Reviewerly/exHarmony huggingface dataset:
Grouped Gaussian processes for solar power prediction

We consider multi-task regression models where the observations are assumed to be a linear combination of several latent node functions and weight functions, which are both drawn from Gaussian process priors. Driven by the problem of developing scalable methods for forecasting distributed solar and other renewable power generation, we propose coupled priors over groups of (node or weight) processes to exploit spatial dependence between functions. We estimate forecast models for solar power at multiple distributed sites and ground wind speed at multiple proximate weather stations. Our results show that our approach maintains or improves point-prediction accuracy relative to competing solar benchmarks and improves over wind forecast benchmark models on all measures. Our approach consistently dominates the equivalent model without coupled priors, achieving faster gains in forecast accuracy. At the same time our approach provides better quantification of predictive uncertainties.
COSMO: Contextualized scene modeling with Boltzmann Machines

Scene modeling is very crucial for robots that need to perceive, reason about and manipulate the objects in their environments. In this paper, we adapt and extend Boltzmann Machines (BMs) for contextualized scene modeling. Although there are many models on the subject, ours is the first to bring together objects, relations, and affordances in a highly-capable generative model. For this end, we introduce a hybrid version of BMs where relations and affordances are incorporated with shared, tri-way connections into the model. Moreover, we introduce a dataset for relation estimation and modeling studies. We evaluate our method in comparison with several baselines on object estimation, out-of-context object detection, relation estimation, and affordance estimation tasks. Moreover, to illustrate the generative capability of the model, we show several example scenes that the model is able to generate, and demonstrate the benefits of the model on a humanoid robot. The code and the dataset are publicly made available at: https://github.com/bozcani/COSMO.
An improved reinforcement learning algorithm based on knowledge transfer and applications in autonomous vehicles

Autonomous learning is a crucially important capability of intelligent robots. As one of the most fashionable machine learning techniques, the reinforcement learning (RL) enables an agent taking an optimized action by interacting with the environment so as to maximize some notion of cumulative reward. In this paper, an improved RL algorithm, named as the KT-HA-Q(λ) algorithm, is proposed by resorting to the knowledge transfer of source domain. First, a BP neural network and a liner sensor network are skillfully constructed to perform the knowledge transfer of source task for weight initialization in target task, and the knowledge transfer on actions of case base obtained by source domain, respectively. Then, the novel case base expansion and progressive forgetting criterion, which realize the balance between new experience via online learning and historical experience in the case base, are developed to enhance the learning efficiency and the learning rate. Furthermore, an improved heuristic function is proposed by replacing the action traditionally obtained via a selection strategy by the experience action. This function acts as a crucial role for both the best action selection and its Q value calculation. Finally, the proposed algorithm is utilized in the hill-climbing experiment of unmanned vehicles under a complex 3D scene by transferring the knowledge obtained in a 2D scene. The results of contrast experiments verified the advantages and effectiveness of the proposed algorithm.
Determination of Te, As, Bi, Sb and Se (TABS) in Geological Reference Materials and Geo<i>PT</i> Proficiency Test Materials by Hydride Generation‐Atomic Fluorescence Spectrometry (HG‐AFS)

The study of Te, As, Bi, Sb and Se (TABS) has increased over the past years due to their use in the development of low‐carbon energy technologies. However, there is a scarcity of mass fraction values of TABS in geological reference materials. This underlines the difficulty in undertaking routine determinations of these elements. The mass fractions of TABS were determined in geological reference materials using hydride generation‐atomic fluorescence spectrometry (HG‐AFS), calibrated with standard solutions. Comparisons with literature values were used to validate the method. Samples from the Geo PT proficiency test were also analysed. For most elements, there are no assigned or even provisional values for many of the Geo PT and reference materials because of the wide range of results reported. For mass fractions above the quantification limit of the method, our results are in good agreement with the median of Geo PT results. Thus, we propose Geo PT median values as informational values for these elements. In contrast, at mass fractions &lt; 0.5 µg g −1 median values of Se from Geo PT are systematically higher than our results. Our Se results are in agreement with the reference materials down to 0.02 µg g −1 , which suggest that many of the results for Se reported in Geo PT testing are too high.
Corpus-based Set Expansion with Lexical Features and Distributed Representations

Corpus-based set expansion refers to mining "sibling" entities of some given seed entities from a corpus. Previous works are limited to using either textual context matching or semantic matching to fulfill this task. Neither matching method takes full advantage of the rich information in free text. We present CaSE, an efficient unsupervised corpus-based set expansion framework that leverages lexical features as well as distributed representations of entities for the set expansion task. Experiments show that CaSE outperforms state-of-the-art set expansion algorithms in terms of expansion accuracy.
A Flexible Convolutional Solver for Fast Style Transfers

We propose a new flexible deep convolutional neural network (convnet) to perform fast neural style transfers. Our network is trained to solve approximately, but rapidly, the artistic style transfer problem of [Gatys et al.] for arbritary styles. While solutions already exist, our network is uniquely flexible by design: it can be manipulated at runtime to enforce new constraints on the final output. As examples, we show that it can be modified to perform tasks such as fast photorealistic style transfer, or fast video style transfer with short term consistency, with no retraining. This flexibility stems from the proposed architecture which is obtained by unrolling the gradient descent algorithm used in [Gatys et al.]. Regularisations added to [Gatys et al.] to solve a new task can be reported on-the-fly in our network, even after training.
Self-Paced Adversarial Training for Multimodal Few-Shot Learning

State-of-the-art deep learning algorithms yield remark-able results in many visual recognition tasks. However, they still fail to provide satisfactory results in scarce data regimes. To a certain extent this lack of data can be compensated by multimodal information. Missing information in one modality of a single data point (e.g. an image) can be made up for in another modality (e.g. a textual description). Therefore, we design a few-shot learning task that is multimodal during training (i.e. image and text) and single-modal during test time (i.e. image). In this regard, we pro-pose a self-paced class-discriminative generative adversarial network incorporating multimodality in the context off ew-shot learning. The proposed approach builds upon the idea of cross-modal data generation in order to alleviate the data sparsity problem. We improve few-shot learning accuracies on the fine grained CUB and Oxford-102 datasets.
Surface Light Field Compression Using a Point Cloud Codec

Light field (LF) representations aim to provide photo-realistic, free-viewpoint viewing experiences. However, the most popular LF representations are the images from multiple views. Multi-view image-based representations generally need to restrict the range or degrees of freedom of the viewing experience to what can be interpolated in the image domain, essentially because they lack explicit geometry information. We present a new surface LF (SLF) representation based on explicit geometry and a method for SLF compression. First, we map the multi-view images of a scene onto a 3-D geometric point cloud. The color of each point in the point cloud is a function of viewing direction known as a view map. We represent each view map efficiently in a B-Spline wavelet basis. This representation is capable of modeling diverse surface materials and complex lighting conditions in a highly scalable and adaptive manner. The coefficients of the B-Spline wavelet representation are then compressed spatially. To increase the spatial correlation and, thus, improve compression efficiency, we introduce a smoothing term to make the coefficients more similar across the 3-D space. We compress the coefficients spatially using existing point cloud compression methods. On the decoder side, the scene is rendered efficiently from any viewing direction by reconstructing the view map at each point. In contrast to multi-view image-based LF approaches, our method supports photo-realistic rendering of real-world scenes from arbitrary viewpoints, i.e., with an unlimited six degrees of freedom. In terms of rate and distortion, experimental results show that our method achieves superior performance with lighter decoder complexity compared with a reference image-plus-geometry compression scheme, indicating its potential in practical virtual and augmented reality applications.
Modeling SOA-Based IoT Applications with SoaML4IoT

A common feature of Internet of Things (IoT) applications is heterogeneity, regarding network protocols, data formats, hardware and software platforms. Aiming to deal with this heterogeneity, several frameworks have applied the Model-Driven Development (MDD) to build IoT applications. On the software architecture point of view, the literature has shown that the Service-Oriented Architecture (SOA) is one of the most suitable style to address the interoperability of heterogeneous entities composing these solutions. However, to the best of our knowledge, there is no modeling language tailored for the design of SOA-based IoT applications. This paper aims at bridging this gap by introducing SoaML4IoT. The modeling language extends the SoaML, the standard graphical modeling language proposed by the OMG for designing systems that follow the services approach.
Nonparametric Density Estimation Using Copula Transform, Bayesian Sequential Partitioning, and Diffusion-Based Kernel Estimator

Non-parametric density estimation methods are more flexible than parametric methods, due to the fact that they do not assume any specific shape or structure for the data. Most non-parametric methods, like Kernel estimation, require tuning of parameters to achieve good data smoothing, a non-trivial task, even in low dimensions. In higher dimensions, sparsity of data in local neighborhoods becomes a challenge even for non-parametric methods. In this paper, we use the copula transform and two efficient non-parametric methods to develop a new method for improved non-parametric density estimation in multivariate domain. After separation of marginal and joint densities using copula transform, a diffusion-based kernel estimator is employed to estimate the marginals. Next, Bayesian sequential partitioning (BSP) is used in the joint density estimation.
Efficient Proofs of Retrievability with Public Verifiability for Dynamic Cloud Storage

Cloud service providers offer various facilities to their clients. The clients with limited resources opt for some of these facilities. They can outsource their bulk data to the cloud server. The cloud server maintains these data in lieu of monetary benefits. However, a malicious cloud server might delete some of these data to save some space and offer this extra amount of storage to another client. Therefore, the client might not retrieve her file (or some portions of it) as often as needed. Proofs of retrievability (POR) provide an assurance to the client that the server is actually storing all of her data appropriately and they can be retrieved at any point of time. In a dynamic POR scheme, the client can update her data after she uploads them to the cloud server. Moreover, in publicly verifiable POR schemes, the client can delegate her auditing task to some third party specialized for this purpose. In this work, we exploit the homomorphic hashing technique to design a publicly verifiable dynamic POR scheme that is more efficient (in terms of bandwidth required between the client and the server) than the “state-of-the-art” publicly verifiable dynamic POR scheme. We also analyze security and performance of our scheme.
Green data center networks: a holistic survey and design guidelines

Data Center Networks (DCNs) are attracting immense interest from the industry, research and academia to keep pace with the increase of Internet services demands. One of the major concerns that draws the attention of researchers is the exponential growth of the energy consumption and carbon emission of the DCNs. Studies conducted to identify the causes of the increasing energy consumption have proved that the growing size of computing demand, the over-provisioning of the networking resources, the under-utilization of the infrastructure, the fault-tolerance, the high bandwidth exigence and the inefficient hardware and cooling structure are leading to considerable energy waste. Therefore, in recent years, new data center (DC) architectures are proposed where new hardware types and new technologies are implemented for the sake of energy efficiency. Other efforts are focusing on designing algorithms and strategies to enhance the utilization of the network resources. Replacing brown power by renewable energy was also one of the attractive ideas to minimize the energy costs. In this survey paper, we will present energy-related problems in data centers and review the state of the art of the research literature on energy efficient architectures, techniques, technologies, resource management, and thermal control and monitoring. Additionally, we present the challenges facing each approach and the strategies to build a green DC. This paper serves as a specification document that shows step by step how to minimize the energy consumption of different components of the system.
Exploiting Natural Language Structures in Software Informal Documentation

Communication means, such as issue trackers, mailing lists, Q&A forums, and app reviews, are premier means of collaboration among developers, and between developers and end-users. Analyzing such sources of information is crucial to build recommenders for developers, for example suggesting experts, re-documenting source code, or transforming user feedback in maintenance and evolution strategies for developers. To ease this analysis, in previous work we proposed Development Emails Content Analyzer (DECA), a tool based on Natural Language Parsing that classifies with high precision development emails' fragments according to their purpose. However, DECA has to be trained through a manual tagging of relevant patterns, which is often effort-intensive, error-prone and requires specific expertise in natural language parsing. In this paper, we first show, with an empirical study, the extent to which producing rules for identifying such patterns requires effort, depending on the nature and complexity of patterns. Then, we propose an approach, named Nlp-based softwarE dOcumentation aNalyzer (NEON), that automatically mines such rules, minimizing the manual effort. We assess the performances of NEON in the analysis and classification of mobile app reviews, developers discussions, and issues. NEON simplifies the patterns identification and rules definition processes, allowing a savings of more than 70 percent of the time otherwise spent on performing such activities manually. Results also show that NEON-generated rules are close to the manually identified ones, achieving comparable recall.
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio

Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible perturbations to audio samples. To help researchers and practitioners gain better understanding of the impact of such attacks, and to provide them with tools to help them more easily evaluate and craft strong defenses for their models, we present Adagio, the first tool designed to allow interactive experimentation with adversarial attacks and defenses on an ASR model in real time, both visually and aurally. Adagio incorporates AMR and MP3 audio compression techniques as defenses, which users can interactively apply to attacked audio samples. We show that these techniques, which are based on psychoacoustic principles, effectively eliminate targeted attacks, reducing the attack success rate from 92.5% to 0%. We will demonstrate Adagio and invite the audience to try it on the Mozilla Common Voice dataset. Code related to this paper is available at: https://github.com/nilakshdas/ADAGIO .
A Longitudinal Study of Identifying and Paying Down Architecture Debt

Architecture debt is a form of technical debt that derives from the gap between the intended and the actual architecture design. In this study we measured architecture debt in two ways: 1) in terms of system-wide coupling measures, and 2) in terms of the number and severity of architecture flaws. In recent research it was shown that the amount of architecture debt has a huge impact on software maintainability and evolution. Consequently, reducing debt is expected to make software less costly and more amenable to change. This paper reports on a longitudinal study of a healthcare communications product created by BrightSquid Secure Communications Corp. This young company is facing the typical trade-off problem of desiring responsiveness to change requests, but wanting to avoid the ever-increasing effort that the accumulation of quick-and-dirty changes eventually incurs. In the first stage of the study, we analyzed the status of the "before" system, which showed the impacts of change requests. This initial study motivated a more in-depth analysis of architecture debt. The results of this debt analysis were used in the second stage of the work to motivate a comprehensive refactoring of the software system. The third stage was a follow-on architecture debt analysis which quantified the improvements realized. Using this quantitative evidence, augmented by qualitative evidence gathered from in-depth interviews with BrightSquid's architects, we present lessons learned about the costs and benefits of paying down architecture debt in practice.
Residual Codean Autoencoder for Facial Attribute Analysis

Facial attributes can provide rich ancillary information which can be utilized for different applications such as targeted marketing, human computer interaction, and law enforcement. This research focuses on facial attribute prediction using a novel deep learning formulation, termed as R-Codean autoencoder. The paper first presents Cosine similarity based loss function in an autoencoder which is then incorporated into the Euclidean distance based autoencoder to formulate R-Codean. The proposed loss function thus aims to incorporate both magnitude and direction of image vectors during feature learning. Inspired by the utility of shortcut connections in deep models to facilitate learning of optimal parameters, without incurring the problem of vanishing gradient, the proposed formulation is extended to incorporate shortcut connections in the architecture. The proposed R-Codean autoencoder is utilized in facial attribute prediction framework which incorporates patch-based weighting mechanism for assigning higher weights to relevant patches for each attribute. The experimental results on publicly available CelebA and LFWA datasets demonstrate the efficacy of the proposed approach in addressing this challenging problem.
Developing a scoring rubric for L2 summary writing: a hybrid approach combining analytic and holistic assessment

In our research project, we have developed a scoring rubric for a second language (L2) summary writing for English as a foreign language (EFL) students in Japanese universities. This study aimed to examine the applicability of our five-dimensional rubric, which features both analytic and holistic assessments, to classrooms in the EFL context. The examination especially focused on a newly added, optional overall quality dimension and two paraphrasing dimensions: paraphrase (quantity) and paraphrase (quality). Six teacher raters evaluated 16 summaries written by Japanese EFL university student writers using our new rubric. The scoring results were quantitatively compared with the scoring results of a commonly used rubric developed by the Educational Testing Service (ETS). For the qualitative examination, the teacher raters' retrospective comments on our five-dimensional rubric were analyzed. The quantitative examination demonstrated positive results as follows: (a) adding the optional overall quality dimension improved the reliability of our rubric, (b) the overall quality dimension worked well even if it was used alone, (c) our rubric and the ETS holistic rubric overlapped moderately as L2 summary writing assessments, and (d) the two paraphrasing dimensions covered similar but different aspects of paraphrasing. However, the quantitative analysis using the generalizability theory (G theory) simulated that the reliability (generalizability coefficients) of the rubric was not high when the number of raters decreased. The qualitative examination of the raters' perceptions of our rubric generally confirmed the quantitative results. This study confirmed the applicability of our rubric to EFL classrooms. This new type of rating scale can be characterized as a "hybrid" approach that offers the user a choice of analytic and holistic measures depending on individual purposes, which can enhance teachers' explicit instructions.
Static data structure lower bounds imply rigidity

We show that static data structure lower bounds in the group (linear) model imply semi-explicit lower bounds on matrix rigidity. In particular, we prove that an explicit lower bound of t ≥ ω(log2 n) on the cell-probe complexity of linear data structures in the group model, even against arbitrarily small linear space (s= (1+)n), would already imply a semi-explicit (PNP) construction of rigid matrices with significantly better parameters than the current state of art (Alon, Panigrahy and Yekhanin, 2009). Our results further assert that polynomial (t≥ nδ) data structure lower bounds against near-optimal space, would imply super-linear circuit lower bounds for log-depth linear circuits (a four-decade open question). In the succinct space regime (s=n+o(n)), we show that any improvement on current cell-probe lower bounds in the linear model would also imply new rigidity bounds. Our results rely on a new connection between the “inner” and “outer” dimensions of a matrix (Paturi and Pudlák, 2006), and on a new reduction from worst-case to average-case rigidity, which is of independent interest.