Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Fault Diagnosis in Discrete Event Systems via Set Intersection Refinements

We extend and verify diagnosability for a class of set intersection refinement strategies, which can be used for distributed state estimation and fault diagnosis in nondeterministic finite automata that are observed at multiple observation sites. These strategies allow observation sites to (periodically) communicate their diagnostic information (i.e., possible states along with corresponding status, such as normal operation and/or fault type) to other observation sites, which subsequently fuse all available diagnostic information (via set intersection operations), and continue operation based on the refined diagnostic information. We verify diagnosability using the proposed distributed protocol, with polynomial complexity, via compositions of (extended versions of) local verifiers, which are capable of capturing the refinement of information under the set intersection operations, as well as the influence of the refinement process on immediate or future diagnosis decisions.
BIBS, a New Descriptor for Melanoma/Non-Melanoma Discrimination

Malignant melanoma is known as the deadliest type of skin cancer. Nowadays, several automatic diagnosis systems have been developed in order to improve the detection of melanoma lesions. Boundary fluctuation of skin lesions is an important factor to discriminate different types of lesions. This paper introduces a new border descriptor that properly quantifies contents of the concave contours. The Fourier and wavelet descriptors of the proposed boundary intersection-based signature (BIBS) are obtained. Support vector machine (SVM) is used for classification process. It is shown that the BIBS has better performance in comparison with conventional shape signature. Also, the combination of the BIBS Fourier descriptor and a shape and textural feature set achieves the best accuracy of 90.34%, sensitivity of 92.02%, specificity of 88.08%, and precision of 91.72% with less feature dimension amongst previous works on Dermatology Information System and DermQuest datasets.
Reproduce. Generalize. Extend. On Information Retrieval Evaluation without Relevance Judgments

The evaluation of retrieval effectiveness by means of test collections is a commonly used methodology in the information retrieval field. Some researchers have addressed the quite fascinating research question of whether it is possible to evaluate effectiveness completely automatically, without human relevance assessments. Since human relevance assessment is one of the main costs of building a test collection, both in human time and money resources, this rather ambitious goal would have a practical impact. In this article, we reproduce the main results on evaluating information retrieval systems without relevance judgments; furthermore, we generalize such previous work to analyze the effect of test collections, evaluation metrics, and pool depth. We also expand the idea to semi-automatic evaluation and estimation of topic difficulty. Our results show that (i) previous work is overall reproducible, although some specific results are not; (ii) collection, metric, and pool depth impact the automatic evaluation of systems, which is anyway accurate in several cases; (iii) semi-automatic evaluation is an effective methodology; and (iv) automatic evaluation can (to some extent) be used to predict topic difficulty.
Hexagonal Search Based Compression Noise Estimation and Reduction in Video

Due to the advancement of digital devices more and more images and videos were captured using smart phones and digital devices. These images and videos were compressed at lower bit rates. Since, the available bandwidth is limited in nature. The compressed images and videos were affected with compression noise, in-order to remove such compression noise a content dependent noise reduction technique is being used here. The Video compression has become an unavoidable feature now a days. Proposed block matching motion estimation in video compression technique such as DCT (Discrete Cosine transform) is used to achieve optimal compression of individual frames and hexagon-based diamond search (HEXBDS) algorithm used for motion estimation. The proposed algorithm is efficient and robust. The proposed algorithm that reducing the bandwidth used by Motion Picture Expert Group (MPEG) algorithm. The objective of this paper is to propose a new algorithm which focuses on combing the hexagon-based search, which is faster than diamond search algorithm. Experiments are performed and obtained high accuracy of hexagon search based method.
CXCR6+ST2+ memory T helper 2 cells induced the expression of major basic protein in eosinophils to reduce the fecundity of helminth

Significance Helminth infection elicits T helper type 2 (Th2) and Treg cells. However, the critical cell subpopulations of Th2 and Treg cells in antihelminth immunity remain unknown. We identified two Th2 cell subpopulations: CXCR6+ST2+ memory Th2 cells and ST2− memory Th2 cells. Although both subpopulations induced the accumulation of eosinophils into the lungs during helminth infection, those induced by CXCR6+ST2+ memory Th2 cells expressed high levels of major basic protein (MBP) and contributed to the reduction of fecundity of helminth. This response was suppressed by ST2+ but not ST2− Treg cells, both of which are induced during helminth infection. We, therefore, identified CXCR6+ST2+ memory Th2 cells as a critical subpopulation to induce accumulation of eosinophils strongly expressing MBP in the lungs. Memory T helper (mTh) cells play important roles in the reinfection of pathogens and drive the pathogenesis of diseases. While recent studies have characterized the pathogenic mTh2 cell subpopulations driving allergic inflammation, those that induce immune responses against helminth infection remain unknown. We found that IL-5–producing CXCR6+ST2+CD44+ mTh2 cells play a crucial role in the IL-33–dependent inhibition of the fecundity of helminth, whereas other ST2− mTh2 cells do not. Although both cell types induced the infiltration of granulocytes, especially eosinophils, into the lungs in response to helminth infection, the ST2+ mTh2 cell-induced eosinophils expressed higher levels of major basic protein (MBP), which is important for reducing the fecundity of Nippostrongylus brasiliensis (Nb), than ST2− mTh2 cell-induced ones. Notably, we also found that ST2+ Treg cells but not ST2− Treg cells suppressed CXCR6+ST2+ mTh2 cell-mediated immune responses. Taken together, these findings show that we identified a mechanism against helminth elicited by a subpopulation of IL-5–producing mTh2 cells through the accumulation of eosinophils strongly expressing MBP in the lungs.
Effects of Combined Plyometric and Short Sprint With Change-of-Direction Training on Athletic Performance of Male U15 Handball Players

Abstract Hammami, M, Gaamouri, N, Aloui, G, Shephard, RJ, and Chelly, MS. Effects of combined plyometric and short sprint with change-of-direction training on athletic performance of male U15 handball players. J Strength Cond Res 33(3): 662–675, 2019—This study examined the effects of combined plyometric and change-of-direction training (CPT) on selected measures of performance (repeated-sprint T-test [RSTT], sprint performance [5, 10, 20, and 30 m], change-of-direction tests [modified Illinois test and T-half test], squat jump, countermovement jump, countermovement jump with aimed arms 5-jump test, Y-balance test, Stork balance test, and 20-m shuttle run) in male U15 handball players. Players were randomly divided between experimental (n = 14; age: 14.5 ± 0.3 years) and control (n = 14; age: 14.6 ± 0.2 years) groups. All trial participants trained and played matches together, but for 8 weeks in the latter part of the season, the experimental group replaced the tactical part of their normal regimen by a biweekly course of CPT. This induced significant increases in sprint performance relative to controls (p = 0.006, effect size [ES] = 0.135, 8.7%; p=<0.001, ES = 0.214, 7.4%; p = 0.022, ES = 0.096, 7.2%; and p = 0.032, ES = 0.085, 8.9% over distances of 5, 10, 20, and 30 m, respectively). The modified Illinois test and T-half test also showed significant gains for experimental subjects (p < 0.025; ES = 0.205, 4.2% and p < 0.001; ES = 0.093, 5.3%, respectively), as did vertical and horizontal jumps. However, there were no significant changes of RSTT, 20-m shuttle run, or balance. Nevertheless, we conclude that the current CPT is effective in enhancing sprint performance, change-of-direction tests, and vertical and horizontal jumping and thus it can be commended to male U15 handball players as a useful option for improving important components of their physical performance.
Monitoring Autonomous Agents in Self-Organizing Industrial Systems

In this paper, we extend an existing approach for monitoring work-pieces in Self-Organizing Industrial Systems (SOIS) to be applicable to more complex SOIS settings, where autonomous agents can move and regulate their velocity completely free. The problem of having infeasible many possible valid routes is handled by extracting significant sub-paths (pathlets), by which valid routes can be reconstructed. We show that meaningful pathlets can also be extracted for complex SOIS settings. By adding a temporal component to the dictionary, we are able to detect anomalous trajectories regarding spatial movement and temporal flow. Furthermore, environmental changes, like added/removed machines, can be located using information gained from the reconstruction process. Additionally, we show that the global movement behavior of the agents can be analyzed, too, by monitoring specific movement parameters for the dictionary pathlets. The data for the experiments are generated using a SOIS simulation which implements a state-of-the-art architecture for multi-agent systems.
Readmission After Elective Ileostomy in Colorectal Surgery Is Predictable

Background and Objectives: Patients who undergo colorectal surgery have high postoperative morbidity, with ileostomates being the most disadvantaged. Recent studies assessing readmission risk factors do not provide a specific prediction model and, if so, do not focus on patients who have had colorectal surgery; thus, the results of these studies have limited applicability to our specialized practice. We wanted to develop a prediction model for readmission within 30 days of discharge after ileostomy creation. Methods: Patients who underwent elective ileostomy creation from 2013 to 2016 at the University of Florida were included in this retrospective study. Factors significantly associated with readmission within 30 days after discharge were identified by comparing a cohort that was readmitted within 30 days with one that was not. A practical, predictive model that stratified a patient's risk of readmission after the index procedure was developed. Results: A total of 86 iliostomates were included; of those, 22 (26%) were readmitted within 30 days. Factors significantly associated with readmission included preoperative steroid use, history of diabetes, history of depression, lack of a hospital social worker or postoperative ostomy education, and the presence of complications after the index procedure. A model predicting readmission within 30 days of discharge that comprised the first 4 factors was developed, with a sensitivity of 73% and a specificity of 77%. Conclusion: Prediction of readmission in patients who undergo ileostomy creation is possible, suggesting interventions addressing predictive factors that may help decrease the readmission rate. Prospective validation of the model in a larger cohort is needed.
Dynamic behaviour of a ring coupled boost converter system with passivity-based control

This paper discusses a dispersed generation system of multiple DC/DC converters with DC power sources connected in a ring formulation. Here is presented the analysis of the system based on the stored energy and passivity characteristics of the system. Passivity Based Control (PBC), with its energy-modifying and damping-injection technique, is applied to a ring coupled converter system to stabilize itself at a desired DC voltage in the presence of external disturbances. The numerical results reveal the effective application of the control as a robust and flexible technique.
Direct-Detection Single-Sideband Systems: Performance Comparison and Practical Implementation Penalties

There is a growing research interest towards high-capacity communication systems for inter data-center (~80 km) communications. While coherent systems are a mature and high-performance technology, their cost is still very high compared to simpler direct-detection solutions. Recently, advanced digital signal processing (DSP) techniques, such as Single Side-Band (SSB) transmission and Kramers-Kronig (KK) receivers have been proposed to increase both spectral efficiency and reach of such direct-detection systems. Apart from the DSP techniques, the main drawback of these solutions is the requirement of a strong (i.e. several decibels higher than the signal) carrier added at the transmitter. This solution inevitably decreases the resulting signal-to-noise ratio (OSNR) and potentially increase the impact of fiber Kerr non-linearities. In this contribution, will provide further insights on the practical implementation penalties which arise from the addition of such carrier in the framework of single-sideband DMT solutions. In particular, we will focus on the modulator biasing technique to add such carrier.
A Reduced KELM model using DBSCAN Clustering algorithm for centroid selection

Extreme Learning Machine (ELM) is a single layer feedforward neural network (SLFN) and a popular classifier for classification and regression problems. It is unstable due to random initialization of weights between the hidden layer and the input layer. To overcome this problem of instability, kernelized ELM with kernels has been developed. Gaussian kernel Extreme Learning Machine (KELM) is one of the stable but computationally complex variant of ELM in which the number of hidden layer neurons is equal to the number of input instances. Therefore, to reduce this computational complexity, a new variant of KELM i.e. reudced KELM has been proposed in the literature. It randomly selects the number of centroids from the training data set such that the number of centroids is always less than the number of input instances. This work develops a new reduced version of KELM using density-based clustering algorithm (DBSCAN). DBSCAN is a non-spherical clustering algorithm which is used to identify the number of concepts. Each concept is represented using the centroid of the cluster. In proposed KELM model, the number of hidden layer neurons is equal to the number of clusters formed by the DBSCAN clustering algorithm. Experiments have been performed on 16 datasets drawn from the KEEL data set repository. The result shows that the developed reduced KELM model gives better performance as compared to the traditional KELM in terms of reduced training time and testing time.
EAT -: The ICMI 2018 Eating Analysis and Tracking Challenge

The multimodal recognition of eating condition - whether a person is eating or not - and if yes, which food type, is a new research domain in the area of speech and video processing that has many promising applications for future multimodal interfaces such as adapting speech recognition or lip reading systems to different eating conditions. We herein describe the ICMI 2018 Eating Analysis and Tracking (EAT) Challenge and address - for the first time in research competitions under well-defined conditions - new classification tasks in the area of user data analysis, namely audio-visual classifications of user eating conditions. We define three Sub-Challenges based on classification tasks in which participants are encouraged to use speech and/or video recordings of the audio-visual iHEARu-EAT database. In this paper, we describe the dataset, the Sub-Challenges, their conditions, and the baseline feature extraction and performance measures as provided to the participants.
A Brief Report on a Reduced Preference for Passive-Avoidant Leadership After a Restless Night

The purpose of this study is to investigate the effect of fragmented sleep on followers’ leadership preferences. In a counterbalanced experimental study involving 39 followers, changes in leadership preferences were recorded after one night of fragmented sleep (awakened every 80 min during the night), compared to a rested condition with the conditions separated by seven nights. The results showed that the participants rated passive-avoidant leadership less ideal, after one night of fragmented sleep. No differences regarding preference for transactional or transformational leadership occurred. Thus, negative perceptions of leaders may partly stem from reduced sleep patterns. However, further studies are needed to confirm this finding.
Overview of Phase-Change Electrical Probe Memory

Phase-change electrical probe memory has recently attained considerable attention owing to its profound potential for next-generation mass and archival storage devices. To encourage more talented researchers to enter this field and thereby advance this technology, this paper first introduces approaches to induce the phase transformation of chalcogenide alloy by probe tip, considered as the root of phase-change electrical probe memory. Subsequently the design rule of an optimized architecture of phase-change electrical probe memory is proposed based on a previously developed electrothermal and phase kinetic model, followed by a summary of the state-of-the-art phase-change electrical probe memory and an outlook for its future prospects.
Concept of an Analytical Model for Cloud Computing Infrastructure

Cloud computing is among the fastest-developing areas of Information and Communication Technologies (ICT). The increasing number of users and the number of services offered by the network makes it necessary to develop new methods of network modeling and analysis. The paper presents a concept of model of cloud computing based on the Erlang’s Ideal Grading (EIG) model.
Manufacturing Execution System Specific Data Analysis-Use Case With a Cobot

The purpose of this research is to analyze and upgrade the performances of the Baxter intelligent robot, through data mining methods. The case study belongs to the robotics domain, being integrated in the context of manufacturing execution systems and product lifecycle management, aiming to overcome the lack of vertical integration inside a company. The explored data comprises the parameters registered during the activities of the Baxter intelligent robot, as, for example, the movement of the left or right arm. First, the state of the art concerning the data mining methods is presented, and then the solution is detailed by describing the data mining techniques. The final purpose was that of improving the speed and robustness of the robot in the production. Specific techniques and sometimes their combinations are experimented and assessed, in order to perform root cause analysis, then powerful classifiers and metaclassifiers, as well as deep learning methods, in optimum configuration, are analyzed for prediction. The experimental results are described and discussed in details, then the conclusions and further development possibilities are formulated. Based on the experiments, important relationships among the robot parameters were discovered, the obtained accuracy for predicting the target variables being always above 96%.
Millimeter-Wave Real-Time All-Digital Transmitter with Electro-Optical Upconversion

In this work, we demonstrate a real-time architecture for enabling the use of all-digital transmitters in mm-wave frequency bands. We propose the combination of delta-sigma modulation techniques and electro-optical upconversion stages to ease the stringent requirements in the design of RF front-ends. In our proof-of-concept demonstration, the real-time delta-sigma modulation technique is synthesised and prototyped into a field-programmable gate array (FPGA). Experimental results are reported in the range from 27 GHz up to 63 GHz and evaluated in terms of error-vector magnitude (EVM). The promising results demonstrate the scalability and flexibility of the proposed technique towards the design of highly-integrated and agile front-ends for next generation of communications.
Does the nicotine metabolite ratio moderate smoking cessation treatment outcomes in real‐world settings? A prospective study

Abstract Background and aims In smoking treatment trials comparing varenicline with transdermal nicotine replacement therapy (NRT), stratified by nicotine metabolite (3‐hydroxycotinine/cotinine) ratio (NMR), the relative benefit of varenicline is greater among normal rather than slow metabolizers. This study tested if the relative effectiveness of varenicline and NRT is associated with NMR status in a natural treatment setting. A secondary aim was to test if this relationship is moderated by behavioural support. Design Prospective observational multi‐centre study with 4‐week and 52‐week follow‐up. Setting Nine English Stop Smoking Services (SSS). Participants Data came from 1556 smokers (aged ≥ 16 years) attending SSS between March 2012 and March 2013. Interventions Participants received pharmacotherapy together with behavioural support. Measurements The primary outcome was carbon monoxide‐verified continuous abstinence at both follow‐up times. Main explanatory variables were (1) NMR status [slow (NMR < 0.31, n = 451) versus normal (NMR ≥ 0.31, n = 1105) metabolizers]; (2) pharmacotherapy (varenicline versus NRT) and (3) behavioural support (individual versus group‐based treatment). Analyses adjusted for baseline socio‐demographic, SSS, mental/physical health and smoking characteristics. Findings Of participants, 44.2% [95% confidence interval (CI) = 41.7–46.6%] and 8.0% (95% CI = 6.8–9.5%) were continuously abstinent at 4 and 52 weeks. Varenicline was more effective than NRT at 4 weeks (P < 0.001) but only marginally so at 52 weeks (P = 0.061). There was no or inclusive evidence that NMR status moderated relative efficacy of varenicline and NRT at 4‐ [P = 0.60, Bayes factor (BF) = 0.25] or 52‐week follow‐ups (P = 0.74, BF = 0.73). However, this relationship was moderated by behavioural support (p = 0.012): the relative benefit of varenicline over NRT at 52‐week follow‐up was greater in slow, not normal, metabolizers receiving group rather than individual support (P = 0.012). Conclusions In a real‐world setting, the nicotine metabolite ratio status of treatment‐seeking smokers does not appear to contribute substantially to the differential effectiveness of varenicline and nicotine replacement therapy in Stop Smoking Services, when both pharmacotherapy and behavioural support are self‐selected.
Application of Pornographic Images Recognition Based on Depth Learning

With the rapid development of the Internet, the images become the main medium of information dissemination, while the spread of pornographic images are getting more serious. Therefore, we propose a detection method of pornographic images based on a combination of global and local features. Considering the NPDI database's defective both in quality and quantity, so this paper constructs new database CUC_NSFW (Not Suitable for Work) applying data augmentation methods to improve the classification performance. Pornographic images with only exposed sensitive organs become the bottleneck of improving model recall ratio. We design a sensitive organs detection module, cascaded behind the residual network assisting the recognition of pornography images. And our method makes a good performance based on the research work of pornographic image detection