Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Passivity and Passivity Relaxation for Impedance Control of Flexible-Joint Robots with Inner-Loop Torque Control

Passivity is a canonical condition for the safety of interactive systems, but practical limitations restrict its utility as a design tool. A system with a passive model can be unstable in high-stiffness environments, passivity is difficult to show with inner-loop controllers, and as it is a binary condition it provides limited design comparison insight; as a result, it is rarely used for inner-loop design. As passivity safety claims are limited by model accuracy, conditions for the passivity of a system with bounded-magnitude model uncertainty (robust passivity) are developed in this paper. Additionally, a condition for coupled environment–robot stability is developed using mixed passivity and small-gain condition, allowing rigorous relaxation of passivity at high frequencies for typical impedance-controlled systems. These approaches are used in the analysis of an impedance-controlled series-elastic actuated system with inner-loop torque control and also compared with traditional design tools (bandwidth ratio, sensitivity function, etc.). The approach is then validated experimentally, identifying model uncertainty bounds under various load conditions, and then using the measured uncertainty for controller synthesis. Robust passivity is then compared with nominal passivity in a validation experiment under manual excitation and impact.
Region of Interest Detection based on Local Entropy Feature for Disaster Victim Detection System

Region of interest (ROI) detection plays an important role in object detection. It needs to be accurate and fast in some applications like real time disaster victim detection systems. ROI can reduce time and search space in detecting objects. In this paper visual saliency map is used for ROI detection. In most literature, most of ROI detection models only concentrate on reducing false positive (detecting wrong objects as intended ones) rate rather than false negative (missing intended object). In disaster victim detection, missing disaster victims is more important than detecting other objects like victim. So, the proposed method also focuses on reducing false negative error rate in object detection. In the proposed system, local entropy feature is added in Graph Based Visual Saliency (GBVS) map in addition to colour, orientation and shape feature maps.
Interactive Virtual Campus Tour System Using Skeleton Information from Kinect

In recent years, various technologies have been used for university campus guide map. However, most existing campus guide map systems are difficult to obtain experiences like actually visiting campus. In this paper, we developed an interactive virtual campus tour system that provide high immersive feeling on a VR system using L-shaped stereoscopic display and Kinect.
Feature normalization for similarity calculations in matrix factorization method

Matrix factorization is a well-established approach in recommender systems with its roots in document retrieval. In this approach the original matrix of interactions between users and items is approximately factorized into matrices representing user features and item features. In this paper we argue for an orthogonality condition, under which the rows of the user feature matrix can be used to estimate similarity between users (and similarly for item similarity). Furthermore we provide an algebraic derivation of a normalization procedure to ensure that orthogonality conditions holds for any matrix factorization technique regardless of the rank of the factors. Finally we demonstrate the improvement in similarity ratings when the aforementioned normalization is applied to both explicit ratings and implicit feedback datasets, using the Alternating Least Square with Weighted $\lambda$-Regularization and Bayesian Probabilistic Matrix Factorization models.
Noise Resistance Global Method Optical Flow Using Adaptive Lorentzian Influence

This paper presents the using of adaptive Lorentzian influence with the global method optical flow for noise resistance on the visual progression. In particular, optical flow is the pattern of apparent motion of image objects between two consecutive frames caused by the movement of object or camera. It is 2D vector field where each vector is a displacement vector showing the movement of points from the first frame to second. When the image flow is degraded by noise, the performance for approximating the vector in optical flow is incorrect. With adaptive Lorentzian influence, we ensure more desirable noise resistance in global method optical flow in our experiment. We also test the using of adaptive Lorentzian influence over many noise resistance models when they proceed on global method optical flow. Many intensities of the additive white Gaussian (AWG) noise from low to high intensity of noise are utilized in our experiment to ensure our proposed method.
Frank-Wolfe Algorithm for the Exact Sparse Problem

In this paper, we study the properties of the Frank-Wolfe algorithm to solve the \ExactSparse reconstruction problem. We prove that when the dictionary is quasi-incoherent, at each iteration, the Frank-Wolfe algorithm picks up an atom indexed by the support. We also prove that when the dictionary is quasi-incoherent, there exists an iteration beyond which the algorithm converges exponentially fast.
DR-Net: A Novel Generative Adversarial Network for Single Image Deraining

Blurred vision images caused by rainy weather can negatively influence the performance of outdoor vision systems. Therefore, it is necessary to remove rain streaks from single image. In this work, a multiscale generative adversarial network- (GAN-) based model is presented, called DR-Net, for single image deraining. The proposed architecture includes two subnetworks, i.e., generator subnetwork and discriminator subnetwork. We introduce a multiscale generator subnetwork which contains two convolution branches with different kernel sizes, where the smaller one captures the local rain drops information, and the larger one pays close attention to the spatial information. The discriminator subnetwork acts as a supervision signal to promote the generator subnetwork to generate more quality derained image. It is demonstrated that the proposed method yields in relatively higher performance in comparison to other state-of-the-art deraining models in terms of derained image quality and computing efficiency.
Non‐invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: The DACHS study

Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non‐metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non‐metastasized vs. metachronously metastasized stage II CRC patients of the German population‐based case–control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow‐up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non‐metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10‐fold cross‐validation, by nested cross‐validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non‐metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10‐fold cross‐validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non‐metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non‐invasively stage II CRC patients according to their risk for metachronous metastasis.
Picture Quality Measurement and Analysis for Human Vision Mimicry in Python Environment

Spatial domain parameters of a digital image deal with the information related to the intensity distribution in the spatial domain of a digital image. Here, use of the two spatial domain parameters is elaborated in this work: Intensity Factor and Brightness Factor. These parameters can be calculated for a digital image to use in applications like quality measurement and real-time alert generation. From this information, an algorithm can be written so that computer vision can predict that how the scenario or picture can be judged by human vision. These parameters can also be used for predicting the next steps to be taken in an image processing algorithm, saving the computational cost.
Stock Prediction via Sentimental Transfer Learning

Stock prediction is always an attractive problem. With the expansion of information sources, news-driven stock prediction based on sentiments of social media, such as sentiment polarities in financial news, becomes more and more popular. However, the distributions of news articles among different stocks are skewed, which makes stocks with few news have few training samples for their prediction models, and thus leads to low prediction accuracy in the stock predictions. To address this problem, we propose sentimental transfer learning, which transfers sentimental information learned from news-rich stocks (source) to the news-poor ones (target), and prediction performances of the later ones are, therefore, improved. In this approach, the financial news articles of both the source and target stocks are first mapped into the same feature space that is constructed by sentiment dimensions. Second, we develop three different transfer principles in order to explore different transfer scenarios: 1) the source and target stocks’ historical price time series are highly correlated; 2) the source and target stocks are in the same sector and the former is the most news-rich one in the sector; and 3) the source stock has the highest prediction performance in validation data set. Third, a majority voting mechanism is designed based on the principles. The voting mechanism is to select the most proper source stock from the candidate stocks that are generated by different principles. Stock predictions are finally made based on the prediction models trained on the selected stocks. Experiments are conducted based on the data of Hong Kong Stock Exchange stocks from 2003 to 2008. The empirical results show that sentiment transfer learning can improve the prediction performance of the target stocks, and the performances are better and more stable with the source stocks selected by the voting mechanism.
An Efficient Approach for Electronic Voting Scheme without An Authenticated Channel

In this paper, we investigate a fully decentralized electronic voting scheme based on a privacy-preserving sum protocol without an authenticated channel. Our solution not only satisfies the most essential requirements for an e-voting scheme but also is convenient since it can be directly implemented on the public network without installing any cryptographic tool. In addition, the experimental results show that the total voting time of our scheme is approximately 20% faster than that of the typical voting protocols
DOA Estimation for Echo Signals and Experimental Results in the AM Radio-Based Passive Radar

The direction of arrival (DOA) of the target’s echo signal is extremely important for passive detection and tracking. However, it is a challenge to estimate the echo signal’s DOA due to the low signal-to-noise ratio of the echo signal and the serious masking effect. In this paper, a novel method is developed to estimate the DOA of the echo signal through enhancing the desired echo signal and attenuating the interference in range-Doppler domain before performing the DOA estimation. Firstly, the cross-ambiguity functions of the direct path signal and the received array signal are calculated. Then, they are combined to form a single-snapshot virtual array signal. Under certain conditions, the desired echo signal can be enhanced, and the interference can be attenuated simultaneously. After extending the single-snapshot virtual array signal to multi-snapshots, the conventional MUSIC approach can be performed, and the clutter interference can be suppressed significantly through subspace orthogonal meanwhile. Finally, the DOAs of echo signals as well as their corresponding Doppler shifts can be measured on a 2-D direction-Doppler map. The virtual array signal’s construction principle and the gain of echo signal are analyzed theoretically, and some experimental results are presented to verify the effectiveness of the proposed method.
Learning a Discriminative Feature Descriptor with Sparse Coding for Action Recognition

In this paper, we propose a novel algorithm for action recognition. The contribution of our work is three-fold. First, modified Weber local descriptor (IWLD) is proposed to capture the form cues of the action video sequences. Through introducing novel Weber magnitude and orientation components, our proposed IWLD can represent local patterns more effectively and accurately than existing Weber local descriptor (WLD). Second, to describe the form feature, histogram of improved Weber orientation Magnitude (HIOWM) is constructed. Considering motion and context cues also have discriminative power, we further propose a scheme that fuses HIOWM with motion and context cues to generate motion context HIOWM (MCHIOWM) descriptor to represent action video sequences. Third, for the sake of the more discriminative feature, we adopt sparse coding method to further refine the selected MCHIOWM. We present experiments to validate that the proposed framework obtains the competitive performance compared with the state-of-the-art methods.
A Framework for Event Log Generation and Knowledge Representation for Process Mining in Healthcare

Process Mining is of growing importance in the healthcare domain, where the quality of delivered services depends on the suitable and efficient execution of processes encoding the vast amount of clinical knowledge gained via the evidence-based medicine paradigm. In particular, to assess and measure the quality of delivered treatments, there is a strong interest in tools able to perform conformance checking. In process mining for the healthcare domain, a number of major challenges are posed by: (i) the complexity of involved data, that refers to patients' aspects such as disease, behaviour, clinical history, psychology, etc; (ii) the availability of data, that come from the heterogeneous, fragmented and scant connected healthcare system; and (iii) the wide range of available standards for communication (DICOM, IHE, etc.) or data representation (ICD9, SNOMED, etc.) purposes. To effectively perform process mining in the healthcare domain, it is crucial to build event logs capturing all the steps of running processes, which have to be derived by the knowledge stored in the Electronic Health Records. It is therefore crucial to cope with aforementioned data-related challenges. In this paper, we aim at supporting the exploitation of process mining in the healthcare domain, particularly with regards to conformance checking. We therefore introduce a set of specifically-designed techniques, provided as a suite of software packages written in R. In particular, the suite provides a flexible and agile way to automatically and reliably build Event Log from clinical data sources, and to effectively perform conformance checking.
MPPT Based SEPIC Converter With Voltage Doubler Cell For PV Systems

This article introduces “MPPT based SEPIC converter with voltage doubler cell for PV systems”. The primary aim of this study is to efficient energy conversion by utilizing SEPIC converter. The SEPIC converter accomplishes requirements like 1) Large transformation ratio with lesser number of elements. 2) Minimization of heavy stresses across switch. The description of operating principle of analysis is expressed within this article. The PV modeling along with MPPT algorithm and SEPIC converter for 250W are simulated in the MATLAB Simulink.
HMM-based Speech Synthesizer for Easily Understandable Speech Broadcasting

In this paper, we report an improvement to our proposed easy-to-understand public-address system. This system broadcasts synthesized speech, which is parsed from speech-recognized text. In order to improve the performance our system, we implemented a Hidden Markov Model (HMM) based speech synthesis system dependent on the input speaker’s speech. As a result, the performance of the speech synthesizer did not depend upon the number of learning sentences to generate the model, and a constant subjective quality value was measured using the MUSHRA evaluation method. We found that speech synthesized by learning 50 sentences exhibited sufficient performance.
Dynamics of pleasure–displeasure at the limit of exercise tolerance: conceptualizing the sense of exertional physical fatigue as an affective response

ABSTRACT The search for variables involved in the regulation and termination of exercise performance has led to integrative models that attribute a central role to the brain and utilize an array of psychological terms (e.g. sensation, perception, discomfort, tolerance). We propose that theorizing about exercise regulation would benefit from establishing cross-disciplinary bridges to research fields, such as affective psychology and neuroscience, in which changes along the dimension of pleasure–displeasure are considered the main channel via which homeostatic perturbations enter consciousness and dictate corrective action (slowing down or stopping). We hypothesized that ratings of pleasure–displeasure would respond to the severity of homeostatic perturbation and would be related to time to exhaustion during exercise performed at an unsustainable intensity. In a within-subjects experiment (N=15, 13 men and 2 women, age 23.4±2.2 years; maximal oxygen uptake 46.0±8.0 ml kg−1 min−1), we compared the slope of ratings of pleasure–displeasure (acquired every 1 min) during cycling exercise at a power output 10% above critical power until volitional termination under glycogen-loaded and glycogen-depleted conditions. As hypothesized, ratings of pleasure–displeasure declined more steeply under glycogen depletion (P=0.009, d=0.70) and correlated closely with time to exhaustion under both glycogen-loaded (r=0.85; P<0.001) and glycogen-depleted conditions (r=0.83; P<0.001). We conclude that in exercise, as in other domains, changes in pleasure–displeasure may be the main channel via which homeostatic perturbations enter consciousness. This proposal may have important implications for conceptualizing and identifying the neurobiological mechanisms of the sense of exertional physical fatigue. Summary: A new conceptualization of the sense of exertional physical fatigue, a universal human trait the nature and the neurobiological origins of which are commonly described in the literature as an enigma.
H-bridge based start-up controller with selector and telemetry control for dc-dc converters operating in parallel between dual power lines

In power converters the demand for the dc-dc converters with higher input voltage range and controlled output voltage are increasing more and more. And also,the dc-dc converters which are capable of working with different input voltages and in parallel are required. While operating in parallel the problems like selection of converter and selection of main and backup supply power lines becomes a problem. In order to turn on or turn off a particular converter which is in parallel loop controller ICsare used. This method involves issues like EMI/EMC, power loss and spurious turn on and lesser controller if multiple power lines are present. In order to overcome these difficulties a H-bridge based start-up controller with selector and telemetry control circuit method's design and simulation has been discussed in this paper. Also, hardware setup and result of the same has been discussed.
Using Extended AC Optimal Power Flow for Effective Decision Making

Ahstract- This paper is motivated by the need for software capable of dispatching many diverse resources as system conditions vary widely. At present, a major gap exists between the software applications needed by the changing electric power industry and the software available. As an example, we present an ACOPF software tool called NETSSWorks that can bridge the gap with its capabilities of: 1) performing optimization over many available decision variables, ranging across real power generation, demand, and T&D voltages; 2) auto-selecting the cost function to be used and automatically changing to a different cost function as conditions vary to achieve an overall objective of providing maximum service at minimum cost; and 3) selecting the most effective dispatch to be made which would bring the selected cost function close to its optimum. Such software would provide the most effective dispatch in an advisory manner for the next dispatch time interval and continue optimizing in a lookahead way as new data becomes available. Additionally, such ACOPF tools must deal with potential infeasibility and also be able to diagnose potential causes of infeasibility. By means of two examples, the IEEE 30-bus system and the New York State grid, we demonstrate the use of NETSSWorks for finding the most effective equipment dispatch and compare to today's industry practice.
Neurovascular structures in human vastus lateralis muscle and the ideal biopsy site

A density model of neurovascular structures was generated from 28 human vastus lateralis muscles isolated from embalmed cadavers. The intramuscular portion of arteries, veins, and nerves was dissected, traced on transparencies, and digitized before adjustment to an average muscle shape using Procrustes analysis to generate density distributions for the relative positions of these structures. The course of arteries, veins, and nerves was highly variable between individual muscles. Nevertheless, a zone of lower average neurovascular density was found between the tributaries from the lateral circumflex femoral and the deep femoral arteries. While the area with the lowest density was covered by the iliotibial tract and would therefore not be suitable for biopsies, another low‐density area was located in the distal portion of vastus lateralis. This was just anterior to the iliotibial tract, in a zone that has been described as a good needle biopsy site. The reported complication rates of needle biopsies (0.1%‐4%) are in the range of expectations when simulated based on this model. It is concluded that the optimal human vastus lateralis biopsy site is in the distal portion of the muscle, between ½ and ¾ of the length from the greater trochanter to the lateral epicondyle, just anterior to the iliotibial band.