Reviewerly/exHarmony huggingface dataset:
Quantitative mineralogical evaluation of Ni-Co laterite ores through XRPD-QPA- and automated SEM-based approaches: The Wingellina (Western Australia) case study

The evaluation of the mineral abundances and metal deportments in Ni-Co laterite ores by means of routine analytical techniques, such as X-Ray Powder Diffraction-based Quantitative Phase Analysis (XRPD-QPA), could be quite challenging or also ineffective, due to occurrence of ore minerals characterized either by complex crystal structures (e.g. smectite and certain Mn-oxy-hydroxides) or by poor structural order (Fe-oxy-hydroxides). In this context, automated Scanning Electron Microscopy (automated SEM)-based methods, which are based on the chemistry and density of various mineral species, could offer breakthroughs in defining the mineralogy and the metal deportments, bypassing the issues related to the presence of phases not evaluable by using solely XRPD-based approaches. For this study we conducted the mineralogical and chemical evaluation of oxide- and clay-dominated mineralised facies occurring in the Wingellina deposit through XRPD-QPA and automated SEM. The results show that the main benefit of using automated SEM is the possibility to directly detect and identify Ni-Co-bearing Mn-oxy-hydroxides (lithiophorite, lithiophorite-asbolane and asbolane) and Ni-bearing clays, which are hardly distinguished at the XRPD-QPA because of their poorly-crystalline structure. Moreover, the use of automated SEM for the analysis of Mn-oxy-hydroxide-dominated samples also allowed determining the Co deportment, which is essential to predict the optimum achievable metals recoveries. The main limitation encountered during the application of the automated SEM in this study was the fine-grained nature of the ore samples, leading to the detection of mixed mineral classes not corresponding to mineral phases sensu stricto.
Social Commonsense Reasoning with Multi-Head Knowledge Attention

Social Commonsense Reasoning requires understanding of text, knowledge about social events and their pragmatic implications, as well as commonsense reasoning skills.In this work we propose a novel multi-head knowledge attention model that encodes semistructured commonsense inference rules and learns to incorporate them in a transformerbased reasoning cell.We assess the model's performance on two tasks that require different reasoning skills: Abductive Natural Language Inference and Counterfactual Invariance Prediction as a new task.We show that our proposed model improves performance over strong state-of-the-art models (i.e., RoBERTa) across both reasoning tasks.Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task.We validate the robustness of the model's reasoning capabilities by perturbing the knowledge and provide qualitative analysis on the model's knowledge incorporation capabilities.Task Context Answer ↵NLI O 1 : Dotty was being very grumpy.H 1 : Dotty ate something bad.H 1 or H 2 H 2 : Dotty call some close friends to chat.O 2 : She felt much better
An Empirical Study of Release Note Production and Usage in Practice

The release note is one of the most important software artifacts that serves as a communication bridge between development teams and users. Release notes contain a set of crucial information, such as descriptions of enhancements, improvements, potential issues, development, evolution, testing, and maintenance of projects throughout the whole development life cycle. A comprehensive understanding of the characteristics of release notes and how to best document one for different targeted users would be highly beneficial. However, the release note is often neglected and has not to date been systematically investigated by researchers. In this paper, we conducted a descriptive case study to investigate release note production and usage in practice. We first performed a large scale empirical study of 32,425 release notes in 1,000 GitHub projects to understand the characteristics of real-world release notes, and eight categories of information identified that are normally documented in release notes. We then conducted interviews with 15 professionals and an online survey with 314 respondents to investigate their opinions on release notes in practice. Our results show that both release note producers and users consider that well-formed release notes impact software activities (e.g., software evolution) positively. We summarised 27 statements about release notes grouped into eight topics based on participants’ opinions. Our study uncovers significant discrepancies between release note producers and users in perceiving release notes. Based on these findings, we provide a set of release note production and usage guidelines for practitioners and highlight future research directions.
Privacy-Preserving Secret Shared Computations using MapReduce

Data outsourcing allows data owners to keep their data at untrusted clouds that do not ensure the privacy of data and/or computations.One useful framework for fault-tolerant data processing in a distributed fashion is MapReduce, which was developed for trusted private clouds.This paper presents algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries such as count, selection including range selection, projection, and join while using MapReduce as an underlying programming model.Our proposed algorithms prevent an adversary from knowing the database or the query while also preventing output-size and access-pattern attacks.Interestingly, our algorithms do not involve the database owner, which only creates and distributes secret-shares once, in answering any query, and hence, the database owner also cannot learn the query.Logically and experimentally, we evaluate the efficiency of the algorithms on the following parameters: (i) the number of communication rounds (between a user and a server), (ii) the total amount of bit flow (between a user and a server), and (iii) the computational load at the user and the server.
The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination

This paper is based on fundamental research to develop the interface structure around the grains and to control the layers between two grains, as a prospective media for high-level electronic parameters integrations. We performed the experiments based on nano-BaTiO3 powders with Y additives. All results on dielectric parameters on submicron level are the part of global values the same measured characteristics at the bulk samples. The original idea is to develop the new computing ways to network electronic parameters in thin layers between the grains on the way to get and to compare the values on the samples. Artificial neural networks are computing tools that map input-output data and could be applied on ceramic electronic parameters. These are developed in the manner signals are processed in biological neural networks. The signals are processed by using elements which represent artificial neurons, which have a simple function to process input signal, as well as adjustable parameter which has an influence to change output signal. The total network output presents the sum of a large number neurons outputs. This important research idea is to connect analysis results and neural networks. There is a great interest to connect all of these microcapacitances by neural network with the goal to compare the results in the standard bulk samples measurements frame and microelectronics parameters. The final result of the study was functional relation definition between consolidation parameters, voltage (U) and relative capacitance change, from the level of the bulk sample down to the grains boundaries.
A Reactive Batching Strategy of Apache Kafka for Reliable Stream Processing in Real-time

Modern stream processing systems need to process large volumes of data in real-time. Various stream processing frameworks have been developed and messaging systems are widely applied to transfer streaming data among different applications. As a distributed messaging system with growing popularity, Apache Kafka processes streaming data in small batches for efficiency. However, the robustness of Kafka's batching method against variable operating conditions is not known. In this paper we study the impact of the batch size on the performance of Kafka. Both configuration parameters, the spatial and temporal batch size, are considered. We build a Kafka testbed using Docker containers to analyze the distribution of Kafka's end-to-end latency. The experimental results indicate that evaluating the mean latency only is unreliable in the context of real-time systems. In the experiments where network faults are injected, we find that the batch size affects the message loss rate in the presence of an unstable network connection. However, allocating resources for message processing and delivery that will violate the reliability requirements implemented as latency constraints of a real-time system is inefficient To address these challenges we propose a reactive batching strategy. We evaluate our batching strategy in both good and poor network conditions. The results show that the strategy is powerful enough to meet both latency and throughput constraints even when network conditions are variable.
Incremental Answer Set Programming with Overgrounding

Repeated executions of reasoning tasks for varying inputs are necessary in many applicative settings, such as stream reasoning. In this context, we propose an incremental grounding approach for the answer set semantics. We focus on the possibility of generating incrementally larger ground logic programs equivalent to a given non-ground one; so called overgrounded programs can be reused in combination with deliberately many different sets of inputs. Updating overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is repeated on a series of inputs similar to each other. Notably, the proposed approach works "under the hood", relieving designers of logic programs from controlling technical aspects of grounding engines and answer set systems. In this work we present the theoretical basis of the proposed incremental grounding technique, we illustrate the consequent repeated evaluation strategy and report about our experiments. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET

Abstract This paper reports an algorithm to automatically identify the chain of events leading to a disruption, evaluating the so-called reference warning time. This time separates the plasma current flat-top of each disrupted discharge into two parts: a non-disrupted part and a pre-disrupted one. The algorithm can be framed into the anomaly detection techniques as it aims to detect the off-normal behavior of the plasma. It is based on a statistical analysis of a set of dimensionless plasma parameters computed for a selection of discharges from the JET experimental campaigns. In every data-driven model, such as the generative topographic mapping (GTM) predictor proposed in this paper, it is indeed necessary to label the samples needed for training the model itself. The samples describing the disruption-free behavior are extracted from the plasma current flat-top phase of the regularly terminated discharges. The disrupted space is described by all the samples belonging to the pre-disruptive phase of each disruptive discharge in the training set. Note that a proper selection of the pre-disruptive phase plays a key role in the prediction performance of the model. Moreover, these models, which are highly dependent on the training input space, may be particularly prone to degradation as the operational space of any experimental machine is continuously evolving. Hence, a regular schedule of model review and retrain must be planned. The proposed algorithm avoids the cumbersome and time-consuming manual identification of the warning times, helping to implement a continuous learning system that could be automated, despite being offline. In this paper, the automatically evaluated warning times are compared with those obtained with a manual analysis in terms of the impact on the mapping of the JET input parameter space using the GTM methodology. Moreover, the algorithm has been used to build the GTM of recent experimental campaigns, with promising results.
Adaptive Gradient Quantization for Data-Parallel SGD

Many communication-efficient variants of SGD use gradient quantization schemes. These schemes are often heuristic and fixed over the course of training. We empirically observe that the statistics of gradients of deep models change during the training. Motivated by this observation, we introduce two adaptive quantization schemes, ALQ and AMQ. In both schemes, processors update their compression schemes in parallel by efficiently computing sufficient statistics of a parametric distribution. We improve the validation accuracy by almost 2% on CIFAR-10 and 1% on ImageNet in challenging low-cost communication setups. Our adaptive methods are also significantly more robust to the choice of hyperparameters.
Clustering of visible and infrared solar irradiance for solar architecture design and analysis

Incoming solar radiation is a key factor influencing solar architecture design. It determines the thermal and optical regime of the building envelope and affects the solar heat and light transfer between the indoors and outdoors. Computational analysis is an essential tool in solar architecture design. Usually, an entire year’s weather data in a conventional weather file can be imported into such computational analyses. Solar irradiance data used in a conventional solar architecture design analytics are broadband (the total of UV, VIS, and NIR); however, these three components play different roles in building energy efficiency. So, analyzing individual solar components separately can be desirable. This research is to develop estimation models of the VIS and NIR components that can be captured efficiently from readily available datasets collected from the ground weather stations; such a model can then be conveniently implemented into current solar architecture design and research. We explored and tested classification-based modeling methods for decomposing hourly broadband global horizontal solar irradiance data in conventional weather files into hourly global horizontal solar VIS and NIR components. Furthermore, a workflow of how to implement these models in solar architecture design and analysis has been developed and discussed herein.
A comparison of different metaheuristics for the quadratic assignment problem in accelerated systems

Although metaheuristics are used for solving complex and real-world problems, they do not provide an exact solution, but only an approximate solution in feasible time. However, for very large instances, a metaheuristic may take a considerable amount of time. Therefore, we utilize a highly parallel metaheuristic to run on a modern massively parallel graphics processing unit (GPU) to further reduce the execution time. Additionally, because metaheuristics are not problem-specific, it is interesting to determine which metaheuristic is best for each problem type. In this study, using a massive parallel machine such as a GPU, we evaluate the performance of different metaheuristics such as the iterated local search, simulated annealing, genetic algorithm, tabu search, particle swarm optimization, and crow search algorithm with respect to the quadratic assignment problem.
Data-Driven Transform-Based Compressed Image Quality Assessment

Image quality assessment is a critical problem for image compression, which can be utilized as a guidance for image compression and codec evaluation. In this paper, we propose a full reference image quality assessment (IQA) algorithm to measure the perceptual quality of compressed images. The proposed IQA model utilizes a data-driven transform, multi-stage Karhunen-Loeve Transform (MS-KLT), as a feature extractor to decompose both reference and compressed images into feature domain, where the importance of feature distortions in different spectral components to human visual system (HVS) is easy to distinguish. Accordingly, an efficient weighting strategy is proposed to reflect the importance of feature distortions based on the energy of transformed coefficients. Considering HVS characteristics, weighted spatial masking effect is derived from both local and global perspectives. In addition, to avoid influences of random noises, a local adaptive low-pass filtering process is applied as a pre-processing operation. Extensive experimental results on popular datasets show that our proposed method correlates better with the subjective results compared with the state-of-the-art algorithms. Moreover, the proposed method behaves more robustly compared with existing methods, and achieves top-ranking performance on different IQA datasets.
Adaptively Meshed Video Stabilization

Video stabilization is essential for improving the visual quality of shaky videos. Current video stabilization methods usually take feature trajectories in the background to estimate one global transformation matrix or several transformation matrices based on a fixed mesh, and warp shaky frames into their stabilized views. However, these methods may not model the shaky camera motion well in complicated scenes, such as scenes containing large foreground objects or strong parallax, and may result in notable visual artifacts in the stabilized videos. To resolve the above issues, this paper proposes an adaptively meshed method to stabilize a shaky video based on all of its feature trajectories and an adaptive blocking strategy. More specifically, we first extract the feature trajectories of the shaky video and then generate a triangle mesh according to the distribution of the feature trajectories in each frame. Then, the transformations between shaky frames and their stabilized views over all triangular grids of the mesh are calculated to stabilize the shaky video. Since more feature trajectories can usually be extracted from all of the regions, including both the background and foreground regions, a finer mesh will be obtained and provided for camera motion estimation and frame warping. We estimate the mesh-based transformations of each frame by solving a two-stage optimization problem. Moreover, foreground and background feature trajectories are no longer distinguished and both contribute to the estimation of the camera motion in the proposed optimization problem, yielding better estimation performance than previous works, particularly in challenging videos with large foreground objects or strong parallax. To further enhance the robustness of our method, we propose two adaptive weighting mechanisms to improve its spatial and temporal adaptability. Experimental results demonstrate the effectiveness of our method in producing visually pleasing stabilization effects in various challenging videos.
FCAZ-recognition based on declustered earthquake catalogs

The article presents the results of FCAZ-recognition of the strongest ( ≥ 7.75) earthquakeprone areas on the Pacific coast of the Kamchatka Peninsula and strong ( ≥ 6.5) earthquakeprone areas in California.For the first time, earthquake epicenters from declustered catalogs were used as recognition objects.Based on the example of the considered regions it is shown that the presence of foreshock and aftershock sequences in the earthquake catalogs does not significantly affect the results of FCAZ-recognition based on the clustering study of weak earthquake epicenters.
Estimation of Pure States Using Three Measurement Bases

We introduce a method to estimate unknown pure $d$-dimensional quantum states using the probability distributions associated with only three measurement bases. The measurement results of $2d$ projectors are used to generate a set of ${2}^{d\ensuremath{-}1}$ states, the value of the likelihood function of which is evaluated using the measurement results of the remaining $d$ projectors. The state with the highest value of the likelihood function is the estimate of the unknown state. The method estimates all pure states except for a null-measure set, which can be overcome by adapting two bases. The viability of the protocol is experimentally demonstrated using two different and complementary high-dimensional quantum information platforms. First, by exploring the photonic path-encoding strategy, we validate the method on a single eight-dimensional quantum system. Then, we resort to the five-superconducting-qubit IBM quantum processor to demonstrate the high performance of the method in the multipartite scenario.
River Sediment Geochemistry as a Conservative Mixture of Source Regions: Observations and Predictions From the Cairngorms, UK

Abstract The elemental composition of sediments in rivers is the product of physical and chemical erosion of rocks, which is then transported across drainage networks. A corollary is that fluvial sedimentary geochemistry can be used to understand geologic, climatic, and geomorphic processes. Here, we predict elemental compositions of river sediments using drainage networks extracted from digital elevation data and erosional models. The Geochemical Baseline Survey of the Environment was used to quantify substrate (i.e., source region) chemistry. Sedimentary compositions in rivers downstream are predicted by formally integrating eroding substrates with respect to distance downstream. Different erosional models, including the Stream Power model and uniform incision rates, are tested. Predictions are tested using a new suite of compositions obtained from fine grained (&lt;150 μm) sediments at 67 sites along the Spey, Dee, Don, Deveron, and Tay rivers, Cairngorms, UK. Results show that sedimentary geochemistry can be predicted using simple models that include the topography of drainage networks and substrate compositions as input. The concentration of numerous elements including Magnesium, Rubidium, Uranium, Potassium, Calcium, Strontium, and Beryllium can be accurately predicted using this simple approach. Predictions are insensitive to the choice of erosional model, which we suggest is a consequence of broadly homogeneous rates of erosion throughout the study area. Principal component analysis of the river geochemical data suggests that the composition of most Cairngorms river sediments can be explained by mafic/felsic provenance and conservative mixing downstream. These results suggest that the elemental composition of river sediments can be accurately predicted using simple erosional models and digital elevation data.
A Systematic Review for Service Humanoid Robotics Model in Hospitality

Abstract Nowadays, the Fourth Industrial Revolution has brought artificial intelligence to the forefront, and more and more intelligent robots begin to be used in the hospitality industry. In this study, the application of service humanoid robots in the hospitality industry is investigated based on Cardiff Metropolitan University EUREKA Robotics Lab’s robot as reported by Lab (in Eureka robotics lab, 2017, https://www.cardiffmet.ac.uk/technologies/Pages/EUREKA-Robotics-Lab.aspx ). The research ontology of this study is post-positivism. The research philosophy of this research is phenomenology. Phenomenological studies have indicated that this phenomenon can only be truly understood and experienced through subjective immersive research directly involving researchers, and the interaction among researchers is an integral part of the research. In this study, the data are collated through case researches and experimental interviews. Finally, Some proposals for transforming the traditional hospitality industry into the direction of intelligence is summarized. In future research, a technical model combining artificial intelligence will be further developed.