Miracle-dz/UniFAR-FTUs huggingface dataset:
Path Tracking of an Autonomous Ground Vehicle With Different Payloads by Hierarchical Improved Fuzzy Dynamic Sliding-Mode Control

A hierarchically improved fuzzy dynamical sliding-model control (HIFDSMC) is presented to address the autonomous ground vehicle (AGV) path tracking problem. The proposed controller has two portions: one is the virtual desired input (VDI), and the second is the path tracking control (PTC). In addition to the equivalent control in VDI and PTC, an improved fuzzy dynamical sliding-mode control (IFDSMC) is designed to deal with the system uncertainties, e.g., different payloads. Contributions of this paper include the following four parts: 1) Based on the nominal system response, the fuzzy rules and scaling factors of the IFDSMCs in the VDI and PTC are easily chosen. In contrast, a conventional fuzzy logic control approach requires more trial-and-error tuning to obtain a satisfactory performance. 2) The proposed HIFDSMC possesses the tuning mechanism (the coefficients of two sliding surfaces, the scaling factors in indirect and direct modes, and the fine tuning in fuzzy table) such that the uncertainties are tackled without a larger computational burden. 3) The stability of the closed-loop system is verified by the Lyapunov stability with hierarchical concept. 4) Different payloads not at the mass center of the AGV (e.g., greater than 25% in the total weight of the AGV) are tackled by the IFDSMCs to obtain a satisfactory performance. Finally, the application to the assembly line of the AGV with different payloads to track the circular and piecewise straight-line paths by the proposed HIFDSMC is compared with the hierarchical fuzzy decentralized PTC.
Finite element formulation of Timoshenko beam on Winkler elastic foundation

Initial parameter solution and finite element formulation of Timoshenko beam on elastic foundation were presented.Element stiffness and equivalent nodal forces of distributing load,concentrating load and concentrating moment were deduced.Based on the shear stress distribution assumption of material mechanics,trapezium-meshed arithmetic method for shear correction factor was proposed.Shear correction factors of T-type cross-sections were calculated.Stepped T-beam of variable cross-section and inverse T-beam were analyzed.Local hanging beam on elastic foundation was studied with and without shear deformation.The results show that the hanging length,maximum deformation,slope,shear force and moment differ at 48.88%,4.61%,67.17%,43.59%,59.26%,which verifies the importance effect of shear deformation for beam on elastic foundation.
PTree: Direct Lookup with Page Table Tree for NVM File Systems

While the rise of fast non-volitle memory (NVM) provides a unique opportunity to re-examine storage architectures and to redesign file systems, it also highlights the inefficiency of existing system software interfaces, especially the multi-step component based name resolution mechanism within modern file systems, which contains much repetitive work and does great harm to the overall performance. We find that the overhead proportion of namespace processing increases greatly in NVM file systems, compared to that in traditional disk based ones, and makes the new bottleneck. We present PTree, a fullpath name resolution scheme designed to maximize performance of file systems on NVM architectures. PTree organizes the metadata of file systems with page table to exploit the fast random access that NVMs provide, and it significantly increases the efficiency and scalability of name resolution. PTree utilizes the same interfaces as the hash table library of the kernel, simplifying the migration of traditional file systems to within 200 lines of code. Experimental results show that file systems ported with PTree improve performance by 22% to 4.5x in throughput and 66% to 6.7x in delay.
On the stabilization of a TCP/AQM systems with time delay by the joint use of the τ-decomposition and the D-decomposition methods

The work presented in this paper proposes a new method of stabilization of a second order system by a delayed positive feedback. This method is based on the root locus approach. The τ-decomposition and D-decomposition methods are used to determine regions in the parameters' space that ensure stability of the closed-loop system. The use of this method for stabilization of a TCP/AQM systems with time delay is successfully applied. An illustrative example is given to show the application of our method.
Integration and Temporal Processing of Asynchronous Audiovisual Speech

Multisensory integration of visual mouth movements with auditory speech is known to offer substantial perceptual benefits, particularly under challenging (i.e., noisy) acoustic conditions. Previous work characterizing this process has found that ERPs to auditory speech are of shorter latency and smaller magnitude in the presence of visual speech. We sought to determine the dependency of these effects on the temporal relationship between the auditory and visual speech streams using EEG. We found that reductions in ERP latency and suppression of ERP amplitude are maximal when the visual signal precedes the auditory signal by a small interval and that increasing amounts of asynchrony reduce these effects in a continuous manner. Time–frequency analysis revealed that these effects are found primarily in the theta (4–8 Hz) and alpha (8–12 Hz) bands, with a central topography consistent with auditory generators. Theta effects also persisted in the lower portion of the band (3.5–5 Hz), and this late activity was more frontally distributed. Importantly, the magnitude of these late theta oscillations not only differed with the temporal characteristics of the stimuli but also served to predict participants' task performance. Our analysis thus reveals that suppression of single-trial brain responses by visual speech depends strongly on the temporal concordance of the auditory and visual inputs. It further illustrates that processes in the lower theta band, which we suggest as an index of incongruity processing, might serve to reflect the neural correlates of individual differences in multisensory temporal perception.
Hypothesis-driven experiment design in computer simulation studies

In simulation studies, the goal specifies the objective or purpose of the study and thus drives the entire experimentation process. Relevant experiments and respective experiment hypotheses are derived from the study's goal and the model's observed behavior provides evidence whether these hypotheses hold. Current assistance systems do not integrate research hypotheses. Thus, the researcher has to make important design decisions which limits both replicability and reproducibility of the results. In this paper, the process of simulation studies is systematized based on a formally specified hypothesis. By this means, the research hypothesis becomes the key element of the study and guides the entire process. Hypothesis-driven simulation studies allow for the automated design, execution, and evaluation of experiments based on specific research questions. This facilitates documentation and execution as well as replication of simulation studies.
When will you do what? - Anticipating Temporal Occurrences of Activities

Analyzing human actions in videos has gained increased attention recently. While most works focus on classifying and labeling observed video frames or anticipating the very recent future, making long-term predictions over more than just a few seconds is a task with many practical applications that has not yet been addressed. In this paper, we propose two methods to predict a considerably large amount of future actions and their durations. Both, a CNN and an RNN are trained to learn future video labels based on previously seen content. We show that our methods generate accurate predictions of the future even for long videos with a huge amount of different actions and can even deal with noisy or erroneous input information.
DOUBLE-COMPRESSED JPEG DETECTION IN A STEGANALYSIS SYSTEM

The detection of hidden messages in JPEG images is a growing concern. Current detection of JPEG stego images must include detection of double compression: a JPEG image is double compressed if it has been compressed with one quality factor, uncompressed, and then re-compressed with a different quality factor. When detection of double compression is not included, erroneous detection rates are very high. The main contribution of this paper is to present an efficient double-compression detection algorithm that has relatively lower dimensionality of features and relatively lower computational time for the detection part, than current comparative classifiers. We use a model-based approach for creating features, using a subclass of Markov random fields called partially ordered Markov models (POMMs) to modeling the phenomenon of the bit changes that occur in an image after an application of steganography. We model as noise the embedding process, and create features to capture this noise characteristic. We show that the nonparametric conditional probabilities that are modeled using a POMM can work very well to distinguish between an image that has been double compressed and one that has not, with lower overall computational cost. After double compression detection, we analyze histogram patterns that identify the primary quality compression factor to classify the image as stego or cover. The latter is an analytic approach that requires no classifier training. We compare our results with another state-of-the-art double compression detector.
Security Assurance for Smart Contract

Currently, Bitcoin and Ethereum are the two most popular cryptocurrency systems, especially Ethereum. It permits complex financial transactions or rules through scripts, which is called smart contracts. Since Ethereum smart contracts hold millions of dollars, their execution correctness is crucial against attacks which aim at stealing the assets. In this paper, we proposed a security assurance method for smart contract source code to find potential security risks. It contains two main functions, the first is syntax topological analysis of smart contract invocation relationship, to help developers to understand their code structure clearly; the second is logic risk (which may lead to vulnerabilities) detection and location, and label results on topology diagram. For developers' convenience, we have built a static analysis tool called SASC to generate topology diagram of invocation relationship and to find potential logic risks. We have made an evaluation on 2,952 smart contracts, experiment results proved that our method is intuitive and effective.
Natural rubber/organoclay nanocomposites: Effect of filler dosage on the physicomechanical properties of vulcanizates

Natural rubber/organoclay nanocomposites of varying filler loading [2 to 10 per hundred rubber (phr)] using derivative of tea (Camellia sinensis) seed oil were prepared by melt intercalation. Effects of filler dosage on the physicomechanical properties of the natural rubber (NR) vulcanizates were examined. Results of the mechanical properties indicates that tensile strength and tear properties of the modified organoclay/NR nanocomposites increases with increasing filler loading compared with the unmodified filled NR vulcanizates. Furthermore, rheological measurement showed that modified filled NR vulcanizate exhibited higher storage modulus (GI) than the unmodified filled NR. The values of the weight-swelling ratio (Qt) of the modified filled nanocomposites decreased remarkably and are lower than the unmodified filled NR vulcanizate. The higher value of the chemical crosslink density of 0.629 at 6 phr for the organoclay/NR composite indicate better reinforcement of the filler-rubber matrix over the unmodified. The scanning electron microscopy revealed that incorporation of modified organoclay up to 6 phr has transformed the failure mechanism of the resulting NR vulcanizate compared to the unmodified. There is an indication that the optimum level of incorporation of sodium salt of tea seed oil is 6 phr. Key words: Vulcanizate, physicomechanical, nanocomposite, Camellia sinensis, natural rubber, organoclay.
Diabetic Neuropathy – Nerve Morphology in the Upper Extremity

Diabetic neuropathy is the most common long-term complication of Type 1 and Type 2 diabetes, affecting approximately 10% within a year of diagnosis to 50% of subjects with diabetes for more than 25 years. Prevalence rates depend on diagnostic technique and the population under study (Dahlin et al., 2011; Rubino et al., 2007). Diabetic neuropathy associated with Type 1 diabetes tend to be more frequent, and develop more rapidly than in Type 2 diabetes. Gender differences seem to exist with males being more affected by neuropathy than females (Booya et al., 2005; Aaberg et al., 2008). Diabetes mellitus imposes a considerable burden on the nervous system and is the most common cause of peripheral nerve damage associated with a vast spectrum of neuropathy syndromes. The primary risk factor for the development of diabetic neuropathy is related to duration and severity of hyperglycaemia. Other independent risk factors for diabetic neuropathy include hypertension, hypercholesterolemia, smoking, increased body mass index and the presence of other complications of diabetes such as retinopathy and nephropathy (Adler et al., 1997). Diabetic neuropathy affects somatic as well as autonomic neurons of the peripheral nervous system with resultant morbidity and mortality for the patients and economic cost for the society and health care system. Interactions of pathogenic metabolic and vascular factors are responsible for the development of diabetic neuropathy. These complex mechanisms, which are not fully understood, need further clarification in order to develop new treatment strategies. The aim of this chapter is to describe methods and result in the evaluation of small and large nerve fibre pathology on the upper extremity.
Effects of ceramic membrane configuration on the performance of crossflow microfiltration of yeast suspension

Crossflow microfiltration of yeast suspensions was carried out using three types of ceramic membrane with different configurations.The effects of membrane configuration on filtration performance were investigated.The results showed that reducing the diameter of the membrane channel could increase the shear stress of the feed on the membrane surface,thus increasing the steady state flux and the critical pressure.Under the conditions of 3 m/s crossflow velocity and 0.1 MPa transmembrane pressure,the steady state flux for tubular,19 channel and 55 channel ceramic membrane elements were 96,128 and 196 L/(m2·h),and under the condition of 3 m/s crossflow velocity,the critical pressures for these three ceramic membrane elements were about 0.15,0.2 and 0.2 MPa,respectively.Moreover,with the reducing of the channel diameter,the specific resistance of the cake deposited on the membrane surface could be reduced.Thus,it had the characteristic of the low energy consumption.
Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning

Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch through model-free deep reinforcement learning. Our method involves training two fully convolutional networks that map from visual observations to actions: one infers the utility of pushes for a dense pixel-wise sampling of end-effector orientations and locations, while the other does the same for grasping. Both networks are trained jointly in a Q-learning framework and are entirely self-supervised by trial and error, where rewards are provided from successful grasps. In this way, our policy learns pushing motions that enable future grasps, while learning grasps that can leverage past pushes. During picking experiments in both simulation and real-world scenarios, we find that our system quickly learns complex behaviors even amid challenging cases of tightly packed clutter, and achieves better grasping success rates and picking efficiencies than baseline alternatives after a few hours of training. We further demonstrate that our method is capable of generalizing to novel objects. Qualitative results (videos), code, pre-trained models, and simulation environments are available at http://vpg.cs. princeton.edu
Multilingual Videos for MOOCs and OER

Massive Open Online Courses (MOOCs) and Open Educational Resources (OER) are rapidly growing, but are not usually offered in multiple languages due to the lack of cost-effective solutions to translate the different objects comprising them and particularly videos. However, current state-of-the-art automatic speech recognition (ASR) and machine translation (MT) techniques have reached a level of maturity which opens the possibility of producing multilingual video subtitles of publishable quality at low cost. This work summarizes authors' experience in exploring this possibility in two real-life case studies: a MOOC platform and a large video lecture repository. Apart from describing the systems, tools and integration components employed for such purpose, a comprehensive evaluation of the results achieved is provided in terms of quality and efficiency. More precisely, it is shown that draft multilingual subtitles produced by domain-adapted ASR/MT systems reach a level of accuracy that make them worth post-editing, instead of generating them ex novo, saving approximately 25%–75% of the time. Finally, the results reported on user multilingual data consumption reflect that multilingual subtitles have had a very positive impact in our case studies boosting student enrolment, in the case of the MOOC platform, by 70% relative.
Stochastic Consentability of Linear Systems With Time Delays and Multiplicative Noises

This paper develops stochastic consentability of linear multiagent systems with time delays and multiplicative noises. First, the stochastic stability for stochastic differential delay equations driven by multiplicative noises is examined, and the existence of the positive definite solution for a class of generalized algebraic Riccati equations (GAREs) is established. Then, sufficient conditions are deduced for the mean square and almost sure consentability and stabilization based on the developed stochastic stability and GAREs. Consensus protocols are designed for linear multiagent systems with undirected and leader-following topologies. It is revealed that multiagent consentability depends on certain characterizing system parameters, including linear system dynamics, communication graph, channel uncertainties, and time delay of the deterministic term. It is shown that a second-order integrator multiagent system is unconditionally mean square and almost surely consentable for any given noise intensities and time delay, and that the mean square and almost sure consensus can be achieved by carefully choosing the control gain according to certain explicit conditions.
Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning

Maintenance of equipment is a critical activity for any business involving machines. Predictive maintenance is the method of scheduling maintenance based on the prediction about the failure time of any equipment. The prediction can be done by analyzing the data measurements from the equipment. Machine learning is a technology by which the outcomes can be predicted based on a model prepared by training it on past input data and its output behavior. The model developed can be used to predict machine failure before it actually happens. There are different approaches available for developing a machine learning model. In this paper, a comparative study of existing set of machine learning algorithms to predict the Remaining Useful Lifetime of aircraft's turbo fan engine is done. The machine learning models were constructed based on the datasets from turbo fan engine data from the Prognostics Data Repository of NASA. Using a training set, a model was constructed and was verified with a test data set. The results obtained were compared with the actual results to calculate the accuracy and the algorithm that results in maximum accuracy is identified. We have selected ten machine learning algorithms for comparing the prediction accuracy. The different algorithms were compared to obtain the prediction model having the closest prediction of remaining useful lifecycle in terms of number of life cycles.
Recall Traces: Backtracking Models for Efficient Reinforcement Learning

In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and sample for which the (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks.