Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy usage and to have positive effects on their environment. Human interaction in the context of cyber-physical systems is a core component and consideration in the implementation of any smart building technology. Research has shown that the adoption of human-centric building services and amenities leads to improvements in the operational efficiency of these cyber-physical systems directed towards controlling building energy usage. We introduce a strategy in form of a game-theoretic framework that incorporates humans-in-the-loop modeling by creating an interface to allow building managers to interact with occupants and potentially incentivize energy efficient behavior. Prior works on game theoretic analysis typically rely on the assumption that the utility function of each individual agent is known a priori. Instead, we propose novel utility learning framework for benchmarking that employs robust estimations of occupant actions towards energy efficiency. To improve forecasting performance, we extend the utility learning scheme by leveraging deep bi-directional recurrent neural networks. Using the proposed methods on data gathered from occupant actions for resources such as room lighting, we forecast patterns of energy resource usage to demonstrate the prediction performance of the methods. The results of our study show that we can achieve a highly accurate representation of the ground truth for occupant energy resource usage. We also demonstrate the explainable nature on human decision making towards energy usage inherent in the dataset using graphical lasso and granger causality algorithms. Finally, we open source the de-identified, high-dimensional data pertaining to the energy game-theoretic framework.
Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs

Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. However, in many of the applications, the underlying graph changes over time and existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs based on a tensor algebra framework. Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique. Theoretical results that establish the connection between the proposed tensor approach and spectral convolution of tensors are developed. Numerical experiments on real datasets demonstrate the usefulness of the proposed method for an edge classification task on dynamic graphs.
Analysis of Cooperative Hybrid ARQ with Adaptive Modulation and Coding on a Correlated Fading Channel

In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.
Lateral Astroturfing Attacks on Twitter Trending Topics

Astroturfing attacks use automated accounts to artificially propel a chosen keyword to the top of Twitter trending topics. Lateral astroturfing is a sophisticated subset of such attacks in which the automated tweets 1) are posted by compromised accounts and 2) are deleted immediately after they are created. The former makes the attack more effective and the latter aids in evading detection. We present the first large-scale analysis of lateral astroturfing attacks. We detected over 20 thousand astroturfing accounts that were used between February 2019 and June 2019 to manipulate 3,710 unique keywords --- at least 10% of daily trending topics in the region analyzed. Lateral astroturfing pollutes trending topics; allows for the manipulation of users' opinions; and permits content that could otherwise be filtered by the platform, such as illicit advertisements. Our results aid in understanding user manipulation on social media and more generally shed light on the type of adversarial behavior that arises to evade detection.
Not Just Cloud Privacy: Protecting Client Privacy in Teacher-Student Learning

Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One recent popular approach to study these concerns is using the differential privacy via a "teacher-student" model, wherein the teacher provides the student with useful, but noisy, information, hopefully allowing the student model to perform well on a given task. However, these studies only solve the privacy concerns of the teacher by assuming the student owns a public but unlabelled dataset. In real life, the student also has privacy concerns on its unlabelled data, so as to inquire about privacy protection on any data sent to the teacher. In this work, we re-design the privacy-preserving "teacher-student" model consisting of adopting both private arbitrary masking and local differential privacy, which protects the sensitive information of each student sample. However, the traditional training of teacher model is not robust on any perturbed data. We use the adversarial learning techniques to improve the robustness of the perturbed sample that supports returning good feedback without having all private information of each student sample. The experimental results demonstrate the effectiveness of our new privacy-preserving "teacher-student" model.
A survey of deep learning techniques for autonomous driving

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.
RGB-D Individual Segmentation

Fine-grained recognition task deals with sub-category classification problem, which is important for real-world applications. In this work, we are particularly interested in the segmentation task on the \emph{finest-grained} level, which is specifically named "individual segmentation". In other words, the individual-level category has no sub-category under it. Segmentation problem in the individual level reveals some new properties, limited training data for single individual object, unknown background, and difficulty for the use of depth. To address these new problems, we propose a "Context Less-Aware" (CoLA) pipeline, which produces RGB-D object-predominated images that have less background context, and enables a scale-aware training and testing with 3D information. Extensive experiments show that the proposed CoLA strategy largely outperforms baseline methods on YCB-Video dataset and our proposed Supermarket-10K dataset. Code, trained model and new dataset will be published with this paper.
Entropy counting from a Schwarzschild/CFT correspondence and soft hair

We revisit Carlip's approach to entropy counting. This analysis reemerged in a recently obtained Schwarzschild/CFT-correspondence as Sugawara-construction of a 2D stress-tensor. Here, for the example of a Schwarzschild black hole, we show how to single out diffeomorphisms forming in contrast to Carlip's analysis the full 2D local conformal algebra. We provide arguments, why their Hamiltonian generators are expected to be the symmetry generators of a possible conformal field theory describing the part of phase space responsible for black hole microstates. Then, we can infer central charges and temperatures of this CFT by inspecting the algebra of these Hamiltonian generators. Using this data in the Cardy-formula, precise agreement with the Bekenstein-Hawking entropy is found. Alternatively, we obtain the same CFT temperatures by thermodynamic considerations. Altogether, this suggests that the Hamiltonian generators need no corrections through possible non-canonical counterterms. We comment on the related recent work by Haco, Hawking, Perry, Strominger.
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control

We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. Rather than designing shaped reward functions, ACGD adaptively sets the appropriate task difficulty for the learner by controlling where to sample from the demonstration trajectories and which set of simulation parameters to use. We show that training vision-based control policies in simulation while gradually increasing the difficulty of the task via ACGD improves the policy transfer to the real world. The degree of domain randomization is also gradually increased through the task difficulty. We demonstrate zero-shot transfer for two real-world manipulation tasks: pick-and-stow and block stacking. A video showing the results can be found at https://lmb.informatik. uni-freiburg.de/projects/curriculum/
Bayesian Analysis of Multifidelity Computer Models With Local Features and Nonnested Experimental Designs: Application to the WRF Model

ABSTRACT Motivated by a multi-fidelity Weather Research and Forecasting (WRF) climate model application where the available simulations are not generated based on hierarchically nested experimental design, we develop a new co-kriging procedure called augmented Bayesian treed co-kriging. The proposed procedure extends the scope of co-kriging in two major ways. We introduce a binary treed partition latent process in the multifidelity setting to account for nonstationary and potential discontinuities in the model outputs at different fidelity levels. Moreover, we introduce an efficient imputation mechanism which allows the practical implementation of co-kriging when the experimental design is nonhierarchically nested by enabling the specification of semiconjugate priors. Our imputation strategy allows the design of an efficient reversible jump Markov chain Monte Carlo implementation that involves collapsed blocks and direct simulation from conditional distributions. We develop the Monte Carlo recursive emulator which provides a Monte Carlo proxy for the full predictive distribution of the model output at each fidelity level, in a computationally feasible manner. The performance of our method is demonstrated on benchmark examples and used for the analysis of a large-scale climate modeling application which involves the WRF model.
Could bow-shaped magnetic morphologies surround filamentary molecular clouds?

Context. A new method based on Faraday rotation measurements recently found the line-of-sight component of magnetic fields in Orion-A and showed that their direction changes from the eastern side of this filamentary structure to its western side. Three possible magnetic field morphologies that can explain this reversal across the Orion-A region are toroidal, helical, and bow-shaped morphologies. Aims. In this paper, we constructed simple models to represent these three morphologies and compared them with the available observational data to find the most probable morphology(ies). Methods. We compared the observations with the models and used probability values and a Monte Carlo analysis to determine the most likely magnetic field morphology among these three morphologies. Results. We found that the bow morphology had the highest probability values, and that our Monte-Carlo analysis suggested that the bow morphology was more likely. Conclusions. We suggest that the bow morphology is the most likely and the most natural of the three morphologies that could explain a magnetic field reversal across the Orion-A filamentary structure (i.e., bow, helical and toroidal morphologies).
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls

Large-scale machine learning training suffers from two prior challenges, specifically for nuclear-norm constrained problems with distributed systems: the synchronization slowdown due to the straggling workers, and high communication costs. In this work, we propose an asynchronous Stochastic Frank Wolfe (SFW-asyn) method, which, for the first time, solves the two problems simultaneously, while successfully maintaining the same convergence rate as the vanilla SFW. We implement our algorithm in python (with MPI) to run on Amazon EC2, and demonstrate that SFW-asyn yields speed-ups almost linear to the number of machines compared to the vanilla SFW.
Robust Planning and Control For Polygonal Environments via Linear Programming

In this paper, we are concerned with the design of a set of controllers, on a cell decomposition of a polygonal environment through Linear Programming. The core of our proposed method consists of a convex min-max formulation that synthesizes an output-feedback controller, based on relative displacement measurements with respect to a set of landmarks. The optimization problem is formulated using piece-wise linear Control Lyapunov Function and Control Barrier Function constraints, to provide guarantees of stability and safety. The inner maximization problem ensures that these constraints are met by all the points in each cell, while the outer minimization problem balances the different constraints to optimize robustness. We convert this min-max optimization problem to a regular Linear Programming problem, by forming the dual of the inner maximization problem. Although in principle our approach is applicable to any system with piecewise linear dynamics, in this paper as a proof of concept, we apply it to first and second order integrators. We show through simulations that the resulting controllers are robust to significant deformations of the environment.
A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method

Recently algorithms incorporating second order curvature information have become popular in training neural networks. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to effectively accelerate the BFGS quasi-Newton method by incorporating the momentum term and Nesterov's accelerated gradient vector. A stochastic version of NAQ method was proposed for training of large-scale problems. However, this method incurs high stochastic variance noise. This paper proposes a stochastic variance reduced Nesterov's Accelerated Quasi-Newton method in full (SVR-NAQ) and limited (SVRLNAQ) memory forms. The performance of the proposed method is evaluated in Tensorflow on four benchmark problems - two regression and two classification problems respectively. The results show improved performance compared to conventional methods.
Service Wrapper: a system for converting web data into web services

Web services are widely used in many areas via callable APIs, however, data are not always available in this way. We always need to get some data from web pages whose structure is not in order. Many developers use web data extraction methods to generate wrappers to get useful contents from websites and convert them into well-structured files. These methods, however, are designed specifically for professional wrapper program developers and not friendly to users without expertise in this domain. In this work, we construct a service wrapper system to convert available data in web pages into web services. Additionally, a set of algorithms are introduced to solve problems in the whole conversion process. People can use our system to convert web data into web services with fool-style operations and invoke these services by one simple step, which greatly expands the use of web data. Our cases show the ease of use, high availability, and stability of our system.
Measuring the net circular polarization of the stochastic gravitational wave background with interferometers

Parity violating interactions in the early Universe can source a stochastic gravitational wave background (SGWB) with a net circular polarization. In this paper, we study possible ways to search for circular polarization of the SGWB with interferometers. Planar detectors are unable to measure the net circular polarization of an isotropic SGWB . We discuss the possibility of using the dipolar anisotropy kinematically induced by the motion of the solar system with respect to the cosmic reference frame to measure the net circular polarization of the SGWB with planar detectors. We apply this approach to LISA, re-assessing previous analyses by means of a more detailed computation and using the most recent instrument specifications, and to the Einstein Telescope (ET), estimating for the first time its sensitivity to circular polarization. We find that both LISA and ET, despite operating at different frequencies, could detect net circular polarization with a signal-to-noise ratio of order one in a SGWB with amplitude h2 ΩGW ≃ 10−11. We also investigate the case of a network of ground based detectors. We present fully analytical, covariant formulas for the detector overlap functions in the presence of circular polarization. Our formulas do not rely on particular choices of reference frame, and can be applied to interferometers with arbitrary angles among their arms.
PFirewall: Semantics-Aware Customizable Data Flow Control for Home Automation Systems

Emerging Internet of Thing (IoT) platforms provide a convenient solution for integrating heterogeneous IoT devices and deploying home automation applications. However, serious privacy threats arise as device data now flow out to the IoT platforms, which may be subject to various attacks. We observe two privacy-unfriendly practices in emerging home automation systems: first, the majority of data flowed to the platform are superfluous in the sense that they do not trigger any home automation; second, home owners currently have nearly zero control over their data. We present PFirewall, a customizable data-flow control system to enhance user privacy. PFirewall analyzes the automation apps to extract their semantics, which are automatically transformed into data-minimization policies; these policies only send minimized data flows to the platform for app execution, such that the ability of attackers to infer user privacy is significantly impaired. In addition, PFirewall provides capabilities and interfaces for users to define and enforce customizable policies based on individual privacy preferences. PFirewall adopts an elegant man-in-the-middle design, transparently executing data minimization and user-defined policies to process raw data flows and mediating the processed data between IoT devices and the platform (via the hub), without requiring modifications of the platform or IoT devices. We implement PFirewall to work with two popular platforms: SmartThings and openHAB, and set up two real-world testbeds to evaluate its performance. The evaluation results show that PFirewall is very effective: it reduces IoT data sent to the platform by 97% and enforces user defined policies successfully.
Features for Ground Texture Based Localization - A Survey

Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization, while SIFT stands out when considering severe synthetic transformations as they might occur during absolute localization.
Mapping Class Groups and Lefschetz Fibrations

The connections between symplectic 4-manifolds, Lefschetz fibrations, and mapping class groups allow the use of symplectic techniques to prove statements about mapping class groups of surfaces, by constraining the set of allowable monodromies. In this paper we set out to prove these statements more directly. For planar surfaces, we exhibit an elementary proof that any factorization of a boundary monodromy obtained by a sequence of positive stabilizations of the identity into positive Dehn twists must have the same number of factors. Further, motivated by Smith’s use of hyperbolic methods to show that there are no factorizations of the identity for surfaces with boundary [Sm], we demonstrate that a negative stabilization can never be factorized as a product of positive Dehn twists. 1