Reviewerly/exHarmony huggingface dataset:
RefDiff4Go

Refactoring is a key software development practice that seeks to improve the internal structure of the code without changing its external behavior. In this way, the identification of refactorings is a key source of information for researchers, practitioners, and tool builders. However, existing approaches and tools documented in the literature do not support emerging ecosystems, such as the one of the Go programming language, which is nowadays widely used to develop robust and popular projects. To address these challenges, we present in this paper RefDiff4Go, a Go extension for RefDiff, which is a multi-language refactoring detection tool. RefDiff4Go detects 13 refactoring types in Go projects. We also evaluated RefDiff4Go with six well-known Go open-source projects, achieving 92% of precision and 80% of recall. Finally, we show that RefDiff4Go runtime performance supports its adoption in professional software projects.
BodySLAM: Opportunistic User Digitization in Multi-User AR/VR Experiences

Today's augmented and virtual reality (AR/VR) systems do not provide body, hand or mouth tracking without special worn sensors or external infrastructure. Simultaneously, AR/VR systems are increasingly being used in co-located, multi-user experiences, opening the possibility for opportunistic capture of other users. This is the core idea behind BodySLAM, which uses disparate camera views from users to digitize the body, hands and mouth of other people, and then relay that information back to the respective users. If a user is seen by two or more people, 3D pose can be estimated via stereo reconstruction. Our system also maps the arrangement of users in real world coordinates. Our approach requires no additional hardware or sensors beyond what is already found in commercial AR/VR devices, such as Microsoft HoloLens or Oculus Quest.
Meta Reinforcement Learning with Generative Adversarial Reward from Expert Knowledge

Meta learning has been widely applied in the field of multi-task Reinforcement Learning. In meta-learning, a meta-model is obtained through a large number of pre-trainings and is able to adapt quickly and well on the unseen task in test. However, previous meta Reinforcement Learning methods often require vast computation cost and well-designed reward function, which are hardly available in many real world tasks, such as self-driving and robots control. On the other hand, in such cases there exist plenty of demonstrations from experts. In order to alleviate the above problems, this paper proposes a meta learning method with expert knowledge in sparse reward scenario. By introducing expert knowledge into a meta learning framework, the training speed and generalization performance of a meta-model are enhanced. Experiments show that our method can effectively improve the training speed of the meta-model. In addition, in sparse reward setting, the convergence speed, as well as the generalization ability of the proposed method, are significantly better than classic meta learning method.
Defending Poisoning Attacks in Federated Learning via Adversarial Training Method

Recently, federated learning has shown its significant advantages in protecting training data privacy by maintaining a joint model across multiple clients. However, its model security issues have not only been recently explored but shown that federated learning exhibits inherent vulnerabilities on the active attacks launched by malicious participants. Poisoning is one of the most powerful active attacks where an inside attacker can upload the crafted local model updates to further impact the global model performance. In this paper, we first illustrate how the poisoning attack works in the context of federated learning. Then, we correspondingly propose a defense method that mainly relies upon a well-researched adversarial training technique: pivotal training, which improves the robustness of the global model with poisoned local updates. The main contribution of this work is that the countermeasure method is simple and scalable since it does not require complex accuracy validations, while only changing the optimization objectives and loss functions. We finally demonstrate the effectiveness of our proposed mitigation mechanisms through extensive experiments.
Runtime testing of context-aware variability in adaptive systems

Context: A Dynamically Adaptive System (DAS) supports runtime adaptations to handle changes in the operational environment. These adaptations can change the system’s structure or behavior and even the logic of its adaptation mechanism. However, these adaptations may insert defects, leading the system to fail at runtime. Objective: Aiming to identify these failures, testing can be executed to verify the system at runtime. Studies in the literature mostly focus on testing to verify the adaptations at design-time or functionalities at runtime, rather than exercising the adaptation mechanism at runtime. So, we propose RETAkE (RuntimE Testing of dynamically Adaptive systEms). Method: RETAkE is an approach to perform the runtime testing based on the system’s context variability and feature modeling. RETAkE tests the adaptation mechanism, enabling the verification of its adaptation rules with the system’s variability model. The runtime testing is supported by the verification of behavioral properties. For the evaluation, we used the mutation testing technique with two DAS. We also conducted an evaluation to measure the overhead introduced when RETAkE is integrated to the DAS. Results: RETAkE identified the mutants in the two mobile DAS, but the results vary due to the probabilistic nature of the approach to generate test sequences. Regarding the overhead, test sequences of size 30 had a low impact. However, bigger test sequences increase the overhead. Conclusion: The integration of RETAkE to the DAS adaptation mechanism can support the discovery of adaptation failures that occur at runtime. Furthermore, the results of the evaluation suggest its feasibility to perform runtime testing.
Designing for oral storytelling practices at home: A parental perspective

Storytelling at home is typically an oral practice that supports parents and children to make sense of their family identity. Parents play a key role in crafting the story plot and facilitating the child’s participation in the storytelling process. Yet in the context of digital technology, interaction design researchers have tended to focus on children and how digital storytelling tools can support them in their learning. Adopting a “family lens” the aim of this research is to understand the character of oral storytelling practice and identify opportunities for digital storytelling design. The findings outline a co-design workshop that involved a team of designers and parents, who regularly engaged in oral storytelling. Grounded in a systematic video analysis of the workshop alongside a reflection of the design decisions that unfolded, we contribute four new design opportunities centring on the themes of flexibility, shared experience, minimalism, and autobiographical memories. These opportunities can guide interaction design researchers interested in designing new digital oral storytelling tools for families.
Approximating Posterior Cramér–Rao Bounds for Nonlinear Filtering Problems Using Gaussian Mixture Models

The posterior Cramér-Rao bound (PCRB) is a fundamental tool to assess the accuracy limit of the Bayesian estimation problem. In this article, we propose a novel framework to compute the PCRB for the general nonlinear filtering problem with additive white Gaussian noise. It uses the Gaussian mixture model to represent and propagate the uncertainty contained in the state vector and uses the Gauss-Hermite quadrature rule to compute mathematical expectations of vector-valued nonlinear functions of the state variable. The detailed pseudocodes for both the small and large component covariance cases are also presented. Three numerical experiments are conducted. All of the results show that the proposed method has high accuracy and it is more efficient than the plain Monte Carlo integration approach in the small component covariance case.
Real-Time Path Planning for Strategic Missions

Robot navigation is still an open research topic, mainly regarding applications that require online planning. In this context, this manuscript presents an implementation of the Lifelong Planning A* (LPA*) search algorithm to guide a mobile robot in an environment that changes along time, by detecting obstacles and updating the current mapping information. Firstly, simulations validate the strategy, and later experiments confirm these results considering a real application. The result is that the LPA* algorithm is able to guide the mobile robot to its target in a changing environment. Obstacles are interactively included in the scene to force the route redesign and the seeking for a new optimal solution connecting the current robot position and its target position.
Co-residency Attacks on Containers are Real

Public clouds are inherently multi-tenant: applications deployed by different parties (including malicious ones) may reside on the same physical machines and share various hardware resources. With the introduction of newer hypervisors, containerization frameworks like Docker, and managed/orchestrated clusters using systems like Kubernetes, cloud providers downplay the feasibility of co-tenant attacks by marketing a belief that applications do not operate on shared hardware. In this paper, we challenge the conventional wisdom that attackers cannot confirm co-residency with a victim application from inside state-of-the-art containers running on virtual machines. We analyze the degree of vulnerability present in containers running on various systems including within a broad range of commercially utilized orchestrators. Our results show that on commercial cloud environments including AWS and Azure, we can obtain over 90% success rates for co-residency detection using real-life workloads. Our investigation confirms that co-residency attacks are a significant concern on containers running on modern orchestration systems.
Exploration for strategic placer mineral deposits in a fluctuating shoreline: Depositional environment and mineralogical characterization of the NE Odisha coast placers, India

Systematic exploration for beach placer minerals has been undertaken in the Chandipur area of the Balasore district on the NE Odisha coast of India. Beach and dune sands are ubiquitous along the 1–1.5 km-wide NNE-trending coast. Forty-three representative samples were collected from selected boreholes sampled by auger and dormer drilling at depths ranging from 6.0 m to 12.0 m. Detailed experimental studies show that the total heavy minerals (THM) grade of the study area varies from 0.4 to 11.2% with an average grade of 3.8% for the individual samples; the upper 5.50 m sand column with a grade of 5.2% is most significant. The heavy mineral assemblages, in decreasing volumetric percentages, are comprised of 55% ilmenite, 25% sillimanite, 11% pyriboles/others (pyroxene, amphibole, mica, coated grains etc.), 5.9% rutile, 1.5% leucoxene, 1.2% garnet, 0.5% zircon, and 0.4% magnetite. Texturally, elevated heavy mineral concentrations are drawn from +200 ASTM (+75 µm) and 120 ASTM (+125 µm) mesh size fraction; the 40 ASTM (425 µm) size fraction contains lowest THM. The overall concentration of heavy minerals shows variable distribution patterns from shore to littoral (inland), and the maximum concentration of THM is observed within the 1.5 m sandy surface horizon. The present study also addresses the down-core variation of heavy mineral assemblages and role of hydrodynamic conditions in the deposition of these wide-ranging placer minerals. The range of graphic mean sizes (Mz) varies from 2.19 to 2.60 (Φ), which corroborates the deposition of sediments in low energy condition in a fluctuating shoreline along the Chandipur coast. Linear discriminant function analysis is predominantly indicative of diversity in the depositional features under aeolian conditions in a shallow marine environment. CM and Tractive current plots indicate that the sediments are mainly formed by graded suspension and without any rolling conditions in the beach subenvironment in a tractive current field. Both the perennial (Panchpara River) and the ephemeral drainage systems (Dubdubi River) played a dominant role in transporting and supplying sediment flux consisting of radioactive mineral (monazite) assemblages in the study area. The present detailed exploration study reveals a new find of placer deposits with radioactive and non-radioactive mineral assemblages formed from diverse sources.
Efficient Deterministic Conditional Sampling of Multivariate Gaussian Densities

We propose a fast method for deterministic multi-variate Gaussian sampling. In many application scenarios, the commonly used stochastic Gaussian sampling could simply be replaced by our method – yielding comparable results with a much smaller number of samples. Conformity between the reference Gaussian density function and the distribution of samples is established by minimizing a distance measure between Gaussian density and Dirac mixture density. A modified Cramér-von Mises distance of the Localized Cumulative Distributions (LCDs) of the two densities is employed that allows a direct comparison between continuous and discrete densities in higher dimensions. Because numerical minimization of this distance measure is not feasible under real time constraints, we propose to build a library that maintains sample locations from the standard normal distribution as a template for each number of samples in each dimension. During run time, the requested sample set is re-scaled according to the eigenvalues of the covariance matrix, rotated according to the eigenvectors, and translated according to the mean vector, thus adequately representing arbitrary multivariate normal distributions.
CryptoSPN

The ubiquitous deployment of machine learning (ML) technologies has certainly improved many applications but also raised challenging privacy concerns, as sensitive client data is usually processed remotely at the discretion of a service provider. Therefore, privacy-preserving machine learning (PPML) aims at providing privacy using techniques such as secure multi-party computation (SMPC).
An Evaluation Framework to Assess Autonomous Navigation Linked to Environment Complexity

The evaluation of mobile robots performing autonomous navigation has recently gained significant attention, not only by the research community, but also by companies wanting to deploy robotic services into the market. In this work we present a framework for the assessment of autonomous navigation performance referenced to the environmental complexity in which the robot operates. The framework consists in selecting a set of metrics, proposing guidelines and comparing in the same test scenario theoretical optimal and measured trajectory performances while linking them to the environment complexity. Supporting the framework, two innovative algorithms are employed: one for trajectory planning and the other to assess the environment complexity. Initial results show the applicability of this approach.
Text Sentiment Classification Based on LSTM-TCN Hybrid Model and Attention Mechanism

Aiming at the problem that traditional convolutional neural networks cannot fully capture text features during feature extraction, and a single model cannot effectively extract deep text features, this paper proposes a text sentiment classification method based on the attention mechanism of LSTM-TCN hybrid model. First, use the Word2vec word vector model to transform words into the form of word vectors. Secondly, with Long Short-Term Memory (LSTM) to obtain the serialized feature information of the text, and then combined with the Temporal Convolutional Network (TCN) to sample and calculate the text features to extract more comprehensive feature information of the text. Then, combined with the attention mechanism, the influence of important information on the sentiment of the text is considered, and the effect of text sentiment classification is optimized. Finally, use softmax function to classify sentiment in the text classification layer. Multiple sets of comparative experiments were conducted on two sets of Chinese data sets, and the accuracy of the text model reached 93.06% and 91.08%, respectively, proving that the proposed model performs better than the traditional single convolutional neural network.
A Framework for Evaluating the Resilience Contribution of Solar PV and Battery Storage on the Grid

Motivated by decreased cost and climate change concerns, the penetration of solar photovoltaic (PV) energy generation and battery energy storage has been continually increasing. The variability in solar PV power generation has led to many new challenges for utilities and researchers. One challenge is the quantification of the resilience contribution to the grid from its assets and is the topic of this paper. In this work, we propose a framework for evaluating the resilience contribution of solar generation and battery storage assets on the grid. The metric provides a quantifiable adaptive capacity measure in terms of real and reactive power and includes uncertainty for solar PV assets. A case study using very short-term and short-term solar generation forecast demonstrates the framework and provides useful insight to the resilience solar and battery storage assets can contribute to the grid.
HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification

Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. In this paper, we propose a semi-supervised deep multi-task classification and localization approach HydraMix-Net in the field of medical imagining where labelling is time consuming and costly. Firstly, the pseudo labels are generated using the model’s prediction on the augmented set of unlabelled image with averaging. The high entropy predictions are further sharpened to reduced the entropy and are then mixed with the labelled set for training. The model is trained in multi-task learning manner with noise tolerant joint loss for classification localization and achieves better performance when given limited data in contrast to a simple deep model. On DLBCL data it achieves 80% accuracy in contrast to simple CNN achieving 70% accuracy when given only 100 labelled examples.
Averaging-Based Ensemble Methods for the Partial Label Ranking Problem

Label Ranking (LR) is a well-known non-standard supervised classification problem, with the goal of inducing preference models able to predict a ranking (permutation) over a finite set of labels from datasets in which the instances are explicitly labelled with (possibly) incomplete rankings. In this work, we focus on the Partial Label Ranking (PLR) problem, a more general approach of the LR problem that allows tied labels (i.e., there is no particular preference among them) in both, the training dataset and the prediction given as output. The main contributions of this paper are: (i) to use unsupervised discretization techniques (equal-frequency and equal-width) to heuristically (but fast) select the threshold for numerical features, and (ii) to apply averaging-based ensembles (Bagging and Random Forests) to improve the robustness of these decision trees. From the experimental results, we can conclude that the ensembles are better (in terms of accuracy) than the nearest neighbors based-method (IBPLR). Also, they are competitive in efficiency (CPU time) when the decision trees are built with the unsupervised discretization techniques.
An Integrated ARMA-Based Deep Autoencoder and GRU Classifier System for Enhanced Recognition of Daily Hand Activities

Recognition of hand activities of daily living (hand-ADL) is useful in the areas of human–computer interactions, lifelogging, and healthcare applications. However, developing a reliable human activity recognition (HAR) system for hand-ADL with only a single wearable sensor is still a challenge due to hand movements that are typically transient and sporadic. Approaches based on deep learning methodologies to reduce noise and extract relevant features directly from raw data are becoming more promising for implementing such HAR systems. In this work, we present an ARMA-based deep autoencoder and a deep recurrent network (RNN) using Gated Recurrent Unit (GRU) for recognition of hand-ADL using signals from a single IMU wearable sensor. The integrated ARMA-based autoencoder denoises raw time-series signals of hand activities, such that better representation of human hand activities can be made. Then, our deep RNN-GRU recognizes seven hand-ADL based upon the output of the autoencoder: namely, Open Door, Close Door, Open Refrigerator, Close Refrigerator, Open Drawer, Close Drawer, and Drink from Cup. The proposed methodology using RNN-GRU with autoencoder achieves a mean accuracy of 84.94% and F1-score of 83.05% outperforming conventional classifiers such as RNN-LSTM, BRNN-LSTM, CNN, and Hybrid-RNNs by 4–10% higher in both accuracy and F1-score. The experimental results also showed the use of the autoencoder improves both the accuracy and F1-score of each conventional classifier by 12.8% in RNN-LSTM, 4.37% in BRNN-LSTM, 15.45% CNN, 14.6% Hybrid RNN, and 12.4% for the proposed RNN-GRU.
The Potential for Implementing a Big Data Analytic-based Smart Village in Indonesia

Smart village is one of the solutions to reduce poverty in rural areas. The main objective of this research is to map the potential implementation of the concept of smart villages based on big data analytics in Indonesia. This research was conducted through the elaboration of text mining-based Systematic Literature Review (SLR) with multiple regression analysis of the 2018 Village Potential Data in Indonesia. The contribution of this study is the production of a map describing the potential for implementing smart villages based on big data analytics in Indonesia. SLR cluster analysis produces a dendrogram that maps the basic terminology of smart villages based on big data analytic. Indonesia has quite substantial economic and social capital resources, which has a positive effect on the poor and farmers/fishermen (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.9759 and 0.9482) in the villages. This occurs through a mix of regional budget revenue (APBD) and local self-subsistent (Swadaya) funding schemes in the management of agricultural and non-agricultural small businesses in the village. Indonesia also has sufficient capital for managing information and communication technology (ICT) in the village for the development of big data analytic smart villages. There is a relatively strong influence on the poor and farmers/fishermen (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.5946 and 0.6006). Therefore, the challenge for future research to develop a smart village model based on big data analytics that is appropriate to the territory of Indonesia. This model needs to be elaborated with diverse factors including economic, social, cultural and smart educational potential as well should include indicators of the potential for data technology available on various media, through the framework of agriculture big data analytic.