Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognosis of faults in gas turbine engines

A problem of interest to aircraft engine maintainers is the automatic detection, classification, and prediction (or prognosis) of potential critical component failures in gas turbine engines. Automatic monitoring offers the promise of substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. Current processing for prognostic health monitoring (PHM) uses relatively simple metrics or features and rules to measure and characterize changes in sensor data. An alternative solution is to use neural nets coupled with appropriate feature extractors. We have developed techniques that couple neural nets with automated rule extractors to form systems that have: good statistical performance; easy system explanation and validation; potential new data insights and new rule discovery, novelty detection; and real-time performance. We apply these techniques to data sets data collected from operating engines. Prognostic examples using the integrated system are shown and compared with current PHM system performance. Rules for performing the prognostics will be developed and the rule performance compared.
Using symmetries and generating functions to calculate and minimize moments of inertia

This paper presents some formulae to calculate moments of inertia for solids of revolution and for solids generated by contour plots. For this, the symmetry properties and the generating functions of the figures are utilized. The combined use of generating functions and symmetry properties greatly simplifies many practical calculations. In addition, the explicit use of generating functions gives the possibility of writing the moment of inertia as a functional, which in turn allows to utilize the calculus of variations to obtain a new insight about some properties of this fundamental quantity. In particular, minimization of moments of inertia under certain restrictions is possible by using variational methods.
Authentication gets personal with biometrics

Securing the exchange of intellectual property and providing protection to multimedia contents in distribution systems have enabled the advent of digital rights management (DRM) systems. User authentication, a key component of any DRM system, ensures that only those with specific rights are able to access the digital information. It is here that biometrics play an essential role. It reinforces security at all stages where customer authentication is needed. Biometric recognition, as a means of personal authentication, is an emerging signal processing area focused on increasing security and convenience of use in applications where users need to be securely identified. In this article, we outline the state-of-the-art of several popular biometric modalities and technologies and provide specific applications where biometric recognition may be beneficially incorporated. In addition, the article also discussed integration strategies of biometric authentication technologies into DRM systems that satisfy the needs and requirements of consumers, content providers, and payment brokers, securing delivery channels and contents.
Effects of Stress and Phenotypic Variation on Inbreeding Depression in Brassica rapa

Abstract Stressful environments are often said to increase the expression of inbreeding depression. Alternatively, Crow's “opportunity for selection” (the squared phenotypic coefficient of variation) sets a limit to how much selection can occur, constraining the magnitude of inbreeding depression. To test these hypotheses, we planted self- and cross-fertilized seeds of Brassica rapa into a factorial experiment that varied plant density and saline watering stresses. We then repeated the experiment, reducing the salt concentration. We observed considerable inbreeding depression, particularly for survival in the first experiment and growth in the second. Both stresses independently depressed plant performance. Families differed in their amounts of inbreeding depression and reaction norms across environments. Outcrossed progeny were sometimes more variable. Stresses had small and inconsistent effects on inbreeding depression and, when significant, tended to diminish it. Levels of phenotypic variability often predicted whether inbreeding depression would increase or decrease across environments and were particularly effective in predicting which traits display the most inbreeding depression. Thus, we find little support for the stress hypothesis and mixed support for the phenotypic variability hypothesis. Variable levels of phenotypic variation provide a parsimonious explanation for shifts in inbreeding depression that should be tested before invoking more complex hypotheses.
Autonomic Recovery Is Delayed in Chinese Compared with Caucasian following Treadmill Exercise

Caucasian populations have a higher prevalence of cardiovascular disease (CVD) when compared with their Chinese counterparts and CVD is associated with autonomic function. It is unknown whether autonomic function during exercise recovery differs between Caucasians and Chinese. The present study investigated autonomic recovery following an acute bout of treadmill exercise in healthy Caucasians and Chinese. Sixty-two participants (30 Caucasian and 32 Chinese, 50% male) performed an acute bout of treadmill exercise at 70% of heart rate reserve. Heart rate variability (HRV) and baroreflex sensitivity (BRS) were obtained during 5-min epochs at pre-exercise, 30-min, and 60-min post-exercise. HRV was assessed using frequency [natural logarithm of high (LnHF) and low frequency (LnLF) powers, normalized high (nHF) and low frequency (nLF) powers, and LF/HF ratio] and time domains [Root mean square of successive differences (RMSSD), natural logarithm of RMSSD (LnRMSSD) and R–R interval (RRI)]. Spontaneous BRS included both up-up and down-down sequences. At pre-exercise, no group differences were observed for any HR, HRV and BRS parameters. During exercise recovery, significant race-by-time interactions were observed for LnHF, nHF, nLF, LF/HF, LnRMSSD, RRI, HR, and BRS (up-up). The declines in LnHF, nHF, RMSSD, RRI and BRS (up-up) and the increases in LF/HF, nLF and HR were blunted in Chinese when compared to Caucasians from pre-exercise to 30-min to 60-min post-exercise. Chinese exhibited delayed autonomic recovery following an acute bout of treadmill exercise. This delayed autonomic recovery may result from greater sympathetic dominance and extended vagal withdrawal in Chinese. Trial Registration: Chinese Clinical Trial Register ChiCTR-IPR-15006684
Specifications in stochastic process algebra for a robot control problem

We present a novel approach to specification of dynamic systems. This approach, a stochastic extension of process algebra, facilitates quantitative, or performance, analysis, in addition to qualitative analysis. For unreliable systems this integrated approach encourages the investigation of the impact of functional characteristics on the performance of the system. Throughout the paper details of the stochastic process algebra are made concrete via an example: a robot control problem. Two specifications are presented of this problem. The first, an idealization, does not represent the possibility of failures. The second models both failures and recoveries. Each is solved to obtain performance measures for the system.
The SUMO protease SENP5 is required to maintain mitochondrial morphology and function

Mitochondria are dynamic organelles that undergo regulated fission and fusion events that are essential to maintain metabolic stability. We previously demonstrated that the mitochondrial fission GTPase DRP1 is a substrate for SUMOylation. To further understand how SUMOylation impacts mitochondrial function, we searched for a SUMO protease that may affect mitochondrial dynamics. We demonstrate that the cytosolic pool of SENP5 catalyzes the cleavage of SUMO1 from a number of mitochondrial substrates. Overexpression of SENP5 rescues SUMO1-induced mitochondrial fragmentation that is partly due to the downregulation of DRP1. By contrast, silencing of SENP5 results in a fragmented and altered morphology. DRP1 was stably mono-SUMOylated in these cells, suggesting that SUMOylation leads to increased DRP1 mediated fission. In addition, the reduction of SENP5 levels resulted in a significant increase in the production of free radicals. Reformation of the mitochondrial tubules by expressing the dominant interfering DRP1 or by RNA silencing of endogenous DRP1 protein rescued both the morphological aberrations and the increased production of ROS induced by downregulation of SENP5. These data demonstrate the importance of SENP5 as a new regulator of SUMO1 proteolysis from mitochondrial targets, impacting mitochondrial morphology and metabolism.
A questionnaire based examination of community networks

There is a variety of community networks out there. In this paper we try to shed a light on the community network landscape by presenting the results of a questionnaire filled in by different community network organisations around the world. We demonstrate the variety of infrastructure used and the different management approaches of the network within those communities. We also show the common challenges of the communities and provide pointers to tackle those challenges.
Estimasi Parameter Model Regresi Logistik Biner Komponen Utama Non Linear dengan Maksimum Likelihood

Regresi logistik biner digunakan pada data respon yang mengandung dua kategorik, dan ketika terjadi multikolinearitas pada variabel prediktor yang berskala campuran, maka pendekatan statistika yang dapat digunakan untuk mengatasi masalah tersebut adalah analisis komponen utama non linear. Estimasi parameter regresi logistik komponen utama non linear dilakukan melalui metode maksimum likelihood dan diperoleh hasil taksiran parameter yang implisit sehingga dilanjutkan dengan metode iterasi Newton Raphson untuk mendapatkan hasil estimasi yang konvergen.
Hidden community detection based on microblog by opinion-consistent analysis

The content or topic of post on the social network such as microblog, forum are usually reflected user's interests. Traditional community detection methods only consider explicit information of users. So that data analysis is limited in user predefined attributes. In order to solve this problem, a hidden community detection framework is proposed in this paper called opinion-consistent hidden community (OCHC) framework. Firstly, we collect and process post comments on facebook. Then, the post topic that the target user participated in can be defined through topic identification by the selected ontology, Wikipedia. Moreover, opinion-consistency between users and the target user is discovered by sentiment analysis. In brief, opinion mining and sentiment analysis are used to track the users who have the similar opinion on the specific topics. Besides, users focus on different features with different scopes on facebook can be found by multi-level OCHC framework that we proposed in this paper. Communities of opinion-consistent users are clustered Multi-level OCHC model. There are two major improvements of OCHC framework, one is that post topic is decided by topic identification instead of user-self, and the other is that user opinions are also considered during analysis phrase on OCHC framework. In experiment results, accuracy of topic identification promoted 5.5% than other methods and the time complexity reached 26 times faster than other one. On quantitative measurements of Polarity and Multi-Dimension sentiment analysis methods are performed well.
High bandwidth phase voltage and phase current control loop of a permanent magnet synchronous motor based on delta sigma bitstreams

Delta Sigma (ΔΣ) analog digital converters are usually comprised of an analog modulator and a digital low pass filter. These converters are used for phase voltage and phase current measurement in control loops. A new approach, the delta sigma signal processing (DSSP), directly processes the high frequent ΔΣ-bitstream. Due to the high frequent sampling rate of the DSSP, sampling effects can be neglected and a high closed loop bandwidth is achieved. In order to fully utilize the advantages of this concept, a high bandwidth three phase hysteresis modulator for power electronics is presented. Since the hysteresis modulator is based on DSSP, the ΔΣ-bitstreams of phase voltage and current measurement can be directly processed. The performance of this feedback is evaluated with regard to the compensation of inverter nonlinearities, average switching frequencies and the dynamic behavior of the control loops.
Towards First-Order Random Walk Inference

Path Ranking Algorithm (PRA) addresses classification and retrieval tasks using learned combinations of labeled paths through a graph. Unlike most Statistical Relational Learning (SRL) methods, PRA scales to large data sets but uses a limited set of paths in its models—ones that correspond to short first order rules with no constants. We consider extending PRA in two ways—learning paths that correspond to first order rules with constants, and incorporating long paths. Backward random walks are applied to find candidate constant paths, and long paths are efficiently constructed using bidirectional random walks from both query and target nodes. We apply these techniques to the tasks of knowledge base inference and coordinate term extraction. Empirical results show that constant paths provide informative class priors for relational classification, significantly improving performance on both tasks. Bidirectional search significantly reduces path finding time, and improves model quality for coordinate term extraction.
Structural Insight into Tau Protein’s Paradox of Intrinsically Disordered Behavior, Self-Acetylation Activity, and Aggregation

Tau is an intrinsically disordered protein (IDP) implicated in Alzheimer’s disease. Recently, tau proteins were discovered to be able to catalyze self-acetylation, which may promote its pathological aggregation. Understanding the paradox of tau’s random-like conformations, aggregation propensity, and enzymatic activity are challenging questions. We characterized the atomic structures of two truncated tau constructs, K18 and K19, consisting of, respectively, only the four- and three-repeats of tau protein, providing structural insights into tau’s paradox. Extensive 4.8 μs replica-exchange molecular dynamics simulations of the tau proteins achieved quantitative correlation with experimental Cα chemical shifts. Our results revealed (1) dynamically ordered conformations with close lysine–cysteine distances essential for tau self-acetylation and (2) high β-sheet content and large hydrophobic surface exposure for the two critical hexapeptides (275VQIINK280 and 306VQIVYK311), crucial for tau aggregation. Together, they illuminate tau’s perplexing behavior of how its disordered state can accomplish both roles.
Giving in the bedroom: The costs and benefits of responding to a partner’s sexual needs in daily life

A common reason why people in ongoing romantic relationships report engaging in sex with their partner—in addition to pursuing their own pleasure—is to meet their partner’s sexual needs. While meeting a partner’s needs with responsiveness and care is crucial in romantic relationships, it is important, especially in the domain of sexuality, that people do not neglect their own needs when meeting the needs of their partner. In a 21-day daily experience study of both members of 122 romantic couples recruited from the community, we tested whether being responsive to a partner’s sexual needs (i.e., high sexual communal strength) and focusing on a partner’s needs while neglecting one’s own needs (i.e., high unmitigated sexual communion) were associated with both partners’ daily sexual and relationship satisfaction. We also tested attention to positive partner-focused and negative self-focused cues during the sexual experience as novel mechanisms of these effects. The results generally showed that on days when people (or their romantic partner) reported higher sexual communal strength, they reported greater attention to positive partner-focused sexual cues and, in turn, both partners experienced greater daily sexual and relationship satisfaction. In contrast, on days when people reported higher unmitigated sexual communion, they reported greater attention to negative self-focused sexual cues and, in turn, experienced lower relationship and sexual satisfaction, although these effects did not extend to their romantic partner. Implications of the results for promoting higher quality sexual experiences and relationships are discussed.
Dynamic Cloud provisioning for scientific Grid workflows

Scientific computing requires an ever-increasing number of resources to deliver results for growing problem sizes in a reasonable timeframe. In the last decade, while the largest research projects were able to afford expensive supercomputers, others were forced to opt for cheaper resources such as commodity clusters or computational Grids. Today, Cloud computing proposes an alternative by which resources are no longer hosted by the scientists' computational facilities, but leased from specialized data centers only when and for how long they are needed. In this paper, we analyze the problem of dynamic provisioning of Cloud resources to scientific workflows that do not have sufficient Grid resources available, as required by their computational demands. We propose and study four provisioning aspects that deal with the general leasing model encountered in today's commercial Cloud environments based on resource bulks, fuzzy descriptions, and hourly payment intervals: Cloud start, instance type, Grid rescheduling, and Cloud stop. We study the impact of our techniques to the overall execution time, overall cost, and cost per unit of saved time with respect to various instance types offered by the Amazon EC2.
Mobile Lorm Glove: introducing a communication device for deaf-blind people

Marginalized communities like deaf-blind people are excluded from several forms of communication. This paper introduces a novel system of interaction to support deafblind people's communication and therefore enhance their independence. We introduce the Mobile Lorm Glove: a mobile communication and translation device for the deafblind. The glove translates the hand-touch alphabet Lorm, a common form of communication used by people with both hearing and sight impairment, into text and vice versa. We will present a hardware prototype, created in a participatory design process, which enables the deaf-blind user to compose messages via fabric pressure sensors placed on the palm of the glove to be transmitted as an SMS to the receiver's handheld. Initiated by small vibrating motors located on the back of the glove, tactile feedback patterns allow the wearer to perceive incoming messages. We discuss related work, prototype design and interaction design and application scenarios. We conclude with an outlook into further research.
Fine-Grained Feature-Based Social Influence Evaluation in Online Social Networks

The evaluation of a user's social influence is essential for various applications in online social networks (OSNs). We propose a fine-grained feature-based social influence (FBI) evaluation model. First, we construct a user's initial social influence by exploring two essential factors, that is, the possibility of impacting others and the importance of the user himself. Second, we design the social influence adjustment model based on the PageRank algorithm by identifying the influence contributions of friends. For the aim of fine-grained evaluation, based on a feature set which includes the related topics and user profiles, we differentiate the feature strength of users and the tie strength of user relations. We also emphasize the effects of common neighbors in conducting influence between two users. Through experimental analysis, our FBI model shows remarkable performance, which can identify all users' social influences with much less duplication (it is less than 7 percent with our model, while more than 80 percent with other degree-based models), while having a larger influence spread with top- k influential users. A case study validates that our model can identify influential users with higher quality.
Une brève introduction à la perception sensori-motrice en robotique

Nous presentons dans cet article une approche de la perception en robotique dont l’originalite est d’etre repensee dans un cadre exclusivement sensorimoteur. Cette demarche suscite un interet croissant au sein de la communaute de recherche, au carrefour de disciplines variees comme la physique, les mathematiques, les sciences cognitives et les neurosciences computationnelles. De fait la robotique apporte un terrain experimental particulierement approprie, un cadre de pensee ideal a l’eclosion de nouvelles idees, voire de decouvertes sur l’une des facultes les plus etonnantes et encore tres mysterieuse de l’animal et de l’homme. Nous presentons pour commencer les travaux de Poincare sur la perception sensorimotrice de la geometrie de l’espace, en particulier sa dimension, puis un certain nombre de travaux actuels qui en sont directement inspires. Nous presentons ensuite des recherches qui visent a etendre ces idees a la representation sensorimotrice de l’espace. La question de la representation du corps y est egalement abordee. Nous terminons par la question de la formulation des motivations dans un cadre sensorimoteur. L’ensemble de ces travaux constitue, nous l’esperons, une bonne porte d’entree dans la comprehension de la vision «sensorimotrice » de la perception.
An Empirical Evaluation for Feature Selection Methods in Phishing Email Classification

Phishing email detection is highly dependent on the accuracy of anti-phishing classifiers. Classifiers that use Machine-Learning techniques achieve highest phishing email classification accuracy results according to the literature. Using effective features in Machine-Learning is a critical step in raising classifiers detection accuracy. This study aims at evaluating a number of feature subset selection methods as they relate to the phishing email classification domain. In order to perform this study, a total of 47 classification features were constructed as previously proposed in the literature. The primary outcome of this study is that the Wrapper evaluator and the Best-First: Forward searching method resulted in finding the most effective features subset among all other evaluated methods. This study addresses the gap that exists between fragmented literature items by evaluating them together following common evaluation metrics. Using the best performing feature selection method, an effective features subset was found among the 47 previously proposed features, which resulted in a highly accurate anti-phishing email classifier with an f 1 score of 99.396%. This also shows that a highly competitive anti-phishing email classifier can still be constructed by only using existing Machine-Learning techniques and previously proposed features if an effective features subset is found.