Miracle-dz/UniFAR-FTUs huggingface dataset:
ON THE LINEAR FILTERS FOR POINT SOURCE EXTRACTION OF THE PLANCK MISSION

The manifestation of point sources in the upcoming Planck maps is a direct reflection of the properties of the pixelized antenna beam shape for each frequency, which is related to the scan strategy, pointing accuracy, noise properties and map-making algorithm. In this paper we firstly compare analytically two filters for the Planck point source extraction, namely, the adaptive top-hat filter (ATHF) and the theoretically-optimal filter (TOF). Our analyses are based on the premise that the required experiment parameters of the TOF are assumed to be already known: the CMB and noise power spectrum and a circular Gaussian beam shape and size. Whereas, the ATHF does not need any a priori knowledge. The analyses show that the TOF is optimal in terms of the gain after the parameter inputs. We simulate the Planck HFI 100 GHz channel with elliptical beam in rotation to test the efficiency of the TOF and the ATHF. We also apply the ATHF on the WMAP Q-band map and the derived map (the foreground-cleaned map by Tegmar.
A Narrative Policy Analysis of the German National Qualification Framework (DQR) discourse surrounding non-formal qualifications

After integrating also general schooling into the DQR in August 2017, the next pending debate is about the integration of non-formal qualifications into the DQR. Despite a consensus of involved actors to integrate non-formal qualifications into the DQR, the process stalled for a long time. This thesis gives an insight on the development of the DQR discourse surrounding non-formal qualification, by analyzing the internal argumentations and conflict lines through the policy narratives used in the discourse. Policy narratives can be an important factor to improve the persuasiveness of arguments, especially in complex and conflictual settings as the DQR negotiations. Hence, utilizing the analytical framework of the Narrative Policy Framework, this thesis identified the three dominant narratives of the DQR discourse surrounding non-formal qualifications – the egalitarian narrative, the hierarchical narrative, and the individualistic narrative. The analysis of these narrative displayed the dominant argumentation patterns of the DQR discourse, how the narratives are structured, and identified possible policy solutions concerning the integration of non-formal qualifications. This thesis also contributes to the small but growing basis of qualitative NPF literature, and extended the NPF’s field of application to education policy issues. (Less)
Experimental Investigation of Five-Phase Induction Motor Drive Using Extended Kalman-Filter

In this paper extended Kalman filter (EKF) is reviewed for estimating the rotor position/speed of a vector controlled five-phase induction motor drive. The basic configuration of the Kalman filter is studied and the system vectors and matrices are explained. The EKF equations are made from a d-q-axis model of the five-phase induction motor by considering the rotor speed as a state variable. The simulation and experimental results show that the EKF is capable of tracking the actual rotor speed. Care should be taken in the selection of elements of the covariance matrices. The performance of the EKF is acceptable even in the presence of noise.
Effective loss reduction analysis in new switched-capacitor boost-multilevel inverter using series/parallel conversion

An switched capacitor multi-level inverter gives higher voltages than the input dc voltage with help of series-parallel conversion operation. In this paper we are finding out from the various methods of multi-level inverters to reduce total harmonic distortion (THD) voltage in bus output of inverter as well as reduction of switching frequency ultimately overall improvement in efficiency of inverter. Also this inverter can reduce need of input step up dc voltage converter system as well as output side transformer or inductors to reduction of overall size of inverter. All results are obtained using MATLAB (SIMULINK) also theoretical results are calculated and verified with simulated results.
Conversational scene analysis

In this thesis, we develop computational tools for analyzing conversations based on nonverbal auditory cues. We develop a notion of conversations as being made up of a variety of scenes: in each scene, either one speaker is holding the floor or both are speaking at equal levels. Our goal is to find conversations, find the scenes within them, determine what is happening inside the scenes, and then use the scene structure to characterize entire conversations. We begin by developing a series of mid-level feature detectors, including a joint voicing and speech detection method that is extremely robust to noise and microphone distance. Leveraging the results of this powerful mechanism, we develop a probabilistic pitch tracking mechanism, methods for estimating speaking rate and energy, and means to segment the stream into multiple speakers, all in significant noise conditions. These features gives us the ability to sense the interactions and characterize the style of each speaker's behavior. We then turn to the domain of conversations. We first show how we can very accurately detect conversations from independent or dependent auditory streams with measures derived from our mid-level features. We then move to developing methods to accurately classify and segment a conversation into scenes. We also show preliminary results on characterizing the varying nature of the speakers' behavior during these regions. Finally, we design features to describe entire conversations from the scene structure, and show how we can describe and browse through conversation types in this way. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Development of an Integrated Platform for Social Collaborations Based on Semantic Peer Network

In this paper we investigate the benefits from using the collaboration services in two case studies: e-learning and industrial knowledge sharing. In the first case, learners can not only be consumers of course material but also annotate her comments, post questions, and retrieve answers on any portion of the material when reading it. On the other hand, instructors not only play the role of content providers but also listeners of the feedback about course material from learners. Because of the metadata in RDF, user can get aggregation services In the second case, we develop industrial domain schema that provides vocabulary for user to describe in more detail their resources to be shared. On the other hand, user can find out resources that are close to the things in her mind using the same set of vocabulary. In addition to finding out resources, user can also rank the found resources using the trust management facilities to determine whether to carry out further downloading.
A Learning Algorithm for Bayesian Networks and Its Efficient Implementation on GPUs

The wide application of omics research has produced a burst of biological data in recent years, which has in turn increased the need to infer biological networks from data. Learning biological networks from experimental data can help detect and analyze aberrant signaling pathways, which can be used in diagnosis of diseases at an early stage. Most networks can be modeled as Bayesian networks (BNs). However, because of its combinatorial nature, computational learning of dependent relationships underlying complex networks is NP-complete. To reduce the complexity, researchers have proposed to use Markov chain Monte Carlo (MCMC) methods to sample the solution space. MCMC methods guarantee convergence and traversability. However, MCMC is not scalable for networks with more than 40 nodes because of the computational complexity. In this work, we optimize an MCMC-based learning algorithm and implement it on a general-purpose graphics processing unit (GPGPU). We achieve a 2.46× speedup by optimizing the algorithm and an additional 58-fold acceleration by implementing it on a GPU. In total, we speed up the algorithm by 143×. As a result, we can apply this system to networks with up to 125 nodes, a size that is of interest to many biologists. Furthermore, we add artificial interventions to the scores in order to incorporate prior knowledge of interactions into the Bayesian inference, which increases the accuracy of the results. Our system provides biologists with a more computational efficient tool at a lower cost than previous works.
NUFFT-based imaging of vegetation on graphic cards

We present an algorithm for the fast tomography of vegetation, based on a Radon mathematical setting and on the combined use of advanced processing algorithms (Non-Uniform FFTs) and hardware resources (Graphic Processing Units - GPUs). The algorithm performance is firstly numerically estimated, showing the favorable trade off between faithfulness and speed, and highlighting the convenience of the GPU implementation against that on CPU. Experimental results on anechoic chamber data complete the algorithm assessment.
Recommend a profitable cruising route for taxi drivers

Every time after the taxi drivers drop off their previous passengers, they have to decide how to search for the next passengers. It may lead to poor performance if the taxi drivers cruise according to their own experience, especially for the freshmen. Therefore, it is valuable to recommend a profitable cruising route for the taxi drivers in order to increase their income and reduce waste in fuel. In this paper, we propose to use a system of linear equations to calculate the score of each road segment based on a large-scale real-world GPS data set. The score of each road segment consists of 1) the total income of the road segment and 2) the attractiveness of the drop-off location with respect to the next pick-up. Then, we get the profitable cruising route based on the score of each road segment using skyline computation. We build our system using historical data set generated by 12,000 taxis of Beijing in November 2012. Finally, we demonstrate that taxi driver who follows our recommendation can enhance their income compared to the ground truth.
Feasability of Combined Area and Performance Optimization for Superscalar Processors Using Searching

When designing embedded systems, one needs to make decisions concerning the different components that will be included in a microprocessor. An important issue is the chip area vs. performance trade-off. In this paper we investigate the relationship between chip area and performance for superscalar microprocessors. We investigate the feasability to obtain a suitable configuration by searching. We show that our approach gives a good configuration after 100 to 150 iterations using a simple random search algorithm. This shows the feasibility of our approach, in particular when more sophisticated search algorithms are employed as we plan in future work.
Establishment of Entrepreneurial Character in the Foundation based School System through Project Based Learning

Establishing entrepreneurial character is pretty much essential to be taught in educational institution. This could even be given from the elementary level of education since it is a lifelong learning of an individual. The purpose of this research is to analyze the establishment process of entrepreneurial character in students through the implementation of learning activities based on project. Research is based on interesting phenomena, creativity, innovation, and self-confidence in taking risks in students in the entrepreneurial learning process that has to do with product creation and entrepreneurship in schools starting from elementary education to secondary education. This research was conducted with qualitative methods using snowball sampling with data collection techniques of observation, interviews, documentation and also data triangulation to validate the collected data. Data analysis used analysis techniques from Spradley through domain analysis and semantic relationships. The results showed that the process of learning activities based on the project can shape the character of students' entrepreneurship according to the level of education. Characters in elementary school students were independent, creative, honest and disciplined. Junior high school formed entrepreneurial characters such as: Self-sufficient, creative, responsibility, cooperation, hard work, risk-taking and communicative. While high school students formed characters of self-sufficient, creative, responsibility, cooperation, hard work, brave to take risks and communicative, leadership and success-oriented.
Antecedents and Consequents of Information Usefulness in User-generated Online Reviews: A Multi-group Moderation Analysis of Review Valence

Online reviews have become a critical component of consumers’ Web-based search queries and help them minimize uncertainty and risk associated with purchase decisions. Not only do customers perceive online reviews to be more “real”, but also online reviews enable opportunities for interactivity between consumers, which makes them a popular source of information when consumers make (online) purchase decisions. In this study, we examine the impact of online reviews on consumers’ beliefs, brand attitudes, and purchase intention by theoretically extending the information adoption model (IAM) with constructs from consumer research. To do so, we used data from a scenariobased online experiment and manipulated three review characteristics (currency, accuracy, and credibility) using carefully selected TripAdvisor reviews. Using a partial-least squares approach (PLS) to structural equation model (SEM), we found strong empirical support for our hypotheses that review quality and reviewer credibility drive information usefulness and that information usefulness, in turn, drives consumers’ attitudes toward and their intention to purchase from a brand. Using PLS multi-group analysis, we further explored the moderating role of review valence—positive versus negative—and found significant differences in the importance of the drivers of information usefulness and its consequents. We discuss our study’s implications for theory and practice.
A solution to support risk analysis on IT Change Management

The growing necessity of organizations in using technologies to support to their operations implies that managing IT resources became a mission-critical issue for the health of the primary companies' businesses. Thus, in order to minimize problems in the IT infrastructure, possibly affecting the daily business operations, risks intrinsic to the change process have to be analyzed and assessed. Risk Management is a widely discussed subject in several areas, although for IT Change Management it is quite a new discipline. The Information Technology Infrastructure Library (ITIL) introduces a set of best practices to conduct the management of IT infrastructures. According to ITIL, risks should be investigated, measured, and mitigated before any change is approved. Even with these guidelines, there is no default automatic method for risk assessment in IT Change Management. In this paper we introduce a risk analysis method based on the execution history of past changes. In addition, we propose a failure representation model to capture the feedback of the execution of changes over IT infrastructures.
Large scale Photovoltaic structures with medium-frequency isolation

Energy generation via Photovoltaic (PV) technologies has increased in the last years. Large scale PV generation have attracted interest among countries due to low operation cost and wide PV resource disposition. However, some operative challenges in large scale PV plants had been detected: power imbalance, low power quality in terms of total harmonic distortion (THD), power factor and conversion efficiency, can be mentioned. This paper is focused on topologies featuring medium-frequency (MF) isolation and presents a review of different approaches to solve those operative issues. Medium-frequency isolation reduces current and improves efficiency, reliability, and provides isolated DC sources for power electronics converters, and can replace bulky low-frequency transformers, thus reducing PV system volume. Simulations of three-phase 10 kW PV systems are presented: MF link PV system, Delta inverter and a PV system based on a matrix converter.
An Evaluation (and Vindication?) of Lewis Terman: What the Father of Gifted Education Can Teach the 21st Century

Lewis Terman is widely seen as the “father of gifted education,” yet his work is controversial. Terman’s “mixed legacy” includes the pioneering work in the creation of intelligence tests, the first large-scale longitudinal study, and the earliest discussions of gifted identification, curriculum, ability grouping, acceleration, and more. However, since the 1950s, Terman has been viewed as a sloppy thinker at best and a racist, sexist, and/or classist at worst. This article explores the most common criticisms of Terman’s legacy: an overemphasis on IQ, support for the meritocracy, and emphasizing genetic explanations for the origin of intelligence differences over environmental ones. Each of these criticisms is justified to some extent by the historical record, and each is relevant today. Frequently overlooked, however, is Terman’s willingness to form a strong opinion based on weak data. The article concludes with a discussion of the important lessons that Terman’s work has for modern educators and psychologists, including his contributions to psychometrics and gifted education, his willingness to modify his opinions in the face of new evidence, and his inventiveness and inclination to experiment. Terman’s legacy is complex, but one that provides insights that can enrich modern researchers and practitioners in these areas.
Prediction of the Number of Helices for the Twilight Zone Proteins

Protein structure prediction is one of the core research areas in bioinformatics. This paper addresses the protein secondary structure prediction problem for the twilight zone proteins, which are characterized by low, about 25% homology to the sets of known sequences. The commonly used sequence alignment based algorithms fail to provide accurate prediction for sequences of such low homology, and thus alternative solutions should be sought. We propose a novel method that aims at the prediction of the number of helical structures based on the twilight zone protein sequences. The method is based on a custom designed and compact feature based sequences representation and applies a decision tree prediction algorithm. The performed experimental study shows superiority of the proposed method over three other prediction algorithms and the results provided by YASPIN algorithm, which is a state-of-the-art alignment based secondary structure prediction method designed using low homology sequences
i-pIDtune: An interactive tool for integrated system identification and PID control

Abstract This paper describes i-pIDtune, an interactive software tool that integrates system identification and PID controller design. The tool supports experimental design and execution under plant-friendly conditions, high-order ARX estimation, and control-relevant model reduction leading to models that comply with the IMC-PID tuning rules. All these stages are depicted simultaneously and interactively in one screen. Thus, i-pIDtune allows to display both open- and closed-loop responses of the estimated models and important control-relevant validation criteria, what enables the user to readily assess how design variable choices, control performance requirements and model error can impact the achievable closed-loop performance from a restricted complexity model estimated under noisy conditions.
Learning Riemannian Metrics

We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relative volume of a given set of points. We derive the details for a family of metrics on the multinomial simplex. The resulting metric has applications in text classification and bears some similarity to TFIDF representation of text documents.
Do cognitive appraisals moderate the link between qualitative job insecurity and psychological-behavioral well-being?

Purpose The purpose of this paper is to detect the association between qualitative job insecurity and well-being related outcomes and to determine the extent to which cognitive appraisals of job insecurity moderate this association. According to appraisal theory, it is anticipated a hindrance appraisal of job insecurity to amplify and a challenge appraisal of job insecurity to buffer this association. Design/methodology/approach To test the hypotheses, 250 healthcare employees from different departments of an Iranian large public hospital were recruited. Participants responded to scales on qualitative job insecurity, cognitive appraisals, job satisfaction, emotional exhaustion, absenteeism and presenteeism. Findings Results showed that qualitative job insecurity negatively influenced both psychological and behavioral well-being; however, this influence was greater for psychological well-being than for behavioral well-being. Besides, the moderation tests showed that only the hindrance appraisals of job insecurity amplified the link between job insecurity and psychological outcomes. Research limitations/implications This study sampled employees from a public hospital and did not include employees from private hospitals. This may limit the generalizability of the findings. Also, due to using a cross-sectional research design we encourage future studies to replicate the same findings using other different research designs. Practical implications The findings aid occupational health psychologists to design particular interventions for protecting those aspects of employee’s well-being that are more vulnerable when qualitative job insecurity is chronically perceived. Originality/value Together, these findings suggest that the hindrance appraisals of qualitative job insecurity are more likely to moderate the link between job insecurity and well-being outcomes.
Learning speech emotion features by joint disentangling-discrimination

Speech plays an important part in human-computer interaction. As a major branch of speech processing, speech emotion recognition (SER) has drawn much attention of researchers. Excellent discriminant features are of great importance in SER. However, emotion-specific features are commonly mixed with some other features. In this paper, we introduce an approach to pull apart these two parts of features as much as possible. First we employ an unsupervised feature learning framework to achieve some rough features. Then these rough features are further fed into a semi-supervised feature learning framework. In this phase, efforts are made to disentangle the emotion-specific features and some other features by using a novel loss function, which combines reconstruction penalty, orthogonal penalty, discriminative penalty and verification penalty. Orthogonal penalty is utilized to disentangle emotion-specific features and other features. The discriminative penalty enlarges inter-emotion variations, while the verification penalty reduces the intra-emotion variations. Evaluations on the FAU Aibo emotion database show that our approach can improve the speech emotion classification performance.
Care Coordination and Medicaid Managed Care: Emerging Issues for States and Managed Care Organizations. Princeton, NJ: Mathematica Policy Research

This policy brief examines the potential of care coordination, an innovative new approach to comprehensive care for patients with disabilities and special needs under Medicaid managed care, noting that it takes time to develop but can be put in place even after a state has implemented Medicaid managed care. Also discusses the differences between care coordination and case management and notes that creative problem solving, through advocacy, is emerging as an important new role for care coordinators serving this population.