Miracle-dz/UniFAR-FTUs huggingface dataset:
Going through a window and landing a quadrotor using optical flow

This paper considers the problem of controlling a quadrotor to go through a window and land on a planar target, using an image-based controller, with only a camera and an Inertial Measurement Unit (IMU) as sensors. The maneuver is divided into two stages: crossing the window and landing on the target plane. For the first stage, a control law is proposed that guarantees that the vehicle will not collide with the wall containing the window and will go through the window with non-zero velocity along the direction orthogonal to the window, keeping at all times a safety distance with respect to the window edges. For the landing stage, the proposed control law ensures that the vehicle achieves a smooth touchdown, keeping at all time a positive height above the target plane. For control purposes, the centroids of the images of a collection of landmarks (corners) for both the window and the target are used as position measurement. The translational optical flow relative to the wall, window edges, and target plane is used as velocity cue. To achieve the proposed objective, no direct measurements of position or velocity are used and no explicit estimate of the height above the target plane or of the distance to the wall is required. Simulation results are provided to illustrate the performance of the proposed controller.
MixTrain: Scalable Training of Verifiably Robust Neural Networks

Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training and verifiably robust training, have limitations that restrict their practical applications. The adversarially robust training only makes the networks robust against a subclass of attackers and we reveal such weaknesses by developing a new attack based on interval gradients. By contrast, verifiably robust training provides protection against any L-p norm-bounded attacker but incurs orders of magnitude more computational and memory overhead than adversarially robust training. We propose two novel techniques, stochastic robust approximation and dynamic mixed training, to drastically improve the efficiency of verifiably robust training without sacrificing verified robustness. We leverage two critical insights: (1) instead of over the entire training set, sound over-approximations over randomly subsampled training data points are sufficient for efficiently guiding the robust training process; and (2) We observe that the test accuracy and verifiable robustness often conflict after certain training epochs. Therefore, we use a dynamic loss function to adaptively balance them for each epoch. We designed and implemented our techniques as part of MixTrain and evaluated it on six networks trained on three popular datasets including MNIST, CIFAR, and ImageNet-200. Our evaluations show that MixTrain can achieve up to $95.2\%$ verified robust accuracy against $L_\infty$ norm-bounded attackers while taking $15$ and $3$ times less training time than state-of-the-art verifiably robust training and adversarially robust training schemes, respectively. Furthermore, MixTrain easily scales to larger networks like the one trained on ImageNet-200, significantly outperforming the existing verifiably robust training methods.
Audit Cloud Adoption by German Audit Firms

A profound transition from manual to digital audit procedures entails extensive re-evaluation of requirements for audit firm organization, IT infrastructures, and expertise. Currently, many audit firms lack sufficient human and technical resources to master this transformation. Therefore, the audit domain is considering introducing a third party to the audit process, in which IT resources and skills are pooled. However, it is uncertain to what extent these so-called audit clouds can contribute to the future of auditing. So far, findings regarding audit cloud acceptance, a prerequisite for their establishment, could not be identified. To fill this research gap, this article describes the development of a research model based on the Unified Theory of Acceptance and Use of Technology. Due to a recent German law change regarding the recognition of legal certainty in cloud computing, the initial data collection is completed in Germany, with the goal of performing future studies in other jurisdictions.
Local Variability in PV Soiling Rate

We examine the correlation between monthly PV system soiling rates over a multi-year period within two groups of First Solar soiling stations in separate regions of California as a function of site separation. The results demonstrate large seasonal variations in monthly soiling rates, with seasonal patterns that differ between sites. Monthly patterns at nearby sites were more highly correlated, suggesting that soiling rates can be strongly affected by local geography and weather. For the two regions studied, soiling rate pattern correlation diminished beyond a distance on the order of 50 km.
Turning the Negative Affiliation Around: Looking Glass Self in Online Community Platforms

Numerous online communities have been established as the advancement of digital technologies. These online communities provide spaces where individuals can express what they like and thus being true to themselves. The expression of the true self is critical especially when such expression is socially sanctioned in the offline contexts (e.g., adults’ expression of love for Twilight products). This study focuses on factors (design related and non-design related) that contribute to the success of online communities (represented as online community belongingness) and enable expressing identity, particularly, socially problematic or less popular identities. We refer to such identities as “Negative Public Affiliation,” which is defined as the extent to which one does not want to openly express or admit having a particular interest or identification with a socially difficult or sanctioned idea, product, activities, or value systems. In this study, we aim to evaluate NPA’s impact on online community belongingness by focusing on people who have certain levels of identification with products that are socially exclusive to them. Our study will contribute to the online community literature by enhancing our understanding of negative public affiliation. This study also informs the designs of online community platforms.
Deep Pixel Probabilistic Model for Super Resolution Based on Human Visual Saliency Mechanism

This work explores super resolution (SR) with a deep network based on a pixel probabilistic model, where particular small inputs and large magnification factors make the problem highly underspecified since fairly large amounts of high-frequency details are missing in low resolution (LR) source. In this paper, we develop a deep architecture comprising of a PixelCNN and a residual network (ResNet), in which PixelCNN predicts the serial dependencies of the pixel sequence and ResNet for capturing the global structure of LR input. A human visual saliency mechanism (HVSM) by employing accurate SR in salient regions and fast interpolation in nonsalient regions is integrated within the pixel probabilistic model to efficiently reduce the computational complexity while maintaining the desired visual quality. Additionally, we present a Bayesian optimization technique to automatically determine the optimal weight of loss function. Furthermore, a modified image quality assessment taking into account HVSM is introduced, trying to align with the human visual perception. Experiments demonstrate that the proposed algorithm could generate more plausible facial features than previous deep learning methods, offering finer details and significant improvement in visual quality.
The Interplay of Presence and Online Learning in a Personalised Flipped Classroom

Blended learning, i.e. the combination of presence and online learning, promises to be the best of both worlds. The flipped classroom is a blended learning intervention explicitly focusing on personalised, interactive, and applied learning. However, the interplay between presence and online learning in the flipped classroom with respect to cognitive, affective, and behavioural learning outcomes remains unclear. Employing a mixed-methods approach consisting of structural equation modelling complemented by qualitative interviews, this study presents a first step to disentangle the interplay of presence and online learning. The study finds personalised learning in the presence realm to be positively related to the information quality of the course materials, while personalised learning in the online realm is positively related to the perceived usefulness of the virtual learning environment used. Whilst learner satisfaction can be increased in both worlds, no positive relationship is found regarding perceived and factual learning outcomes.
Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques

Nowadays, as internet speed has increased and the prices of mobile have decreased very much in past few years. Also the data prices too are very much affordable to most of the people. This has resulted into the digitization of most of the institutes as it is easy and convenient for the people and also for the authority to maintain the records. So, it resulted in most of the banks and other institutes receiving and transferring money through credit cards. But with the hackers and other cyber criminals around there is always chances of the frauds in the transactions. The possibility of the fraud transaction is very less but it is not negligible and even having one fraud transaction is unacceptable because it is crime and we can’t neglect it even if it is very less as it harms both the customer and credibility of the institute. So this paper aims at analyzing various classification techniques using various metrics for judging various classifiers. This model aims at improving fraud detection rather than misclassifying a genuine transaction as fraud.
Synaptic Functions of Hemichannels and Pannexons: A Double-Edged Sword

The classical view of synapses as the functional contact between presynaptic and postsynaptic neurons has been challenged in recent years by the emerging regulatory role of glial cells. Astrocytes, traditionally considered merely supportive elements are now recognized as active modulators of synaptic transmission and plasticity at the now so-called “tripartite synapse. ” In addition, an increasing body of evidence indicates that beyond immune functions microglia also participate in various processes aimed to shape synaptic plasticity. Release of neuroactive compounds of glial origin, -process known as gliotransmission-, constitute a widespread mechanism through which glial cells can either potentiate or reduce the synaptic strength. The prevailing vision states that gliotransmission depends on an intracellular Ca2+/exocytotic-mediated release; notwithstanding, growing evidence is pointing at hemichannels (connexons) and pannexin channels (pannexons) as alternative non-vesicular routes for gliotransmitters efflux. In concurrence with this novel concept, both hemichannels and pannexons are known to mediate the transfer of ions and signaling molecules -such as ATP and glutamate- between the cytoplasm and the extracellular milieu. Importantly, recent reports show that glial hemichannels and pannexons are capable to perceive synaptic activity and to respond to it through changes in their functional state. In this article, we will review the current information supporting the “double edge sword” role of hemichannels and pannexons in the function of central and peripheral synapses. At one end, available data support the idea that these channels are chief components of a feedback control mechanism through which gliotransmitters adjust the synaptic gain in either resting or stimulated conditions. At the other end, we will discuss how the excitotoxic release of gliotransmitters and [Ca2+]i overload linked to the opening of hemichannels/pannexons might impact cell function and survival in the nervous system.
Hierarchic Conv Nets Framework for Rare Sound Event Detection

In this paper, we propose a system for rare sound event detection using a hierarchical and multi-scaled approach based on Convolutional Neural Networks (CNN). The task consists on detection of event onsets from artificially generated mixtures. Spectral features are extracted from frames of the acoustic signals, then a first event detection stage operates as binary classifier at frame-rate and it proposes to the second stage contiguous blocks of frames which are assumed to contain a sound event. The second stage refines the event detection of the prior network, discarding blocks that contain background sounds wrongly classified by the first stage. Finally, the effective onset time of the active event is obtained. The performance of the algorithm has been assessed with the material provided for the second task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2017. The achieved overall error rate and F-measure, resulting respectively equal to 0.22 and 88.50 % on the evaluation dataset, significantly outperforms the challenge baseline and the system guarantees improved generalization performance with a reduced number of free network parameters w.r. t. other competitive algorithms.
Convolutional Neural Networks for Heart Sound Segmentation

In this paper, deep convolutional neural networks are used to segment heart sounds into their main components. The proposed method is based on the adoption of a novel deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. A further post-processing step is applied to the output of the proposed neural network, which induces the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). The proposed approach is tested on heart sound signals longer than 5 seconds from the publicly available PhysioNet dataset, and it is shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.4% and an average positive predictive value of 94.5% in detecting S1 and S2 sounds.
Energy Conservation Using an Optimized Topology With Distributed Processing of EEG Signals

Investigations in the field of wireless body area network reveals that Electroencephalogram (EEG) is the more concentrated area of research currently. In this regard, this paper works on reducing the power consumed by the electrodes in wireless EEG sensor network. Various existing topological structures such as tree topology, fully connected topology, near end and far end topology are used for electrode arrangements during signal acquisition. But the proposed hexagonally clustered topological structure is found to outperform compared to the other structures for node localization. Also, a comparison is made between the centralized and distributed method of processing on the acquired EEG signals. The proposed work follows distributed processing on the EEG datas where its combination with hexagonal clustering structure for node positioning reports better level of energy conservation compared to other methods as proven in the experimentation.
An Efficient Approximation Method for Delay-Rational Model of Multiconductor Transmission Lines

In this paper, a new algorithm for the generation of the approximate poles and residues of multiconductor transmission lines (TLs) characterized by frequency-dependent per-unit-length parameters is presented. By using the Green’s function-based delay-rational model for lossy TLs, the proposed algorithm can calculate a reduced number of rational functions to enhance computational efficiency. Also, it can further improve the efficiency by performing the approximation of poles and residues instead of solving high-order equations. The preservation of passivity proof is developed. The frequency response and transient analysis results are presented and compared to those of the delay-rational model. The outcomes show that the proposed method can significantly improve the speed while preserving a similar accuracy.
Finite-Time Consensus and Tracking Control of A Class of Nonlinear Multiagent Systems

In this technical note, we consider the finite-time consensus and tracking problems for a class of nonlinear multiagent systems with directed topology. By using the homogeneous function, we firstly give a finite-time consensus protocol for high-order linear multiagent systems. Then, we present the finite-time consensus and tracking protocols for strict feedback nonlinear multiagent systems. When state information is not available, the finite-time convergent observer is used to estimate each agent’ states, and we give the finite-time consensus and tracking protocols based on the finite-time observers.
Distributed Hybrid Secondary Control for a DC Microgrid via Discrete-Time Interaction

This paper studies the current sharing problem of a dc microgrid using the hybrid dynamic control method. The hybrid dynamic controller framework is established including a continuous-time part and a discrete-time part, where the former part eliminates the voltage deviation of the dc bus and the latter part ensures the current sharing accuracy of the dc microgrid. The proposed distributed hybrid secondary controller not only guarantees a high accuracy of current sharing but also maintains the voltage regulation at the dc bus. Different from most existing methods, it only utilizes the sampling output current information of neighbors at the discrete time instants, which greatly reduces the communication burden. Under the framework of stability analysis on the closed-loop system, the proposed hybrid dynamic controller achieves both current sharing and voltage regulation if the average interacted interval of the discrete time interaction satisfies a bounded constraint. Besides, a detailed parameter design of the controller is provided. Finally, simulation and experimental tests are presented to demonstrate the effectiveness of the proposed method.
A Novel Method to Estimate the Reaching Time of the Super-Twisting Algorithm

The super-twisting algorithm is a well-known technique in the field of sliding mode control or observation. In this contribution, an exact analytic expression for this algorithm's finite reaching time in the unperturbed case is derived. Based on this derivation, a novel estimation for the upper bound of the algorithm's reaching time the presence of perturbations is presented. The considered perturbations may be composed of additive components that are either Lipschitz continuous in time or Hölder continuous in the sliding variable. Both analytically and in the course of numerical examples the new strategy is shown to yield significant improvements compared to existing reaching time estimates.
What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection

This paper develops and evaluates a novel method that allows for the detection of affordances in a scalable and multiple-instance manner on visually recovered pointclouds. Our approach has many advantages over alternative methods, as it is based on highly parallelizable, one-shot learning that is fast in commodity hardware. The approach is hybrid in that it uses a geometric representation together with a state-of-the-art deep learning method capable of identifying 3D scene saliency. The geometric component allows for a compact and efficient representation, boosting the performance of the deep network architecture which proved insufficient on its own. Moreover, our approach allows not only to predict whether an input scene affords or not the interactions, but also the pose of the objects that allow these interactions to take place. Our predictions align well with crowd-sourced human judgment as they are preferred with 87% probability, show high rates of improvement with almost four times (4x) better performance over a deep learning-only baseline and are seven times (7x) faster than previous art.
Diarization and Separation Based on a Data-Driven Simplex

Separation of underdetermined speech mixtures, where the number of speakers is greater than the number of microphones, is a challenging task. Due to the intermittent behaviour of human conversations, typically, the instantaneous number of active speakers does not exceed the number of microphones, namely the mixture is locally (over-)determined. This scenario is addressed in this paper using a dual stage approach: diarization followed by separation. The diarization stage is based on spectral decomposition of the correlation matrix between different time frames. Specifically, the spectral gap reveals the overall number of speakers, and the computed eigenvectors form a simplex of the activity of the speakers across time. In the separation stage, the diarization results are utilized for estimating the mixing acoustic channels, as well as for constructing an unmixing scheme for extracting the individual speakers. The performance is demonstrated in a challenging scenario with six speakers and only four microphones. The proposed method shows perfect recovery of the overall number of speakers, close to perfect diarization accuracy, and high separation capabilities in various reverberation conditions.
A Deep Convolutional Network Based Supervised Coarse-to-Fine Algorithm for Optical Flow Measurement

The measurement of optical flow is an important problem in image processing. There are a number of methods available for optical flow estimation, including traditional variational methods, deep learning based supervised/unsupervised methods. In this work, we propose a deep convolutional network (CNN) based supervised coarse-to-fine approach, which is trained in end-to-end fashion. The proposed method is tested on standard optical flow benchmark datasets including Flying Chairs, MPI Sintel Clean and Final, KITTI. Experimental results show that the proposed framework is able to achieve comparable results to previous approaches with much smaller network architecture.
A Distributed Data Acquisition Architecture for Magnetic Positioning Systems

We illustrate a devised and implemented data acquisition system (DAQ) for a magnetic positioning system (MPS) that is currently under development. This system aims to track position and attitude of an active transmitting coil (TX) supplied with a sinusoidal current, whose generated magnetic field induces tensions on an array of passive receiving coils (RX). The DAQ system has to acquire voltages at all RX coils. These signals are then processed to estimate the TX coordinates, according to a mathematical model in order. In order to track the TX in real-time with a good resolution, voltages have to be measured simultaneously on all RXs. To this aim, we opted for a distributed architecture of microcontroller units (MCU). Each selected MCU has four analog-to-digital converters (ADC) that can work in parallel. Moreover multiple MCUs can be triggered simultaneously by a single MCU in a master-slave configuration. We used MCUs with a fast dual-core CPU. Each unit can directly process its own acquired signals. Then all data are sent to the master MCU, which estimates the coordinates of the TX. According to a preliminary analisys, this tracking system should achieve more than fifty coordinates measurements per second.