Miracle-dz/UniFAR-FTUs huggingface dataset:
Tactile Echoes: A Wearable System for Tactile Augmentation of Objects

We present Tactile Echoes, a wearable system for augmenting tactile interactions with any object. This system senses vibrations in the fingertip that are produced by interactions of the finger with a touched object. It processes the vibration signals in real-time via a parametric signal network and returns them to the finger as "Tactile Echoes" of the touch interaction. Just as acoustic echoes continuously respond to sound, Tactile Echoes are continuously generated in response to the sensed tactile contacts. A short finger tap produces discrete Echoes, while a slide can yield continuous feedback. We also render the signals as sound, yielding multisensory feedback. Many different effects can be designed using ten signal processing parameters. Distinct effects may be assigned to different touched objects or surface regions by sensing the hand location in a mapped environment. We investigated how Tactile Echoes are perceived in a behavioral study using semantic differential scaling and multidimensional scaling methods. This yielded low-dimensional, semantically grounded representations of the perceptual similarities between different Echoes. This system holds promise for enabling evocative haptic effects during a wide range of free-hand tactile interactions.
Estimation and Forecasting of Sovereign Credit Rating Migration Based on Regime Switching Markov Chain

Our research aims to develop the regime switching Markov chain (RSMC), a discrete time Markov chain whose underlying regime is depending on a hidden Markov model, which express the dynamics of sovereign credit rating migration. Estimated based on a version of the Expectation-Maximization algorithm, the regime in RSMC indicates either economic expansion or contraction. Then, we apply RSMC to the monthly time series of the sovereign credit rating of 41 nations from January 1994 to December 2018. At first, we confirm that the estimation of RSMC is superior to a homogeneous Markov chain. It implies that the credit rating dynamics are subject to the underlying economic condition. Secondly, we observe that the second tier and non-investment credit ratings in economic contractions are likely to be downgraded. We also detect the continental clustering of economic contractions for the Asian currency and European sovereign debt crises. Lastly, we discover that the forecasting performance of RSMC is superior to that of the benchmark, especially for the second tier and non-investment credit ratings. In conclusion, we claim that RSMC can improve the management of sovereign credit risk exposures.
A Systematic Review of the Third Party Auditing in Cloud Security: Security Analysis, Computation Overhead and Performance Evaluation

Cloud storage offers a considerable efficiency and security to the user’s data and provide high flexibility to the user. The hackers make attempt of several attacks to steal the data that increase the concern of data security in cloud. The Third Party Auditing (TPA) method is introduced to check the data integrity. There are several TPA methods developed to improve the privacy and efficiency of the data integrity checking method. Various methods involved in TPA, have been analyzed in this review in terms of function, security and overall performance. Merkel Hash Tree (MHT) method provides efficiency and security in checking the integrity of data. The computational overhead of the proof verify is also analyzed in this review. The communication cost of the most TPA methods observed as low and there is a need of improvement in security of the public auditing.
Effect of the Shape of the Solid Insulators on Creeping Discharge Propagation Under AC Voltages

This paper studies the use of circular and square shaped solid insulators on creeping discharge propagation over solid/liquid dielectric interfaces under AC voltages. A point-plane electrode arrangement based test apparatus is used to initiate creeping discharges over solid/liquid interfaces and the discharge propagation is captured using a camera. Two shapes of solid material samples are used to analyze the effect of using different shaped dielectric samples for the creeping propagation under AC voltages. Two types of solid materials, glass and acrylic, immersed in copra coconut oil are tested. Discharge length of the discharge patterns and the fractal dimension calculated by box counting method are used to explain the effect of square shaped and circular shaped dielectric samples for the morphology of creeping discharges. Results show that the shape of the solid dielectric material does not have a significant effect on the discharge length, fractal dimension and the shape of the discharge pattern.
Investigating Extensions to Random Walk Based Graph Embedding

Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches. However, most of the embedding approaches focus on representing the local neighborhood of nodes and fail to capture the global graph structure, i.e. to retain the relations to distant nodes. To counter that problem, we propose a novel extension to random walk based graph embedding, which removes a percentage of least frequent nodes from the walks at different levels. By this removal, we simulate farther distant nodes to reside in the close neighborhood of a node and hence explicitly represent their connection. Besides the common evaluation tasks for graph embeddings, such as node classification and link prediction, we evaluate and compare our approach against related methods on shortest path approximation. The results indicate, that extensions to random walk based methods (including our own) improve the predictive performance only slightly - if at all.
A Simplified Nonlinear Method for Progressive Collapse Analysis of Moment Resisting Frames

The Alternate Load Path Method (APM) that is presented in several standards is the widely followed method for the assessment of progressive collapse potential. In particular, the Linear Static APM is popular since it is simple and easy to implement compared to nonlinear methods. Although nonlinear approaches can give us a wider range of structural responses, they are not widely used since they involve complex procedures. We present here a nonlinear dynamic analysis approach for the post column loss behaviour in buildings that combines the simplicity of the linear methods with the comprehensiveness of the nonlinear ones. The method involves defining a special hinge that captures the specifications of the General Services Administration (GSA) guidelines, and reduces the complexities involved in nonlinear procedures. In addition to the collapse potential, the method is able to obtain the point of collapse initiation, collapse path and final extent of collapse relatively easily. A 3D concrete moment resisting frame under the short side face middle column removal case yielded similar results to the linear APM recommended by the GSA guidelines; but the proposed method was also able to identify that failure was initiated with the double spanned beam directly over the removed column yielding in flexure and progressed upwards, finally resulting in an overall collapse of all beams on the short side face that frame into the columns vertically in line with the removed column.
Identification of decompensation episodes in chronic heart failure patients based solely on heart sounds

Decompensation episodes in chronic heart failure patients frequently result in unplanned outpatient or emergency room visits or even hospitalizations. Early detection of these episodes in their pre-symptomatic phase would likely enable the clinicians to manage this patient cohort with the appropriate modification of medical therapy which would in turn prevent the development of more severe heart failure decompensation thus avoiding the need for heart failure-related hospitalizations. Currently, heart failure worsening is recognized by the clinicians through characteristic changes of heart failure-related symptoms and signs, including the changes in heart sounds. The latter has proven to be largely unreliable as its interpretation is highly subjective and dependent on the clinicians’ skills and preferences. Previous studies have indicated that the algorithms of artificial intelligence are promising in distinguishing the heart sounds of heart failure patients from those of healthy individuals. In this manuscript, we focus on the analysis of heart sounds of chronic heart failure patients in their decompensated and recompensated phase. The data was recorded on 37 patients using two types of electronic stethoscopes. Using a combination of machine learning approaches, we obtained up to 72% classification accuracy between the two phases, which is better than the accuracy of the interpretation by cardiologists, which reached 50%. Our results demonstrate that machine learning algorithms are promising in improving early detection of heart failure decompensation episodes.
Subgame Perfect Equilibrium Analysis for Jamming Attacks on Resilient Graphs

A cyber security problem is considered in a networked system formulated as a resilient graph problem based on a game theoretic approach. The connectivity of the underlying graph of the network system is reduced by an attacker who removes some of the edges whereas the defender attempts to recover them. Both players are subject to energy constraints so that their actions are restricted and cannot be performed continuously. We provide a subgame perfect equilibrium analysis and fully characterize the optimal strategies for the attacker and the defender in terms of edge connectivity and the number of connected components of the graph. The resilient graph game is then applied to the multiagent consensus problem. We study how the attacks and the recovery on the edges affect the consensus process.
Bacteria common to rhizosphere communities of Asiatic bittersweet across a post-glacial landscape

Invasive plants such as Asiatic bittersweet (Celastrus orbiculatus Thunb.) are a significant problem for land managers as they impact plant species composition, disrupt nutrient dynamics and structure of native ecosystems, and are difficult to eradicate. As a result of the increasing abundance of Asiatic bittersweet across the eastern U.S., we have been investigating underlying factors potentially contributing to the success of this plant. Recently, ecologists have been investigating the role of plant-soil-microbe interactions contributing to plant invasion. This work has led to question: are there certain microbes (e.g., bacteria, fungi) contributing to the success of some invasive plants? We hypothesize that despite differences in geologic age of soils where Asiatic bittersweet has established in the Indiana Dunes National Park there are sufficient common factors that led to common bacterial taxa in their rhizosphere. The objectives were to determine differences and commonalities in the soil chemistry, plant community and bacterial communities of Asiatic bittersweet plants. To achieve these objectives, bittersweet plants were collected at thirteen locations in the national park from soils ranging in geologic age from 150 to over 14,500 years. Surrounding soil chemistry, plant cover and the 16S rRNA gene amplicon sequences of rhizosphere soil bacterial communities of these Asiatic bittersweet were compared. Asiatic bittersweet coverage of sampling sites ranged from 2 to 77% averaging 52 ± 2%. There were statistically significant differences (p < 0.05) in alpha diversity (Shannon, Faith’s PD and Pielou’s evenness) and beta diversity (Bray Curtis, Jaccard, unweighted Unifrac, weighted Unifrac) among the samples when grouped by soil age or habitat. Despite these differences in the bacterial communities from different soil ages and habitats, some bacterial taxa (e.g., Bacillus, Streptomyces, Sphingomonas and Rhizobiales) previously found in other studies to be beneficial to plant growth were found in every rhizosphere community sampled. These microbes provide insight into a possible contributing factor to the success of this invasive plant at the Indiana Dunes National Park, and a strategy for future work to reduce the impact of Asiatic bittersweet establishment and offer some new strategies to manage this nuisance species.
The crossed-hands deficit in temporal order judgments occurs for present, future, and past hand postures

When people judge the temporal order (TOJ task) of two tactile stimuli at the two hands while their hands are crossed, performance is much worse than with uncrossed hands [1]. This crossed-hands deficit is widely considered to indicate interferences of external spatial coordinates with body-centered coordinates in the localization of touch [2]. Similar deficits have also been observed when people are only about to move their hands towards a crossed position [3]–[5], suggesting a predictive update of external spatial coordinates. Here, we extend the investigation of the dynamics of external coordinates during hand movement. Participants performed a TOJ task while they executed an uncrossing or a crossing movement, and during presentation of the TOJ stimuli the present posture of the hands was crossed, uncrossed or in-between. Present, future and past crossed-hands postures decreased performance in the TOJ task, suggesting that the update of external spatial coordinates of touch includes both predictive processes and processes that preserve the recent past. In addition, our data corroborate the flip model of crossed-hands deficits [1], and suggest that more pronounced deficits come along with higher time requirements to resolve interferences.
Dynamical Behavior of Biological Healthy Steady State in Leukemia Using a New Leukemic & Healthy Stem Cells Cohabitation Model with Distributed Delay

Acute Myeloid Leukemia (AML) treatment protocol from clinical point of view, aims to maintain a normal amount of healthy cells and to eradicate all malignant cells. This particular objective is biologically qualified as a positive healthy situation. In this paper, we give sufficient and necessary conditions for the global stability of such a healthy situation. To this end, we first propose a new distributed delay model of AML. The latter is an improvement of an existing delayed coupled model describing the dynamics of hematopoesis stem cells in AML. We modify the PDEs equations and transform them into a set of distributed delay equations. The proposed model is more suitable for biological phenomena than constant delay models as the proliferation time differs from a cell type to another. Furthermore in the proposed model, we consider the sub-population of cells that have lost their capacity of self-renewal and became progenitors. In second, we derive sufficient and necessary conditions for the global stability of healthy steady state. For this, the positivity of the obtained model and sequences of functions theory are used to construct new Lyapunov function candidates. Finally, we conduct numerical simulations to show that the obtained results complete and generalize those published in the literature.
Short Block Length Transmissions under Multi-Variate Stationary Rayleigh Fading

The need for latency reduction has recently spurred interest in short block length transmissions over wireless channels. In this paper, the performance of this type of transmissions is analyzed in the presence of multi-variate stationary Rayleigh fading coherent channels. The channel model is inspired in multi-symbol OFDM transmissions for ultra-reliable low latency communication (URLLC) services, according to which information is sent in a small number of OFDM symbols to reduce latency. More specifically, a high SNR approximation of the ergodic capacity and channel dispersion is derived for this particular fading choice. Results show that the optimum performance depends on the time-frequency fading dynamics through very simple functions of the channel correlation coefficients. The derived formulas provide a very useful tool to design physical layer system parameters from information measured at the receiver, as illustrated in a simple example where a HARQ system is studied in this asymptotic regime.
Active Tactile Recognition of Deformable Objects with 3D Convolutional Neural Networks

In this paper, a new concept of active tactile perception based on deep learning is presented. A tactile sensor is used to acquire sequences of tactile images of deformable objects when different forces are applied. Hence, the sequence of data can be represented by 3D tactile tensors in a similar way to the sequences of images represented in Magnetic Resonance Imaging (MRI). However, in this case, each 2D frame represents the pressure distribution when a certain force is applied, and the third dimension represents time or the variation of the applied force. Due to this feature of data, a 3D Convolutional Neural Network (3D CNN) called TactNet3D has been created to classify tactile information from 9 deformable objects. A dataset composed of 540 tactile sequences formed by [28×50×10] tactile tensors is used to train, validate and test the performance of TactNet3D, showing that it can classify deformable objects with an accuracy of 96.39% with time series of pressure distributions.
Robust Control of Wave Energy Converters Using Spectral and Pseudospectral Methods: A Case Study

Although Spectral and Pseudospectral methods have been used in a wide range of optimal control applications, to date, most of the literature uses these methods in a non-robust sense without considering possible dynamic deviation (uncertainties) from the nominal model. This study applies a recent robust approach for spectral and pseudospectral methods to a wave energy converter, considering structured uncertainty in the dynamical system. The results show that the robust approach gives better worst-case performance than an equivalent non-robust approach. Additionally, when structured uncertainty is considered in the dynamical system, the results show that the absorbed energy, obtained with the robust approach, is always positive. Finally, the advantages of this new approach are commented.
Study on the Influence of Carrier Motion of Vertical Axis Turbine Power Station on Turbine Performance

The carrier movement has great impact on the turbine performance. Firstly, the performance parameters of the stationary turbine are solved with flux-tube model and the blade element theory. Then the kinematics and dynamics of the carrier are analyzed, and the motion response of the carrier in the tidal power station is obtained. After that, the movement of the turbine spindle caused by the carrier oscillation is transformed into the movement of water flow, and the relative velocity of each point on the turbine spindle is obtained. By solving the local flow velocity of the spindle, the hydrodynamic force of the turbine impeller, when the carrier is moving, is obtained. Finally, the performance parameters of the carrier in stationary state and in motion state are compared. The results of comparison can provide theoretical support for the design and optimization of hydraulic turbine.
A mixed H2/H∞approach to robust actuator fault estimation

The main objective of the paper is to provide an actuator fault estimation scheme, which takes into account various uncertainty sources. In particular, they are divided into three groups: sensor measurement noise, process external exogenous disturbances as well as unknown fault dynamics. Contrarily to the approaches presented in the literature, they are not treated in the same waybut analyzed separately in a tailored fashion. Irrespective of the uncertainty source, the common approach is to minimize its effect on the fault estimation error in either $\mathcal{H}_{2}$or $\mathcal{H}_{\infty}$sense. As a result, a mixed performance-based actuator fault estimation scheme is obtained and its convergence is carefully analyzed as well. The final part of the paper presents results obtained for the DC servo-motor and compares the proposed approach with an alternative one.
Automatic Container Code Recognition via Faster-RCNN

Automatic container code recognition (ACCR) plays an important role in customs logistics and transport management. Due to complicated lighting conditions and background pollution, automatic detection and recognition of container codes remains a difficult task. In this work, we exploit Faster-RCNN, a robust object detection algorithm based on deep learning algorithm, to detect and recognize container codes. First, container code characters are detected as 36 classes of small objects, consisting of 26 capitals and 10 digits. Next, a novel post processing algorithm based on binary search tree is adopted to find container code from detected characters. Experimental results validate the proposed approach, and the overall accuracy on a dataset with 831 container codes achieves 97.71%.
Sparse Bayesian Learning-Based Kalman Filtering (SBL-KF) for Group-Sparse Channel Estimation in Doubly Selective mmWave Hybrid MIMO Systems

This work develops a sparse Bayesian learning based Kalman filtering (SBL-KF) framework for channel estimation in frequency and time selective, i.e. doubly selective, millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Towards this end, the doubly selective mmWave MIMO channel is represented in terms of the beamspace channel vector employing suitable sparsifying dictionary matrices comprising of the transmit and receive array response vectors. Subsequently, a multiple measurement vector (MMV) model is developed for mmWave MIMO channel estimation that also leverages the group-sparsity inherent in the equivalent beamspace channel, thus leading to improved performance. The Bayesian Cramér-Rao bound (BCRB) is also derived to benchmark the estimation performance. Simulation results are presented to demonstrate the improved performance of the proposed scheme in comparison to the existing schemes based on OMP and also the performance improvement from leveraging group-sparsity.
A Lane Keeping Assist Design: Adaptation to Driving Style Based on Aggressiveness

The influence of driver style identification on the performance of adaptive driver assistance systems has been well explored. Typically, designed controllers focus on safety, fuel efficient performance, reduced emissions etc. In this work, a rule based approach is formulated to classify driver style based on levels of aggressiveness. Employing the identified driver style a adaptive robust lane keeping controller is formulated. With focus on lane keeping, a robust controller is designed using higher-order sliding mode for a driver-in-the-loop dynamic model developed with driver-style adaptation. The closed loop stability of the proposed design is established. The proposed cooperative control approach is verified through simulations over the Satory test track for different driving conditions and the results are presented.