Reviewerly/exHarmony huggingface dataset:
Chemical identification of metamorphic protoliths using machine learning methods

The fundamental origins of metamorphic rocks as sedimentary or igneous are integral to the proper interpretation of a terrane’s tectonic and geodynamic evolution. In some cases, the protolith class cannot be determined from field relationships, texture, and/or compositional layering. In this study, we utilize machine learning to predict a metamorphic protolith from its major element chemistry so that accurate interpretation of the geology may proceed when the origin is uncertain or to improve confidence in field predictions. We survey the efficacy of several machine learning techniques to predict the protolith class (igneous or sedimentary) for whole rock geochemical analyses using 9 major oxides. The data are drawn from a global geochemical database with >533 000 geochemical analyses. In addition to metamorphic samples, igneous and sedimentary analyses are used to supplement the dataset based on their similar chemical distributions to their metamorphic counterparts. We train the classifiers on most of the data, retaining ∼10% for post-training validation. We find that the RUSBoost algorithm performs best overall, achieving a true-positive rate of >95% and >85% for igneous- and sedimentary-derived samples, respectively. Even the traditionally-difficult-to-differentiate metasedimentary and metaigneous rocks of granitic–granodioritic composition were consistently identified with a >75% success rate (92% for granite; 85% for granodiorite; 88% for wacke; 76% for arkose). The least correctly identified rock types were iron-rich shale (58%) and quartzolitic rocks (6%). These trained classifiers are able to classify metamorphic protoliths better than common discrimination methods, allowing for the appropriate interpretation of the chemical, physical, and tectonic contextual history of a rock. The preferred classifier is available as a MATLAB function that can be applied to a spreadsheet of geochemical analyses, returning a predicted class and estimated confidence score. We anticipate this classifier’s use as a cheap tool to aid geoscientists in accurate protolith prediction and to increase the size of global geochemical datasets where protolith information is ambiguous or not retained.
Developing an online general type-2 fuzzy classifier using evolving type-1 rules

General type-2 fuzzy systems have been shown to handle more levels of uncertainty present in the majority of real-world applications. Nevertheless, the rapid growth of information generation does not allow utilizing general type-2 models for their complex learning process. This paper introduces a novel online general type-2 fuzzy classifier (called oGT2FC). It aims to reduce the computations needed to get the type-2 fuzzy sets. As in most online and evolving fuzzy schemes, the initial rule-base in oGT2FC is empty, and then the fuzzy rules are generated in completely online manner; without storing the samples. To specify the type-2 fuzzy sets, oGT2FC employs some experts' opinions, drawn from training data, to generate automatically some diverse type-1 fuzzy rule-bases. These type-1 rule-bases are updated/evolved by incoming new samples and are used to construct the general type-2 model. By defining a type-2 fuzzy set as the union of vertical slices, oGT2FC performs the type reduction in a fast and efficient manner. The efficiency of the proposed oGT2FC is assessed experimentally, using synthetic and real-world data streams, via comparing with other type-2 and type-1 evolving fuzzy classifiers as well as some state-of-the-art incremental algorithms. In addition, oGT2FC is compared against some fuzzy classifiers in its ability to model uncertainty.
SeSe-Net: Self-Supervised deep learning for segmentation

Deep convolutional networks have become ubiquitous for image segmentation. But relatively little is known on how to leverage unlabeled data to further improve their performance. Here we introduce a new approach named as Self-supervised deep learning for Segmentation (SeSe-Net) that uses a ‘Worker’ (W) neural network to segment input images and a ‘Supervisor’ (S) neural network to evaluate the quality of the segmentation results. We further propose a two-stage training process. In the first stage, W learns to segment with standard dataset and generates training sets for S. Based on this, S learns how well the W performs for the segmentation task. In the second stage, S transfers the knowledge from the first stage to supervise the learning process of W on unlabeled datasets towards better segmentation performance. We show that SeSe-Net leads to a significant performance boosting with additional unlabeled data. Furthermore, with less than 5% of labeled training dataset, SeSe-Net performs on par with challenging baseline trained on full training dataset in terms of Dice Metric (DM) on three different datasets. Theoretical analysis is also given to establish the convergence of SeSe-Net.
Development and performance evaluation of an electromagnetic tracking system for surgery navigation

In Image Guided Surgery (IGS), an electromagnetic tracking system is used nowadays to locate surgical instruments in the patient's anatomical 3D model. In this work we illustrate the development and the evaluation of a new Electromagnetic Tracking system (EMTS) able to provide a large tracking volume. The innovation of this proposed EMTS consists in the development of the Field Generator (FG), which includes five properly designed coils. Attention has been paid to the magnetic field generated by each coil in order to comply with the safety limits imposed by IEEE Stds C95.1 and C95.6. The simultaneous transmission of the five coils is possible thanks to a suitable frequency division multiplexing. To validate the proposed design, a detailed noise analysis was performed with several experimental tests in different working conditions with the aim of evaluating measurement errors. Actual performance of the system in reconstructing sensor position has been quantified by using a suitable interpolation technique. In particular, mean position error and standard deviation were evaluated for different distances of magnetic sensor from field generator.
Deep Learning based Automatic Modulation Classification for Varying SNR Environment

Automatic modulation classification (AMC) is a crucial task for various communications applications. Deep learning (DL) based classifier is emerging as a prevalent choice for AMC. Previous research on DL based AMC usually assumes an environment of fixed signal to noise ratio (SNR). This paper considers DL based AMC for varying SNR environment. Two algorithms, including M2M4-aided algorithm and multi-label DL based algorithm, are proposed to combat the varying SNR. The former utilizes an M2M4 estimator to estimate SNR, according to which a proper trained DL model can be selected for AMC. The latter exploits multi-label DL to train a model, with which SNR scenario and modulation type can be inferred simultaneously. Experiment results show that the performance of both algorithms is fairly close to that of DL based AMC under fixed SNR environment.
Survey on Evaluating the Performance of Machine Learning Algorithms: Past Contributions and Future Roadmap

The goal of machine learning technology is to optimize the performance of a system when handling new instances of data through user defined programming logic for a given environment. To accomplish this goal effectively and efficiently, machine learning draws heavily on statistics and computer science. Statistical methods provide machine learning algorithms ways to infer conclusions from data. Computer science methods, on the other hand, give machine learning algorithms (MLAs) the computing power to solve problems, including useful large-scale computational architectures and algorithms for capturing, manipulating, indexing, combining and performing predictions with data. The objective of the present research work is to make the reader understand the importance of MLAs in daily life. This will help the readers get familiar with applying MLAs when addressing classification and regression problems. The evaluation parameter used in rating the performance of a regression algorithm is the mean squared error (MSE), whereas, for rating the performance of a classification algorithm, we utilized precision, recall, and F-score as evaluation metrics, which are constructed using a confusion matrix. Additionally, the possible directions in evaluating the performance of both regression and classification models are analyzed using 15 evaluation parameters. The finding in our research is that machine learning algorithms can be applied to analyze the data to extract the hidden patterns from it, which in turns helps in effective decision making.
Combining patient visual timelines with deep learning to predict mortality

Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality.All adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P < 0.001) and the Sequential Organ Failure Assessment score (AUC: 0.91 vs. 0.57, P < 0.001) in the independent validation set. Class-activation heatmaps were utilized to highlight areas of the picture that were most important for making the prediction, thereby providing clinicians with insight into each individual patient's prediction.We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.
Pedagogical Agent Responsive to Eye Tracking in Educational VR

We present an architecture to make a VR pedagogical agent responsive to shifts in user attention monitored by eye tracking. The behavior-based AI includes low-level sensor elements, sensor combiners that compute attention metrics for higher-level sensors called generalized hotspots, an annotation system for arranging scene elements and responses, and its response selection system. We show that the techniques can control the playback of teacher avatar clips that point out and explain objects in a VR oil rig for training.
Combined Task and Motion Planning under Partial Observability: An Optimization-Based Approach

We propose a novel approach to Combined Task and Motion Planning (TAMP) under partial observability. Previous optimization-based TAMP methods [1][2] compute optimal plans and paths assuming full observability. However, partial observability requires the solution to be a policy that reacts to the observations that the agent receives. We consider a formulation where observations introduce additional branching in the symbolic decision tree. The solution is now given by a reactive policy on the symbolic level together with a path tree that describes the branchings of optimal motion depending on the observations. Our method works in two stages: First, the symbolic policy is optimized using approximate path costs estimated from independent optimizations of trajectory pieces. Second, we fix the best symbolic policy and optimize a joint trajectory tree. We test our approach on object manipulation and autonomous driving examples. We also compare the algorithm's performance to a state-of-the-art TAMP planner in fully observable cases.
Attribute Based Encryption (and more) for Nondeterministic Finite Automata from LWE

Constructing Attribute Based Encryption (ABE) [56] for uniform models of computation from standard assumptions, is an important problem, about which very little is known. The only known ABE schemes in this setting that (i) avoid reliance on multilinear maps or indistinguishability obfuscation, (ii) support unbounded length inputs and (iii) permit unbounded key requests to the adversary in the security game, are by Waters from Crypto, 2012 [57] and its variants. Waters provided the first ABE for Deterministic Finite Automata (DFA) satisfying the above properties, from a parametrized or "q-type" assumption over bilinear maps. Generalizing this construction to Nondeterministic Finite Automata (NFA) was left as an explicit open problem in the same work, and has seen no progress to date. Constructions from other assumptions such as more standard pairing based assumptions, or lattice based assumptions has also proved elusive. In this work, we construct the first symmetric key attribute based encryption scheme for nondeterministic finite automata (NFA) from the learning with errors (LWE) assumption. Our scheme supports unbounded length inputs as well as unbounded length machines. In more detail, secret keys in our construction are associated with an NFA M of unbounded length, ciphertexts are associated with a tuple $$(\mathbf {x}, m)$$ where $$\mathbf {x}$$ is a public attribute of unbounded length and m is a secret message bit, and decryption recovers m if and only if $$M(\mathbf {x})=1$$ . Further, we leverage our ABE to achieve (restricted notions of) attribute hiding analogous to the circuit setting, obtaining the first predicate encryption and bounded key functional encryption schemes for NFA from LWE. We achieve machine hiding in the single/bounded key setting to obtain the first reusable garbled NFA from standard assumptions. In terms of lower bounds, we show that secret key functional encryption even for DFAs, with security against unbounded key requests implies indistinguishability obfuscation ( $$\mathsf {iO}$$ ) for circuits; this suggests a barrier in achieving full fledged functional encryption for NFA.
CloudsStorm: A framework for seamlessly programming and controlling virtual infrastructure functions during the DevOps lifecycle of cloud applications

Summary The infrastructure‐as‐a‐service (IaaS) model of cloud computing provides virtual infrastructure functions (VIFs), which allow application developers to flexibly provision suitable virtual machines' (VM) types and locations, and even configure the network connection for each VM. Because of the pay‐as‐you‐go business model, IaaS provides an elastic way to operate applications on demand. However, in current cloud applications DevOps (software development and operations) lifecycle, the VM provisioning steps mainly rely on manually leveraging these VIFs. Moreover, these functions cannot be programmatically embedded into the application logic to control the infrastructure at runtime. Especially, the vendor lock‐in issue, which different clouds provide different VIFs, also enlarges this gap between the cloud infrastructure management and application operation. To mitigate this gap, we designed and implemented a framework, CloudsStorm, which enables developers to easily leverage VIFs of different clouds and program them into their cloud applications. To be specific, CloudsStorm empowers applications with infrastructure programmability at design‐level, infrastructure‐level, and application‐level. CloudsStorm also provides two infrastructure controlling modes, ie, active and passive mode, for applications at runtime. Besides, case studies about operating task‐based and big data applications on clouds show that the monetary cost is significantly reduced through the seamless and on‐demand infrastructure management provided by CloudsStorm. Finally, the scaling and recovery operation evaluations of CloudsStorm are performed to show its controlling performance. Compared with other tools, ie, “jcloud” and “cloudinit.d”, the scaling and provisioning performance evaluations demonstrate that CloudsStorm can achieve at least 10% efficiency improvement in our experiment settings.
A GDPR Controller for IoT Systems: Application to e-Health

General Data Protection Regulation (GDPR) targets personal data protection of the European Union citizens, with a strong input on the rights of people to control their data. Current GDPR solutions are adhoc and are still challenging for scalable systems like Internet of Things (IoT). In this paper, we propose a general solution of a GDPR Controller in IoT systems. The controller gives the data owner a full control of his data: setting security policies, modifying them on run time, tracking data flow and notifying him for any illicit access. The controller architecture is validated and evaluated using an e-health use case with acceptable overhead on the system performance.
Simultaneous Identification and Tracking of Multiple People Using Video and IMUs

Most modern approaches for multiple people tracking rely on human appearance to exploit similarity between person detections. In this work we propose an alternative tracking method that does not depend on visual appearance and is still capable to deal with very dynamic motions and long-term occlusions. We make this feasible by: (i) incorporating additional information from body-worn inertial sensors, (ii) designing a neural network to relate person detections to orientation measurements and (iii) formulating a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. We evaluate our approach on several challenging tracking sequences and achieve a very high IDF1 score of 91.2%. We outperform appearance-based baselines in scenarios where appearance is less informative and are on-par in situations with discriminative people appearance.
Graph Transform Optimization With Application to Image Compression

In this paper, we propose a new graph-based transform and illustrate its potential application to signal compression. Our approach relies on the careful design of a graph that optimizes the overall rate-distortion performance through an effective graph-based transform. We introduce a novel graph estimation algorithm, which uncovers the connectivities between the graph signal values by taking into consideration the coding of both the signal and the graph topology in rate-distortion terms. In particular, we introduce a novel coding solution for the graph by treating the edge weights as another graph signal that lies on the dual graph. Then, the cost of the graph description is introduced in the optimization problem by minimizing the sparsity of the coefficients of its graph Fourier transform (GFT) on the dual graph. In this way, we obtain a convex optimization problem whose solution defines an efficient transform coding strategy. The proposed technique is a general framework that can be applied to different types of signals, and we show two possible application fields, namely natural image coding and piecewise smooth image coding. Experimental results show that the proposed graph-based transform outperforms classical fixed transforms, such as DCT for both natural and piecewise smooth images. In the case of depth map coding, the obtained results are even comparable to the state-of-the-art graph-based coding method that is specifically designed for depth map images.
Logistic approach to intellectual property

In the age of economy’s digitalization, the importance of intangible assets in the activity of enterprises increases, which is caused by the content of fundamental trends, according to which the behavior of economic entities and their way of functioning transform into a new operating model of companies, especially in banking and telecommunications sectors, aimed at increasing cost efficiency and identification of new opportunities in the market mainly on the basis of methods of analysis of large amounts of data to generate new knowledge and make effective management decisions. Accordingly, in the conditions of economy’s digitalization, intellectual property starts to play more and more significant role as a backbone asset of enterprises, causing development of the intellectual property market and the need to formalize its operation and create an efficient infrastructure for the market. This article discusses the issue of intellectual property objects’ turnover in the Russian economy in the conditions of digitalization with funds being invested into specific energy project.
Effects of explicit feature traceability on program comprehension

Developers spend a substantial amount of their time with program comprehension. To improve their comprehension and refresh their memory, developers need to communicate with other developers, read the documentation, and analyze the source code. Many studies show that developers focus primarily on the source code and that small improvements can have a strong impact. As such, it is crucial to bring the code itself into a more comprehensible form. A particular technique for this purpose are explicit feature traces to easily identify a program’s functionalities. To improve our empirical understanding about the effects of feature traces, we report an online experiment with 49 professional software developers. We studied the impact of explicit feature traces, namely annotations and decomposition, on program comprehension and compared them to the same code without traces. Besides this experiment, we also asked our participants about their opinions in order to combine quantitative and qualitative data. Our results indicate that, as opposed to purely object-oriented code: (1) annotations can have positive effects on program comprehension; (2) decomposition can have a negative impact on bug localization; and (3) our participants perceive both techniques as beneficial. Moreover, none of the three code versions yields significant improvements on task completion time. Overall, our results indicate that lightweight traceability, such as using annotations, provides immediate benefits to developers during software development and maintenance without extensive training or tooling; and can improve current industrial practices that rely on heavyweight traceability tools (e.g., DOORS) and retroactive fulfillment of standards (e.g., ISO-26262, DO-178B).
An Analysis on the Effect of Selection on Exploration in Particle Swarm Optimization and Differential Evolution

The goal of exploration to produce diverse search points throughout the search space can be countered by the goal of selection to focus search around the fittest current solution(s). In the limit, if all exploratory search points are rejected by selection, then the behaviour of the metaheuristic will be equivalent to one which performs no exploration at all (e.g. hill climbing). The effects of selection on exploration are clearly important, but our review of the literature indicates limited coverage. To address this deficit, we introduce new experiments which can specifically highlight the occurrence of “failed exploration” and its effects through selection that can trap a metaheuristic in a less promising part of the search space. We subsequently propose new lines of research to reduce the effects of selection and failed exploration which we believe are distinctly different from traditional lines of research to increase (pre-selection) exploration.