Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Device Tracking: Systematic Method to Detect and Measure CDT

Online advertising, the backbone of the free Web, has transformed the marketing business by creating countless opportunities for advertisers to reach potential customers. The advertising ecosystem exists upon a complex infrastructure composed by intermediate entities and technologies whose main goal is to deliver personalized ads to such customers. In recent years, however, advertisers have started to develop new advanced techniques such as Cross-Device Tracking, to detect and track the user's activity across multiple devices and target them on the all possible device. For this method to work, a variety of user data is collected, aggregated, processed and traded behind the scenes, and often times, without the user-data owner's informed consent. Therefore, despite the enormous value of online advertising and support it offers to the Web, the prevalence and intensity of these tracking and targeting practices prompt serious concerns for the online users' privacy. In this paper, we propose a novel methodology for systematically investigating Cross-Device Tracking and various components affecting its targeting performance. We materialize our methodology into a platform that is able to perform small and large scale automated measurements, under different experimental scenarios, emulated users and settings. By conducting a variety of measurements with our framework, we are able to detect and measure Cross-Device Tracking with average accuracy of 78-96%, and also report significant insights about its internal mechanisms and its impact on online user's privacy.
Shortcuts to Thermodynamic Computing: The Cost of Fast and Faithful Erasure

Author(s): Boyd, AB; Patra, A; Jarzynski, C; Crutchfield, JP | Abstract: Landauer's Principle states that the energy cost of information processing must exceed the product of the temperature and the change in Shannon entropy of the information-bearing degrees of freedom. However, this lower bound is achievable only for quasistatic, near-equilibrium computations -- that is, only over infinite time. In practice, information processing takes place in finite time, resulting in dissipation and potentially unreliable logical outcomes. For overdamped Langevin dynamics, we show that counterdiabatic potentials can be crafted to guide systems rapidly and accurately along desired computational paths, providing shortcuts that allows for the precise design of finite-time computations. Such shortcuts require additional work, beyond Landauer's bound, that is irretrievably dissipated into the environment. We show that this dissipated work is proportional to the computation rate as well as the square of the information-storing system's length scale. As a paradigmatic example, we design shortcuts to erase a bit of information metastably stored in a double-well potential. Though dissipated work generally increases with erasure fidelity, we show that it is possible perform perfect erasure in finite time with finite work. We also show that the robustness of information storage affects the energetic cost of erasure---specifically, the dissipated work scales as the information lifetime of the bistable system. Our analysis exposes a rich and nuanced relationship between work, speed, size of the information-bearing degrees of freedom, storage robustness, and the difference between initial and final informational statistics.
State representation learning with recurrent capsule networks

Unsupervised learning of compact and relevant state representations has beenproved very useful at solving complex reinforcement learning tasks Ha and Schmid-huber (2018). In this paper, we propose a recurrent capsule network Hinton et al.(2011) that learns such representations by trying to predict the future observationsin an agent’s trajectory
VASS reachability in three steps

This note is a product of digestion of the famous proof of decidability of the reachability problem for vector addition systems with states (VASS), as first established by Mayr in 1981 and then simplified by Kosaraju in 1982. The note is neither intended to be rigorously formal nor complete; it is rather intended to be an intuitive but precise enough description of main concepts exploited in the proof. Very roughly, the overall idea is to provide a decidable condition Theta on a VASS such that Theta implies reachability and its negation implies that the size of VASS can be reduced. With these two properties, the size of input can be incrementally reduced until the problem becomes trivial. We proceed in three steps: we first formulate the condition Theta for plain VASS, then adapt it to more general VASS with unconstrained coordinates, and finally to generalized VASS of Kosaraju.
Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction

Multiple players are each given one independent sample, about which they can only provide limited information to a central referee. Each player is allowed to describe its observed sample to the referee using a channel from a family of channels $\mathcal {W}$ , which can be instantiated to capture, among others, both the communication- and privacy-constrained settings. The referee uses the players’ messages to solve an inference problem on the unknown distribution that generated the samples. We derive lower bounds for the sample complexity of learning and testing discrete distributions in this information-constrained setting. Underlying our bounds is a characterization of the contraction in chi-square distance between the observed distributions of the samples when information constraints are placed. This contraction is captured in a local neighborhood in terms of chi-square and decoupled chi-square fluctuations of a given channel, two quantities we introduce. The former captures the average distance between distributions of channel output for two product distributions on the input, and the latter for a product distribution and a mixture of product distribution on the input. Our bounds are tight for both public- and private-coin protocols. Interestingly, the sample complexity of testing is order-wise higher when restricted to private-coin protocols.
Soft-Autoencoder and Its Wavelet Shrinkage Interpretation

Deep learning is a main focus of artificial intelligence and has greatly impacted other fields. However, deep learning is often criticized for its lack of interpretation. As a successful unsupervised model in deep learning, various autoencoders, especially convolutional autoencoders, are very popular and important. Since these autoencoders need improvements and insights, in this paper we shed light on the nonlinearity of a deep convolutional autoencoder in perspective of perfect signal recovery. In particular, we propose a new type of convolutional autoencoders, termed as Soft-Autoencoder (Soft-AE), in which the activations of encoding layers are implemented with adaptable soft-thresholding units while decoding layers are realized with linear units. Consequently, Soft-AE can be naturally interpreted as a learned cascaded wavelet shrinkage system. Our denoising numerical experiments on CIFAR-10, BSD-300 and Mayo Clinical Challenge Dataset demonstrate that Soft-AE gives a competitive performance relative to its counterparts.
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting

Access to a large variety of data across a massive population has made it possible to predict customer purchase patterns and responses to marketing campaigns. In particular, accurate demand forecasts for popular products with frequent repeat purchases are essential since these products are one of the main drivers of profits. However, buyer purchase patterns are extremely diverse and sparse on a per-product level due to population heterogeneity as well as dependence in purchase patterns across product categories. Traditional methods in survival analysis have proven effective in dealing with censored data by assuming parametric distributions on inter-arrival times. Distributional parameters are then fitted, typically in a regression framework. On the other hand, neural-network based models take a non-parametric approach to learn relations from a larger functional class. However, the lack of distributional assumptions make it difficult to model partially observed data. In this paper, we model directly the inter-arrival times as well as the partially observed information at each time step in a survival-based approach using Recurrent Neural Networks (RNN) to model purchase times jointly over several products. Instead of predicting a point estimate for inter-arrival times, the RNN outputs parameters that define a distributional estimate. The loss function is the negative log-likelihood of these parameters given partially observed data. This approach allows one to leverage both fully observed data as well as partial information. By externalizing the censoring problem through a log-likelihood loss function, we show that substantial improvements over state-of-the-art machine learning methods can be achieved. We present experimental results based on two open datasets as well as a study on a real dataset from a large retailer.
Whittemore: An embedded domain specific language for causal programming

This paper introduces Whittemore, a language for causal programming. Causal programming is based on the theory of structural causal models and consists of two primary operations: identification, which finds formulas that compute causal queries, and estimation, which applies formulas to transform probability distributions to other probability distribution. Causal programming provides abstractions to declare models, queries, and distributions with syntax similar to standard mathematical notation, and conducts rigorous causal inference, without requiring detailed knowledge of the underlying algorithms. Examples of causal inference with real data are provided, along with discussion of the implementation and possibilities for future extension.
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models

Latent representations are the essence of deep generative models and determine their usefulness and power. For latent representations to be useful as generative concept representations, their latent space must support latent space interpolation, attribute vectors and concept vectors, among other things. We investigate and discuss latent variable modeling, including latent variable models, latent representations and latent spaces, particularly hierarchical latent representations and latent space vectors and geometry. Our focus is on that used in variational autoencoders and generative adversarial networks.
Predictive Modelling of In-Vehicle CO2 Concentration Using Sensor Data Analytics

In densely populated metropolitan cities, people spend an average of 45–150 minutes inside a vehicle commuting to their workplace. Most of these travelers often experiences short term health conditions like drowsiness, breathlessness, discomfort and headache. Many accidents have been reported due to drowsiness while driving. There has been considerable increase in the number of death/choking cases reported due to CO2buildup inside the vehicle. Even though Vehicle Indoor Air Quality is an important parameter, the general awareness about it is very low, and only few high end new models of automobiles have indoor air quality sensing systems, with limited capability. This paper attempts to use a sensing system that can be retrofitted in any old or new vehicle to measure the CO2level inside the vehicle and use this sensed data to perform statistical and stochastic analysis of the growth pattern of CO2inside the vehicle chamber.
Comparison of Loss Functions for Training of Deep Neural Networks in Shogi

Evaluation functions are crucial for building strong computer players in two-player games, such as chess, Go, and shogi. Although a linear combination of a large number of features has been popular representation of an evaluation function in shogi, deep neural networks (DNNs) are recently considered to be more promising by the success of AlphaZero in multiple domains, chess, Go, and shogi. This paper shows that three loss functions, loss in comparison training, temporal difference (TD) errors and cross entropy loss in win prediction, are effective for the training of evaluation functions in shogi, presented in deep neural networks. For the training of DNNs in AlphaZero, the main loss function only consists of win prediction, though it is augmented with move prediction for regularization. On the other hand, for training in traditional shogi programs, various losses including loss in comparison training, TD errors, and cross entropy loss in win prediction, have contributed to yield accurate evaluation functions which are the linear combination of a large number of features. Therefore, it is promising to combine these loss functions and to apply them to the training of modern DNNs. In our experiments, we show that training with combinations of loss functions improved the accuracy of evaluation functions represented by DNNs. The performance of trained evaluation functions is tested through top-1 accuracy, 1-1 accuracy, and self-play.
Securing Substations through Command Authentication Using On-the-fly Simulation of Power System Dynamics

There are increasing concerns that cyber attackers may inject malicious remote control commands into smart grid systems, as witnessed in the Ukraine incidents in 2015 and 2016 and the recent CrashOverride malware campaign. To counter such risks, command authentication mechanisms, which evaluate the legitimacy and validity of each remote control command based on the up-to-date power grid status and context, can be deployed near the edge of smart grid infrastructure (e.g., in substations) as an additional line of defense. However, many of the state-of the-art command authentication schemes only utilize steady-state power flow information, which does not capture all details of power grid behaviors in the transient state as well as cascading effects. Therefore, they may overlook indication of significant grid instability triggered by malicious commands. In this paper, we propose the use of on-the-fly power system dynamics simulation for command authentication to overcome such limitations. We also discuss system architecture and design considerations on longer simulation latency towards the practical deployment of the enhanced command authentication system.
Automatic Test Case Generation from Usage Information

Nowadays, web applications play an important role in our society and in the business world. Many companies earn a large part of their revenues through web applications that provide support services that must be maintained and improved over time. Most of these services operate on a large scale and are in constant change due to the environment in which they operate and due to the rapid technological evolution as we strive to improve our everyday lives. Due to this constantly changing environment, it is difficult to estimate the impact of changes; to maintain the software requirements documents updated; and to build and maintain a test suit for regression testing. Regression tests must be updated continually in order to test the current behavior after requested changes are implemented. REQAnalytics is a tool that aims to solve these problems. This paper presents REQAnalytics and an extension to generate test cases from the usage of a SaaS platform in order to diminish the effort in building and maintaining regression tests that are useful to check if the SaaS platform remains working as expected.
Active Thermal Control of SiC/Si Hybrid Switch

The SiC/Si hybrid switch has been reported as a natural next step moving forward for high voltage applications to address the high cost issue of SiC devices. However, the existing gate control strategies of the SiC/Si hybrid switch focus on decreasing the total power loss, which induces a serious concern of the SiC MOSFET’s overheating and reliability degradation in the hybrid switch. In order to solve these issues, a dynamic thermal controller is proposed for power switching devices inside the SiC/Si hybrid switch to prevent them from overheating and keep the thermal balance between the two switching devices simultaneously. The proposed control method utilizes the turn off delay time between the gate signal of the SiC MOSFET and Si IGBT to balance the junction temperature between the two devices and keep them from overheating. A 20 kHz DC/DC buck converter which is based on SiC/Si hybrid switch is developed and tested to validate the proposed active thermal control technique. Experimental results demonstrate that the SiC/Si hybrid switch with the proposed control method can accomplish a much lower junction temperature than that with the existing gate control mode in the same output power rating, and a larger output power capability in the DC/DC buck converter.
Decision Support System for Drivers & Passengers: Smartphone-Based Reference Model and Evaluation

Last years, decision support systems brought a lot of new possibilities for the people. Developing such technologies as context-aware recommendations, personification, cloud computing allows to support vehicle drivers and passengers in their trips. Decision support systems for vehicle drivers and passengers allows one to provide context-based personalized recommendations based on determined situation in the vehicle cabin and location region as well as driver preferences. For example, during the trip the driver can be notified about the interesting places. If the system determines the dangerous state it generates recommendations for the driver to predict the emergency situation. The paper presents a reference model of the decision support system, describe the dangerous state identification scheme, and discuss evaluation.
Electro chemically Synthesized Nanocomposites for MEMS Device Performance Enhancement

This paper presents graphical modeling and control of the quadcopter dynamics. A quadcopter is an under-actuated system with nonlinear dynamics. For stabilization of such complex system, it is necessary to have a nonlinear control. Therefore, a model-based control is developed for the altitude (height) and attitude (roll, pitch, and yaw) control of the quadcopter dynamics. The dynamic model of quadcopter is developed using a power-based graphical modeling tool, called bond graph. Subsequently, the bond graph model is used to develop altitude and attitude control of the quadcopter. Finally, the developed algorithm is validated through simulation.
Fractional-order time delay compensation in deadbeat control for power converters

Deadbeat control scheme is widely implemented in the control of power electronics and electrical drives, which is of simplification, rapidity and flexibility. However, owing to its sensitive to model uncertainties and unmodeled dynamics, the practical control performance is severely degraded and sometimes even unstable. Uncertain time delay is a typical case of model uncertainties, which severely deteriorates the control accuracy and dramatically reduce the system stability margin of deadbeat control. In this paper, the time delay effects on the control performance and system stability are investigated. A fractional-order Smith Predictor based solution is proposed to compensate arbitrary time delay with high accuracy, simple structure, and good robustness. The composite control scheme offers accurate time delay compensations in digital implementation and considerably enhances the robustness of the control system, which will effectively promote widespread applications of the deadbeat scheme. An application example of three-phase inverter system is explored to comprehensively illustrate the feasibility and effectiveness of the proposed scheme.
Evaluating an Open Learner Model Visualisation Prototype Tool with User eXperience Metrics

Learning management systems (LMS) are widely used in many educational environments. Although usability is widely acknowledged as a desired quality attribute, it has not been deeply investigated in the context of LMS systems where it has to align with pedagogy. In this research we evaluate extensions to an LMS designed to support the implementation of constructive alignment for technical units using a Task-Oriented Portfolio approach. The LMS application was extended with a range of open learner model-based visualisations to help staff and students monitor progress towards achieving unit intended learning outcomes throughout unit delivery. This paper reports on a case study of a formative usability testing of the prototype tool by using User eXperience (UX) metrics. The results unveil the potential usability issues and ways to improve the design of the prototype tool to better cater the actual target user needs.
Airbrushed Dipole RF Strain Sensor Antenna on a Stretchable Polyurethane Substrate

We present a novel dipole strain sensor antenna that has been fabricated on a highly stretchable polyurethane substrate by airbrushing a diluted commercially available ink PE872 in comparison with the screen printing using PE872 and CI-1036, both are commercially available silver inks. The frequency sensitivity with strain of sensors fabricated with airbrushing is 36.3 Mflz/strain” while that of sensors fabricated with screen printing is 31.2 Mflz/strain'zs. The sensitivity of the airbrushed strain sensor reduced by 4.1% after 1000 cycles of 5% strain. SEM images are used to characterize the microscopic morphology differences between the screen printed and airbrushed samples before and after the cyclic strain.
A New Black Box Evaluation Protocol for Biometric Systems

As a trending method for the authentication, biometrics tends to be integrated in various devices, and in particular in smartphones. If the evaluation is performed on operational device, the biometric sample and algorithm are not reachable by the assessors. So, these latter have to perform an evaluation on a system considered as a black box. This kind of evaluation implies numerous manual comparison. This paper proposes a methodology to perform an evaluation of biometric black boxes. Two preliminary experiments were performed in order to determine an optimized conduct. This paper describes the used methodology to perform evaluation on black boxes systems, and the results obtained on the systems under test.
Beam Aggregation for Instantaneous Link Recovery in Millimeter Wave Communications

Cellular networks at millimeter wave frequencies experience tremendous propagation impairments and channel fluctuations. One major challenge here is the aggregated path loss levels attributed to obstacles of various shapes and density in the propagation links, i.e., link blockage. Therefore, once the base and mobile stations establish the optimum beamforming and combining vectors during the initial beam access procedure in the control-plane, efficient link maintenance techniques are required to maintain communication sessions with sufficient signal levels at various blockage densities. Hence this paper presents a novel link recovery scheme based upon beam aggregation during link transitions from line-of-sight to non-line-of-sight (NLoS) environments. The proposed scheme utilizes circular antenna arrays and hybrid beamforming designs to achieve near-instantaneous recovery times without the need for repeated beam scanning. Simulation results show that the proposed recovery scheme exhibits significant improvements compared to alternative traditional methods in terms of signal-to-noise ratios and recovery times.