Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Consistency in Multi-Cloud Storage Systems With Passive Servers and Non-Communicating Clients

Multi-cloud storage systems are becoming more popular due to the ever-expanding amount of consumer data. This growth is accompanied by increasing concerns regarding security, privacy, and reliability of cloud storage solutions. Ensuring data consistency in such systems is especially challenging due to their architecture and characteristics. Specifically, passive cloud storage cannot run coordination software, and clients cannot communicate directly to reach consensus. Furthermore, the atomicity of operations is not always guaranteed by the clouds’ public APIs. In this paper, we formally define data consistency in multi-cloud storage systems, identify how they can be violated, and introduce a new method that provably maintains the data consistency in these systems. The implementation and experiments show that the proposed method can maintain data consistency with a certain delay in data uploading and that it is scalable with respect to the number of used clouds as well as the number of users. Integrating this method into multi-cloud storage systems will enhance their usability and reliability.
Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors

Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizens. In this article, we present an IoT pipeline for acquisition, processing, and visualization of air pollution data over the city of Antwerp, Belgium. Our system employs IoT devices mounted on vehicles as well as static reference stations to measure a variety of city parameters, such as humidity, temperature, and air pollution. Mobile measurements cover a larger area compared to static stations; however, there is a tradeoff between temporal and spatial resolution. We address this problem as a matrix completion on graphs problem and rely on variational graph autoencoders to propose a deep learning solution for the estimation of the unknown air pollution values. Our model is extended to capture the correlation among different air pollutants, leading to improved estimation. We conduct experiments at different spatial and temporal resolution and compare with state-of-the-art methods to show the efficiency of our approach. The observed and estimated air pollution values can be accessed by interested users through a Web visualization tool designed to provide an air pollution map of the city of Antwerp.
Siamese-Like Convolutional Neural Network for Fine-Grained Income Estimation of Developed Economies

Estimating the per-capita income and the household income at a fine-grained geographical scale is critical but challenging, even across the developed economies. In this article, a novel Siamese-like Convolutional Neural Network, integrating Ridge Regression and Gaussian Process Regression, has been developed for fine-grained estimation of income across different parts of New York City. Our model (the GP-Mixed-Siamese-like-Double-Ridge model) makes good use of the pairwise comparison of location-based house price information, daytime satellite image, street view and spatial location information as the inputs. Taking the per-capita income and the median household income in New York City as the ground truths, our model outperforms (R2 = 0.72–0.86 for five-fold validation) other state-of-the-art income estimation models and achieves good performance in cross-district and cross-scale validation. We also find that models which partially share our model architecture, including the Spatial-Information-GP and the Mixed-Siamese-like model, perform well under certain spatial granularity and data availability. Since such models rely on less data input types and simpler architectures, they can be used to save resources on data collection and model training. Hence, using our model for fine-grained income estimation does not mean excluding these models that share similar architectures. Our fine-grained income estimation model can allow the per-capita and the household income data generated in fine-grained resolution to couple with other types of data, such as the air pollution or the epidemic data, of the same scale, to ensure that any location-specific socio-economic-related study and evidence-based decision-making at the fine-grained resolution can be conducted. Future research will focus on extending our model for fine-grained income estimation in developing metropolises, and for developing other socio-economic indicators.
A Joint Photovoltaic-Dependent Navigation Routing and Energy Storage System Sizing Scheme for More Efficient All-Electric Ships

With the extensive electrification introduced into the shipboard power systems, the navigation routine has become more important in an electric propulsion and solar power integrated ships since various sailing paths and speeds will lead to different operation performances. Unlike traditional navigation, which solves the shortest path problem, a data-driven optimization scheme is developed in this article for a photovoltaic (PV)-dependent navigation routing. In order to minimize the total cost and greenhouse gas emissions of an all-electric ship (AES), a new coordinated optimization framework is proposed to jointly optimize the energy storage system (ESS) sizing and voyage scheduling while considering solar power variations. Several cases are compared to verify the proposed method on a cargo ship, and numeral results reveal that the deep-learning-based forecasting method can better characterize the onboard solar power variations, and the proposed joint optimization framework can well accommodate the onboard PV generation.
Estimating an LPV Model of Driver Neuromuscular Admittance Using Grip Force as Scheduling Variable

Humans can rapidly change their low-frequency arm dynamics to resist forces or give way to them. Quantifying driver time-varying arm dynamics is important to develop steer-by-wire and haptic support systems. Conventional linear time-invariant (LTI) identification, and even time-varying techniques such as wavelets, fail to capture fast changing dynamics. Moreover, such techniques require perturbation signals on the steering wheel (SW), which may affect steering feel and control behavior. We propose a novel two-step method to estimate time-varying driver admittance, using unobtrusive grip-force measurements of the hands on the wheel to schedule a linear parameter-varying (LPV) model that captures the full admittance range. A total of 18 subjects participated in two experiments in a simulator with an actuated SW. In a sensorimotor control experiment, we first establish the grip force and admittance relationship, requiring subjects to perform a boundary tracking task where perturbations on the wheel enabled local LTI identification. Six boundary widths is used to evoke admittance changes, after which a global LPV model is obtained through interpolation between the local models. Results show an inverse relationship between grip force and admittance and that the LPV model accurately captures the admittance settings (fit percentage $>90\%$). Second, a driving experiment is followed that aims to evoke differences in grip force and admittance in response to varying road widths, offering more realistic data to evaluate the LPV model predictions. Results show that the LPV model accurately describes adaptations in admittance to road width. Our method allows for online estimation of time-varying admittance during driving, without applying force perturbations
Evaluating Machine Learning Models for Disparate Computer Systems Performance Prediction

Performance prediction is an active area of research due to its applicability in the advancements of hardware-software co-development. Several empirical machine-learning models, such as linear models, non-linear models, probabilistic models, tree-based models and, neural networks, are used for performance prediction. Furthermore, the prediction model’s accuracy may vary depending on performance data gathered for different software types (compute-bound, memory-bound) and different hardware (simulation-based or physical systems). We have examined fourteen machine-learning models on simulation-based hardware and physical systems by executing several benchmark programs with different computation and data access patterns. Our results show that the tree-based machine-learning models outperform all other models with median absolute percentage error (MedAPE) of less than 5% followed by bagging and boosting models that help to improve weak learners. We have also observed that prediction accuracy is higher on simulation-based hardware due to its deterministic nature as compared to physical systems. Moreover, in physical systems, the prediction accuracy of memory-bound algorithms is higher as compared to compute-bound algorithms due to manufacturer variability in processors.
An Optimal Power Allocation and Relay Selection Full-Duplex Store-Carry-Forward Scheme for Intermittently Connected Vehicular Networks

We consider intermittently connected vehicular networks (ICVNs) in which base stations (BSs) are installed along a highway to connect moving vehicles with internet. Due to the deployment cost, it is hard to cover the entire highway with BSs. To minimize the outage time in the uncovered area (UA), several store-carry-forward (SCF) schemes have been proposed in which a vehicle is selected to act as a relay by buffering data to be relayed to a target vehicle in the UA. In this paper, we propose a full-duplex (FD) SCF scheme that exploits the relay’s ability to simultaneously receive and transmit in order to improve the effective communication time (ECT) with the target vehicle and accordingly deliver more data to it in the UA. The optimal power allocation (PA) that maximizes the ergodic capacities of the links is determined and the amount of data that can be buffered inside the BS coverage and can be delivered by each relay candidate to the target vehicle in the UA is found. Since the relay cannot deliver more data in the UA than what can be buffered inside the coverage, the performance of each relay candidate is limited by the minimum of the two amounts and hence is used as a relay selection (RS) criteria. To reduce the computational complexity, an alternative RS scheme that selects the relay candidate that offers the highest ECT is proposed. As compared to half-duplex SCF schemes, simulation results show that the proposed FD schemes are capable of delivering significantly higher amount of data to the target vehicle in the UA.
Towards an adaptive reference model for agile and hybrid frameworks in automotive development

While the German car industry was very successful over the past few decades, new challenges have surfaced in recent years. Societal changes, technological innovation and sustainability requirements call for improvements in flexibility and speed during the automotive development process. Traditionally, plan-based frameworks like the waterfall or Vee-model are used by the automotive industry. However, these frameworks do not work well for all suppliers that partake in the automotive product development process. An agile or hybrid framework may offer a better alternative for specific suppliers. To create an innovative, digital and adaptive product development process, this research aims at the integration of the whole value chain. This includes the requirements of the OEM (original equipment manufacturer) and their suppliers. To allow for simple usage and adaptation, the knowledge is processed into a digital reference model. This generic reference model is then used to derive specific development processes that consider the context of each partner. By integrating communication mechanisms, it ensures that all partners can use their preferred framework for their development process while still ensuring a consistent product. In order to be able to select the most appropriate framework according to the context of a specific partner, the reference model takes into consideration all contextual factors of the development process.
On the Identifiability of Transform Learning for Non-Negative Matrix Factorization

Non-negative matrix factorization with transform learning (TL-NMF) aims at estimating a short-time orthogonal transform that projects temporal data into a domain that is more amenable to NMF than off-the-shelf time-frequency transforms. In this work, we study the identifiability of TL-NMF under the Gaussian composite model. We prove that one can uniquely identify row-spaces of the orthogonal transform by optimizing the likelihood function of the model. This result is illustrated on a toy source separation problem which demonstrates the ability of TL-NMF to learn a suitable orthogonal basis.
Analysis of MTJ Based Ternary Content Addressable Memory With and Without Match-Line Pre-Charge

The intent of this paper is to analyze the working of the Magnetic Tunnel Junction (MTJ) based Ternary Content Addressable Memory (TCAM) with and without the match-line (ML) pre-charge. The designs of the memory with MTJs that have been implemented by previous researchers have made use of ML pre-charge. In this paper, for performance comparison, three different TCAM cells have been implemented - one with ML pre-charge (PC) and the other two, namely Self-Controlled Pre-charge free (SCPF) and Hybrid SCPF (HSCPF), without ML pre-charge. The designs were simulated on Cadence Virtuoso design tool using CMOS 180 nm technology with a supply voltage of 3 V. Arrays of different sizes for all three circuits were simulated and compared with respect to delay, area and power. The study revealed that, for an 8X16 array, the delay for SCPF and HSCPF is 79.77% and 25013.5% more than that of PC circuit, area occupied is 98.65% and 38.04% more than that of PC Circuit. Power consumed for SCPF and HSCPF arrays is 125.38% and 7.5% more than that of PC Circuit. This is in contrast to the Static Random Access Memory (SRAM) based TCAM circuits where power and delay for SCPF and HSCPF circuits is lesser than that of PC circuit.
Modeling Photocathode Performance Using MedeA-VASP Simulation Software

Photocathodes have a wide range of applications, and different materials can be used depending on the incident photon wavelength. The structure and composition of a crystalline material can strongly affect the electronic properties such as the electron affinity and the work function. Molecular beam epitaxy (MBE) provides much improved control over the composition and synthesis of a photocathode versus traditional manufacturing methods, which allows these electrical and optical properties to be optimized for a particular application. In this article, we use density functional theory (DFT) through commercially available MedeA-VASP simulation software to identify the available parameter space of electron affinity and absorption coefficient by varying the stoichiometry and crystallinity. We identify trends to motivate the manufacture of new dicesium telluride- and gallium arsenide-like materials via MBE to optimize photocathode performance for a particular application.
Sequence Imputation using Machine Learning with Early Stopping Mechanism

In today’s world, humans need data from every situation, even from conditions where humans cannot directly extract data or from places that are hazardous for human beings. Wireless sensor networks or WSNs can solve this problem. They gather the required information using their sensors and provide the human race with the necessary data. Missing data are unavoidable in WSN, due to issues such as network communication outage, sensor maintenance or failure, etc. A complete informed and accurate data can help make the right decision. Just getting the missing data is not essential; it is also crucial to get this information as quickly as possible. A sequence imputation model with an encoder-decoder model is used to identify the missing values in the sensor network. It uses SSIM, with LSTM, attention layer and an early stopping layer, which gives comparable results in the minimum time by taking into consideration the past and the future information. The training and testing data is the subset of the hourly dataset of PM2.5 data of the US Embassy in Beijing. This model was able to produce accurate values keeping in mind the time.
Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods

To improve the accuracy of rainfall estimation by microwave links, this article presents a method for classifying wet and dry periods based on the support vector machine (SVM). The average, minimum, and maximum attenuation measurements in 5 min are applied as the feature vector of the SVM after the analysis of the relation between the statistical parameters of the attenuation measurements from seven microwave links and the wet/dry periods. When the baseline attenuation is needed for retrieving the path-averaged rain rate, the method can classify the wet/dry periods and estimate a dynamic baseline with an optimal combination of the statistical parameters of the attenuation measurements based on the prior training. Experiments are conducted to test the classification method. The results show that the classification accuracy is higher than 0.8, which is a satisfactory result. Most values of the true positive rate are higher than 0.9, which indicates that the method can correctly classify most of the wet periods. Additionally, the values of the false positive rate are less than 0.3, and most of the values are less than 0.2, suggesting that the method incorrectly classifies the dry period as the wet period with a low probability. The results demonstrate that the classification method is capable of classifying the wet and dry periods with a high accuracy, which can help improve the precision of the baseline of microwave links and rainfall estimation.
Flexible and Printed Microwave Plasmonic Sensor for Noninvasive Measurement

The printed electrodes for detecting direct current signal changes of human vital signs have been fully investigated. Here a flexible and printed microwave plasmonic sensor for detecting liquid solutions is proposed and demonstrated. The sensor for noninvasive measurement at microwave frequencies is based on the spoof localized surface plasmons resonator, which is composed of a metal corrugated ring fabricated on the flexible PET substrate using the inkjet printing technology. The winding-shaped polydimethylsiloxane (PDMS) microfluidic channel is interleaved with the corrugated ring to simulate the complicated blood vessel. The simulated results agree well with the experimental measurements. It shows that the measured resonance frequency offset of 147 MHz has been achieved when the microfluidic channel is filled with deionized water, which indicates that the flexible microwave biological sensor is feasible. The calculated figure of merit is as high as 1178. The sensor can find wider applications in the flexible and wearable device field for continuous health monitoring.
A Hierarchical Framework for Collaborative Probabilistic Semantic Mapping

Performing collaborative semantic mapping is a critical challenge for cooperative robots to maintain a comprehensive contextual understanding of the surroundings. Most of the existing work either focus on single robot semantic mapping or collaborative geometry mapping. In this paper, a novel hierarchical collaborative probabilistic semantic mapping framework is proposed, where the problem is formulated in a distributed setting. The key novelty of this work is the mathematical modeling of the overall collaborative semantic mapping problem and the derivation of its probability decomposition. In the single robot level, the semantic point cloud is obtained based on heterogeneous sensor fusion model and is used to generate local semantic maps. Since the voxel correspondence is unknown in collaborative robots level, an Expectation-Maximization approach is proposed to estimate the hidden data association, where Bayesian rule is applied to perform semantic and occupancy probability update. The experimental results show the high quality global semantic map, demonstrating the accuracy and utility of 3D semantic map fusion algorithm in real missions.
Stable Control in Climbing and Descending Flight under Upper Walls using Ceiling Effect Model based on Aerodynamics

Stable flight control under ceilings is difficult for multirotor Unmanned Aerial Vehicles (UAVs). The wake interaction between rotors and upper walls, called the "ceiling effect", causes an increase of rotor thrust. As a result of the thrust increase, multi-rotors are drawn upward abruptly and collide with ceilings. In previous work, several thrust models of the ceiling effect have been proposed for stable flight under ceilings, assuming that the airflow around rotors is in steady states. However, the airflow around rotors in vertical flight is not in steady states and each thrust model in previous work is skillfully determined based on large amounts of precise experimental data. In this paper, we introduce an aerodynamics-based thrust model and a stable control method under ceilings. This model is derived from the momentum theory and the relationship between vertical climbing/descending rates of rotors and an induced velocity. To confirm our proposed model, we collect thrust data at various vertical rates in flight. In addition, we use only onboard sensors to estimate selfstate for structural inspections. Consequently, we reveal that the proposed model is consistent with the experimental results. Based on an aerodynamic model, we need not collect large amounts of precise experimental data to realize stable flight. Furthermore, the vertical flight tests under ceilings demonstrate that our in-unsteady-state-model-based controller outperforms the conventional steady-state ones.
Self-Supervised Learning for Alignment of Objects and Sound

The sound source separation problem has many useful applications in the field of robotics, such as human-robot interaction, scene understanding, etc. However, it remains a very challenging problem. In this paper, we utilize both visual and audio information of videos to perform the sound source separation task. A self-supervised learning framework is proposed to implement the object detection and sound separation modules simultaneously. Such an approach is designed to better find the alignment between the detected objects and separated sound components. Our experiments, conducted on both the synthetic and real datasets, validate this approach and demonstrate the effectiveness of the proposed model in the task of object and sound alignment.