. Finally, we introduce and study uniform privacy mechanisms which, by construction, provide privacy to all the distributions within a neighborhood of the estimated distribution and, thereby, guarantee privacy for the true distribution with high probability.
SEthesaurus: WordNet in Software Engineering

Informal discussions on social platforms (e.g., Stack Overflow, CodeProject) have accumulated a large body of programming knowledge in the form of natural language text. Natural language process (NLP) techniques can be utilized to harvest this knowledge base for software engineering tasks. However, consistent vocabulary for a concept is essential to make an effective use of these NLP techniques. Unfortunately, the same concepts are often intentionally or accidentally mentioned in many different morphological forms (such as abbreviations, synonyms and misspellings) in informal discussions. Existing techniques to deal with such morphological forms are either designed for general English or mainly resort to domain-specific lexical rules. A thesaurus, which contains software-specific terms and commonly-used morphological forms, is desirable to perform normalization for software engineering text. However, constructing this thesaurus in a manual way is a challenge task. In this paper, we propose an automatic unsupervised approach to build such a thesaurus. In particular, we first identify software-specific terms by utilizing a software-specific corpus (e.g., Stack Overflow) and a general corpus (e.g., Wikipedia). Then we infer morphological forms of software-specific terms by combining distributed word semantics, domain-specific lexical rules and transformations. Finally, we perform graph analysis on morphological relations. We evaluate the coverage and accuracy of our constructed thesaurus against community-cumulated lists of software-specific terms, abbreviations and synonyms. We also manually examine the correctness of the identified abbreviations and synonyms in our thesaurus. We demonstrate the usefulness of our constructed thesaurus by developing three applications and also verify the generality of our approach in constructing thesauruses from data sources in other domains.
An Augmentation Strategy for Medical Image Processing Based on Statistical Shape Model and 3D Thin Plate Spline for Deep Learning

At present, deep learning has been widely adopted in medical image processing. However, the current deep neural networks depend on a large number of labeled training data, but medical images segmentation tasks often suffer from the problem of small quantity of labeled data because labeling medical images is a very expensive and time-consuming task. In order to overcome this difficulty, this paper proposes a new image augmentation strategy based on statistical shape model and three-dimensional thin plate spline, which can generate many simulated images from a small number of real images. Firstly, the shape information of the real labeled images is modeled with the statistical shape model, and a series of simulated shapes are generated by sampling from this model. Secondly, the simulated shapes are filled with texture using three-dimensional thin plate spline to generate the simulated images. Finally, the simulated images and the real images are used together for training deep neural networks. The proposed framework is a general data augmentation method that can be used in any anatomical structure segmentation tasks with any deep neural network architecture. We used two different datasets, including prostate MRI dataset and liver CT dataset, and used two different deep network structures, including multi-scale 3D Convolutional Neural Networks (multi-scale 3D CNN) and U-net. The experimental results showed that the proposed data augmentation strategy can improve the accuracy of existing segmentation algorithms based on deep neural networks.
Deep language-based critiquing for recommender systems

Critiquing is a method for conversational recommendation that adapts recommendations in response to user preference feedback regarding item attributes. Historical critiquing methods were largely based on constraint- and utility-based methods for modifying recommendations w.r.t. these critiqued attributes. In this paper, we revisit the critiquing approach from the lens of deep learning based recommendation methods and language-based interaction. Concretely, we propose an end-to-end deep learning framework with two variants that extend the Neural Collaborative Filtering architecture with explanation and critiquing components. These architectures not only predict personalized keyphrases for a user and item but also embed language-based feedback in the latent space that in turn modulates subsequent critiqued recommendations. We evaluate the proposed framework on two recommendation datasets containing user reviews. Empirical results show that our modified NCF approach not only provides a strong baseline recommender and high-quality personalized item keyphrase suggestions, but that it also properly suppresses items predicted to have a critiqued keyphrase. In summary, this paper provides a first step to unify deep recommendation and language-based feedback in what we hope to be a rich space for future research in deep critiquing for conversational recommendation.
IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning

Slowly but steadily, the Internet of Things (IoT) is becoming more and more ubiquitous in our daily life.However, it also brings important security and privacy challenges along with it, especially in a sensitive context such as the smart home.In this position paper, we propose a novel architecture for smart home, called IOTFLA, focusing on the security and privacy aspects, which combines federated learning with secure data aggregation.We hope that our proposition will provide a step forward towards achieving more security and privacy in smart homes.
On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression

Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently optimize its parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensionality, how to increase its generalization ability, etc. Literature has shown that by making appropriate connections between fuzzy systems and other machine learning approaches, good practices from other domains may be used to improve the fuzzy systems, and vice versa. This article gives an overview on the functional equivalence between Takagi-Sugeno-Kang fuzzy systems and four classic machine learning approaches-neural networks, mixture of experts, classification and regression trees, and stacking ensemble regression-for regression problems. We also point out some promising new research directions, inspired by the functional equivalence, that could lead to solutions to the aforementioned problems. To our knowledge, this is so far the most comprehensive overview on the connections between fuzzy systems and other popular machine learning approaches, and hopefully will stimulate more hybridization between different machine learning algorithms.
Distributed and Private Coded Matrix Computation with Flexible Communication Load

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. These operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a tradeoff between recovery threshold and communication load. In this work, we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.