Miracle-dz/UniFAR-FTUs huggingface dataset:
Permanent magnet synchronous motor modeling and control

The objective of this study is to achieve permanent magnet synchronous motor (PMSM) speed control in PSIM using a self-tuning PI controller with dual feedforward artificial neural network (DFNN). The two parameters of the PI controller gain, kp and ki, can be controlled online by selftuning in the motor control system verification. The speed response, robustness of anti-load disturbance performance are better compared to a single feedforward artificial neural network (SFNN).
CFA ( color filter array ) based Motion Blur detection using lowest directional High-frequency energy

35  Abstract— Capturing of motion image is critical task for digital camera. Some times due to motion pixel position are change such event are motion bluer. Motion bluer degrades quality of motion image. Now removal of bluer image is challenging task in this scenario. Various authors develop a different bluer removal filter such as Gaussian filter, usm filter and Laplace filter but all filter are not to up to mark and suffered some restoration problem. Now proposed a new motion bluer image filter using CFA (color filter array) based on lowest directional high frequency energy. In this method we used independent color image filter with segmented block of image data and apply such filtration technique and getting promising result. It will improve accuracy with less computational cost. Moreover, the proposed motion blurred region detection can efficiently estimate blurred regions without Point Spread Function estimation.
Collaborative content caching in wireless edge with SDN

The increasing computing and storage capacity at the wireless access nodes (eNodeB and Access Points) creates opportunities for content caching at the wireless edge and, therefore, further reduction of content delivery delay. However, realizing a large-scale content caching at the wireless edge entails significant challenges, due to the difficulties of managing the vast amount of cached content and handling redirections among the large number of nodes. In this respect, we propose collaborative content caching at wireless edge nodes by coupling a low-false-positive hash table to index the cached content with centralized content routing and redirection, leveraging on software-defined networking (SDN) principles. We present emulation results to assess the benefits of the proposed solution.
Tweet classification based on their lifetime duration

Many microblog messages remain useful only within a short time, and users often find such a message after its informational value has vanished. Users also sometimes miss old but still useful messages buried among outdated ones. To solve these problems, we develop a method of classifying messages into the following three categories: (1) messages that users should read now because their value will diminish soon, (2) messages that users may read later because their value will not largely change soon, and (3) messages that are not useful anymore because their value has vanished. Our method uses an error correcting output code consisting of binary classifiers each of which determines whether a given message has value at specific time point. Our experiments on Twitter data confirmed that it outperforms naive methods.
Cs229 Final Project Report Identifying Elephant Vocalizations

Introduction The Asian Elephant has been worshipped for centuries and plays an important role in Asian culture and religion. It is also a key biological species in the tropical forests of Asia. In the wild, this majestic animal is threatened by extinction, mainly due to rapidly growing human populations. Wild elephant populations are mostly small, isolated and unable to join as ancient migratory routes are cut off by human settlements. These facts have led the World Wildlife Fund (WWF) to establish the elephant as a flagship priority species, and has determined that their survival cannot be guaranteed by conserving their habitat alone. Being able to identify elephant vocalizations is a crucial part of understanding their communication patterns, which can then better inform decision makers about how to better conserve this iconic species. Thus, for this project, we aimed to identify and label a set of known Asian Elephant vocalizations. We used audio recordings of elephant sounds to build a series of classifiers with different models and features to attempt to capture the differences in these calls. Dataset Our original dataset belongs to the Linguistics Data Consortium (LDC) at the University of Pennsylvania. Specifically, we used data set LDC2010S05 Asian Elephant Vocalizations [1], which consists of 57.5 hours of audio recordings of vocalizations made by Asian Elephants in the Uda Walawe National Park, Sri Lanka. These recordings feature several types of elephant sounds that have some social meaning collected over an 18 month period from primarily adult female and juvenile elephants. The raw recordings features long stretches of time during which no vocalizations can be heard, but about 31 hours of them have already been annotated with information on elephant calls, individuals, etc. As for the elephant vocalizations themselves, researchers for this data set have identified 14 different calls. These fourteen classes are:
ADAPTS: a digital transient simulation strategy for integrated circuits

Activity Driven Adaptive Partitioning Transient Simulation (ADAPTS) has been developed to bridge the gap between circuit and logic simulation. ADAPTS can accept analytical models for arbitrary multi-terminal elements and can control the accuracy versus efficiency tradeoff automatically. In addition, ADAPTS can simulate stiff circuits efficiently and accurately.
Tangible Context Modelling for Rapid Adaptive System Testing

With the rise of adaptive interactive systems came a growing demand for tools to test the envisioned adaptivity. This article presents a tactile interface allowing direct manipulation of a context model for adaptive behavior in intelligent environments. The presented tool starts with an abstract context model, which is transformed to an easy-to-use interface supporting tangible interaction. Different context parameters can directly be manipulated. Diverse context-relevant items can be integrated using tangible objects to emulate and use their real world location parameters. The article gives an insight in the technical implementation and the usage as substitute for a sensor topology from an existing intelligent environment to support evaluations and tests.
Classification of Colon Cancer Patients Based on the Methylation Patterns of Promoters

Diverse somatic mutations have been reported to serve as cancer drivers. Recently, it has also been reported that epigenetic regulation is closely related to cancer development. However, the effect of epigenetic changes on cancer is still elusive. In this study, we analyzed DNA methylation data on colon cancer taken from The Caner Genome Atlas. We found that several promoters were significantly hypermethylated in colon cancer patients. Through clustering analysis of differentially methylated DNA regions, we were able to define subgroups of patients and observed clinical features associated with each subgroup. In addition, we analyzed the functional ontology of aberrantly methylated genes and identified the G-protein-coupled receptor signaling pathway as one of the major pathways affected epigenetically. In conclusion, our analysis shows the possibility of characterizing the clinical features of colon cancer subgroups based on DNA methylation patterns and provides lists of important genes and pathways possibly involved in colon cancer development.
MITOCHONDRIAL DNA SEQUENCE VARIATION AMONG POPULATIONS OF SUGARCANE BORER ELDANA SACCHARINA WALKER (LEPIDOPTERA: PYRALIDAE)

Studies on Eldana saccharina have shown that populations from western Africa have distinct behavioural differences when compared with populations from eastern and southern Africa. In addition, the parasitoid guilds attacking these populations in the different regions are markedly different. The parallel geographical variation in these patterns between several widespread populations of E. saccharina evoked the hypothesis of diversification. To evaluate this hypothesis, a molecular analysis of the Cytochrome Oxidase c subunit I (COI) region of the mitochondrial DNA was conducted on populations of E. saccharina from various parts of Africa. Results of the current study reveal the presence of genetic variation in E. saccharina populations, which is related to geographical distribution.
Interrelated Dynamics of Health and Poverty in Australia

Using the Household, Income and Labour Dynamics in Australia (HILDA) Survey, this study examines the joint dynamics of health and poverty in Australian families. Taking advantage of panel data, the modelling approach used in this study allows a better estimation of the causal relationship between health and poverty. The results indicate that the causality between health and poverty runs both ways and the relationship is confounded by unobserved heterogeneity. In particular, it is found that families headed by a person in ill-health are more likely to be in poverty compared with families headed by a person with good health. On the other hand, a family head whose family is in poverty in the current year is more likely to be in ill-health in the next year compared with a family head whose family is not in poverty. In addition, there is evidence that health and poverty are affected by correlated unobservables, causing health to be endogenous to poverty even in the absence of a reverse effect from poverty on health. Consequently, treating health as exogenous in a poverty equation would produce biased estimates.
Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs) with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP) and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC) reaches its boundaries.
Incorporating fuzzy prior knowledge into Relevance Vector Machine regression

Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper presents a simple but effective re-sampling algorithm based on piecewise differential interpolation and it is integrated with the sparse Bayesian learning framework for fuzzy model fused Relevance Vector Machine (RVM) regression. By using resampling algorithm encoded derivative regularization, the prior knowledge is translated into a pseudo training dataset, which finally is trained by the adaptive Gaussian kernel RVM to obtain more sparse solution. A preliminary empirical study shows that combining prior knowledge with training examples can dramatically improve the regression performance, particularly when the training dataset is limited.
Performance evaluation of a QoS technique for Bufferless Optical Packet Switches

In this paper we propose a technique to support Quality of Service for Bufferless Optical Packet Switches (OPS) equipped with shared Wavelength Converters (WC). The WCs sharing is fully, that is all of the arriving packets share the same pool of WCs. The QoS, expressed in terms of Packet Loss Probability, is differentiated by giving the packets different priorities in accessing both the output wavelengths and the WCs. The effectiveness of the QoS technique is studied by means of a sophisticated analytical model validated by simulation results. The results show that the proposed technique to support QoS allows a good differentiation of the QoS offered to the packets.
Fast booting based on nand flash memory

Fast booting is an important tool for minimizing the booting time of embedded systems, and a major factor in determining the usability of the systems. Most fast booting tools have focused on minimizing the booting time by optimizing the processing time from the starting stage of the boot loader to mounting stage of the root file system. However, previous research works did not consider read and write overheads of nand flash memory. In this paper we propose an efficient fast booting for embedded systems based on nand flash memory. We also show that the proposed fast booting mechanism has better performance.
Tyne Performance Evaluation of Anomaly-Based Detection Mechanisms

Common practice in anomaly-based intrusion detection is that one size fits all: a single anomaly detector should detect all anomalies. Compensation for any performance shortcomings is sometimes effected by resorting to correlation techniques, which could be seen as making use of detector diversity. Such diversity is intuitively based on the assumption that detector coverage is different – perhaps widely different – for different detectors, each covering some disparate portion of the anomaly space. Diversity, then, enhances detection coverage by combining the coverages of individual detectors across multiple sub-regions of the anomaly space, resulting in an overall detection coverage that is superior to the coverage of any one detector. No studies have been done, however, in which measured effects of diversity in anomaly detectors have been obtained. This paper explores the effects of using diverse anomalydetection algorithms (algorithmic diversity) in intrusion detection. Experimental results indicate that while performance/coverage improvements can in fact be effected by combining diverse detection algorithms, the gains are surprisingly not the result of combining large, non-overlapping regions of the anomaly space. Rather, the gains are seen at the edges of the space, and are heavily dependent on the parameter values of the detectors, as well as on the characteristics of the anomalies. As a consequence of this study, defenders can be provided with detailed knowledge of diverse detectors, how to combine and parameterize them, and under what conditions, to effect diverse detection performance that is superior to the performance of a single detector.
Dangerous Decisions: Influence of Juror Attitudes and Defendant Appearance on Legal Decision-Making

According to the dangerous decisions theory (Porter, S., & ten Brinke, L. (2009). Dangerous decisions: A theoretical framework for understanding how judges assess credibility in the courtroom. Legal and Criminological Psychology, 14(1), 119–134), intuitive evaluations of facial trustworthiness influence the interpretation of evidence presented in courtroom settings. This study examined the role of individual differences in this process. Participants were presented with crime narratives, accompanied by a photograph of the supposed defendant that was previously rated as highly trustworthy or untrustworthy. Following presentation of the evidence in each case, participants rated the defendant's culpability and then completed questionnaires assessing potential biases. Participants endorsing justice–fairness were more likely to exonerate an untrustworthy-looking defendant, but less likely to exonerate a trustworthy-looking one. Individuals who held a strong racial bias, by contrast, were less likely to be influenced by exonerating evidence, specifically for untrustworthy-looking defendants. These results suggest that faces varying in trustworthiness activate particular biases, and a tunnel vision approach to decision-making that may lead to wrongful convictions in a legal setting.
Correlates of perceived favorability of online courses for quantitative versus qualitative undergraduate business majors

ABSTRACT An online survey tested the association among background, technological, and course-related variables with perceived favorability of online courses for two independent samples of fall 2015 and spring 2016 business undergraduates taking at least one online or hybrid course. Results showed that perceived learning was a consistent positive correlate of perceived favorability of online courses for quantitative majors, whereas number of prior online or hybrid courses and perceived ease of technology tool use were consistent positive correlates for qualitative majors.
The link between religion and health: Psychoneuroimmunology and the faith factor.

Introduction 1. The Connection between Psychoneuroimmunology and Religion 2. The Development and History of Psychoneuroimmunology 3. Understanding How Stress Affects the Physical Body 4. Stress, Natural Killer Cells, and Cancer 5. Psychosocial Interventions and Prognosis in Cancer 6. Psychosocial Stress, Social Networks, and Susceptibility to Infection 7. Psychosocial Factors, Immunity, and Wound Healing 8. Psychosocial Factors, Spirituality/Religiousness, and Immune Function in HIV/AIDS Patients 9. Hostility, Neuroendocrine Changes, and Health Outcomes 10. Psychological Stress and Autoimmune Disease 11. Immune, Neuroendocrine, and Religious Measures 12. Psychoneuroimmunology and Eastern Religious Traditions 13. Psychoneuroimmunology and Western Religious Traditions 14. Psychoneuroimmunology and Religion: Implications for Society and Culture 15. Avenues for Future Research Conclusions
Leakage Resilient Provable Data Possession in Public Cloud Storage

Cloud storage is now an important development trend in information technology. To ensure the integrity of data storage in cloud storing, researchers have present some provable data possession (PDP) schemes. However the schemes can't resist side-channel attacks. Moreover securing cryptographic implementations against side-channel attacks is one of the most important challenges in modern cryptography. In this paper, we propose the first leakage-resilient provable data possession (LR PDP) scheme, which utilizes leakage-resilient signature to construct the homomorphic authenticator. In the scheme, the homomorphic authenticator is based on probabilistic Boneh-Lynn-Shacham(BLS) short signature. Moreover, the leakage-resilient provable data possession can tolerate leakage of (1-O(1)/2) of the secret key at every tag invocation. And the security of the proposed scheme is proved in the generic bilinear group model.
ANALISIS MOTIVASI KARYAWAN DI RUMAH MAKAN WARUNG KIPUNG

Penelitian analisis motivasi karyawan di rumah makan Warung Kipung bertujuan untuk mengetahui motivasi karyawan Warung Kipung. Motivasi berpengaruh terhadap keinginan bekerja seseorang yang juga kemudian berpengaruh terhadap hasil kerja. Motivasi dapat muncul berdasarkan kebutuhan dan kebutuhan terbagi menjadi 3 bagian yaitu existence, relatedness, dan growth. Selama ini Warung Kipung memberikan kebutuhan existence (eksistensi), relatedness (hubungan), dan growth (pertumbuhan) kepada karyawannya. Subyek pada penelitian ini adalah karyawan Warung Kipung, karyawan rumah makan lain dan pemilik rumah makan yang telah berdiri lebih dari 1 tahun. Obyek pada penelitian ini adalah motivasi karyawan Warung Kipung. Jenis penelitian ini menggunakan pendekatan kualitatif. Hasil dari penelitian ini adalah motivasi karyawan Warung Kipung merupakan kebutuhan material berupa uang dan tempat tinggal serta lingkungan kerja, rekan kerja yang baik, dan pencapaian terhadap dirinya sendiri. Dari kebutuhan tersebut karyawan masih berharap untuk mendapat hasil yang lebih baik dan hal itu menjadi motivasi mereka. Agar motivasi karyawan Warung Kipung dapat meningkat maka Warung Kipung akan memberikan gaji tambahan jika target tercapai, memberikan tempat tinggal yang lebih nyaman kepada karyawannya dengan menambah perabotan, dan memberikan buku dan televisi untuk mereka agar mereka dapat belajar. Pemberian kebutuhan tersebut dapat meningkatkan motivasi mereka dan hasil kerja mereka.
Tracking control of two degree of freedom parallel robots with adaptive bounds for the actual torque

Firstly, a further discussion on the modeling of the two degree of freedom parallel robots is carried out, some known formulas on the kinematics are modified and the analytical expressions of the reduced model are given. Secondly, two adaptive PD approaches with adaptive bounds on the actual torque vector or on the physical parameter vector are proposed for the tracking control, where the adaptive laws contain only one adaptive parameter to be tuned online. Finally, the experimental results are included to show the effectiveness of the proposed controllers.
A new object proposal generation method for object detection in RGB-D data

This paper proposes a modified selective search method that generates object proposals on RGB-D data in indoor scenes. The proposed method first applies color flattening to generate monotonous color variations in RGB image data. Then, from the color-flattened image and depth map data, cost function-based segment grouping and depth segmentation are applied to produce desirable segmentation results. Segment grouping using cost function on image data computes dissimilarities in color, texture, and size between two adjacent regions with pre-learned weights. Depth segmentation uses the height difference of grid cells in the binned depth grid map. The final set of object proposal regions extracted from the RGB image and depth map data is organized by considering the overlapping between two data modalities. Finally, the extracted set of object proposal regions is fed into AlexNet or VGG-16, both of which are widely used for object classification, to evaluate our method on object detection and classification tasks. The proposed segment-based method can precisely detect meaningful object regions using a smaller number of proposals than other methods. Further, its detection and classification performance are better than those of previous methods.
Eigensensing and deconvolution for the reconstruction of heat absorption profiles from photoacoustic tomography data

Photoacoustic tomography (PAT) is a relatively recent imaging modality that is promising for breast cancer detection and breast screening. It combines the high intrinsic contrast of optical radiation with acoustic imaging at submillimeter spatial resolution through the photoacoustic effect of absorption and thermal expansion. However, image reconstruction from boundary measurements of the propagating wave field is still a challenging inverse problem. Here we propose a new theoretical framework, for which we coin the term eigensensing, to recover the heat absorption profile of the tissue. One of the main features of our method is that there is no explicit forward model that needs to be used within a (usually) slow iterative scheme. Instead, the eigensensing principle allow us to computationally obtain several intermediate images that are blurred by known convolution kernels which are chosen as the eigenfunctions of the spatial Laplace operator. The source image can then be reconstructed by a joint deconvolution algorithm that uses the intermediate images as input. Moreover, total variation regularization is added to make the inverse problem well-posed and to favor piecewise-smooth images.