Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter-element dependency models for sequence classification

Naive Bayes is a fast to train model for sequence classification. We develop and experiment with two methods that are equally fast (they require only one pass through the training data), but they are also able to models interactions among close neighbours in the sequence (unlike the Naive bayes independence assumption). The first method basically runs Naive Bayes on overlapping k-grams obtained from the sequence. The second method, called NB(k), constructs a classifier associated with an undirected graphical model over the sequence that takes into account k-wise dependencies. We test our algorithms on protein function classification tasks based on functional families from the Gene Ontology (GO) database. Our results show significant improvements of the proposed methods over Naive Bayes with NB(k) leading over NB k-grams in all test cases. The two proposed methods despite having improved modelling accuracy and demonstrated improved test accuracy maintain the “one pass through the data only” training property of Naive Bayes thus yielding fairly accurate and efficient classifiers.
Think Process, Think in Time: Advancing Study of Informing Systems

The purpose of this article is to contribute to theorizing the process dimension of informing systems. The conceptualization draws on a framework called the Informing View of Organization and, particularly, on its segment of informing process. The article discusses the concept of informing process and its background in the process view of organization and explores the relationship between informing process and informing system on several examples. The main conclusion is that an informing system facilitates an initial positioning of a research (or practical) problem in systems terms, while the infoprocess stance enables dynamic and analytical view of connected activities that lead to business completion, modern technologies, and performance measurement. Parallels with complex systems theory are also demonstrated. Directions for further research are outlined.
ShareInsights: An Unified Approach to Full-stack Data Processing

The field of data analysis seeks to extract value from data for either business or scientific benefit. This field has seen a renewed interest with the advent of big data technologies and a new organizational role called data scientist. Even with the new found focus, the task of analyzing large amounts of data is still challenging and time-consuming. The essence of data analysis involves setting up data pipe-lines which consists of several operations that are chained together - starting from data collection, data quality checks, data integration, data analysis and data visualization (including the setting up of interaction paths in that visualization). In our opinion, the challenges stem from from the technology diversity at each stage of the data pipeline as well as the lack of process around the analysis. In this paper we present a platform that aims to significantly reduce the time it takes to build data pipelines. The platform attempts to achieve this in following ways. Allow the user to describe the entire data pipeline with a single language and idioms - all the way from data ingestion to insight expression (via visualization and end-user interaction). Provide a rich library of parts that allow users to quickly assemble a data analysis pipeline in the language. Allow for a collaboration model that allows multiple users to work together on a data analysis pipeline as well as leverage and extend prior work with minimal effort. We studied the efficacy of the platform for a data hackathon competition conducted in our organization. The hackathon provided us with a way to study the impact of the approach. Rich data pipelines which traditionally took weeks to build were constructed and deployed in hours. Consequently, we believe that the complexity of designing and running the data analysis pipeline can be significantly reduced; leading to a marked improvement in the productivity of data analysts/data scientists.
Relationship between Statin Utilization and Socioeconomic Deprivation in Hungary

The risk of premature mortality caused by cardiovascular diseases (CVDs) is approximately three times higher in the Central Eastern European region than in high income European countries, which suggests a lack and/or ineffectiveness of preventive interventions against CVDs. The aim of the present study was to provide data on the relationship between premature CVD mortality, statin utilization as a preventive medication and socioeconomic deprivation at the district level in Hungary. As a conceptually new approach, the prescription of statins, the prescription redemption and the ratio between redemption and prescription rates were also investigated. The number of prescriptions for statins and the number of redeemed statin prescriptions were obtained from the National Health Insurance Fund Administration of Hungary for each primary healthcare practice for the entire year of 2012. The data were aggregated at the district level. To define the frequency of prescription and of redemption, the denominator was the number of the 40+-year-old population adjusted by the rates of 60+-year-old population of the district. The standardized mortality rates, frequency of statin prescriptions, redeemed statin prescriptions, and ratios for compliance in relation to the national average were mapped using the “disease mapping” option, and their association with deprivation (tertile of deprivation index as a district-based categorical covariate) was defined using the risk analysis capabilities within the Rapid Inquiry Facility. The risk analysis showed a significant positive association between deprivation and the relative risk of premature cardiovascular mortality, and a reverse J-shaped association between the relative frequency of statin prescriptions and deprivation. Districts with the highest deprivation showed a low relative frequency of statin prescriptions; however, significantly higher primary compliance (redemption) was observed in districts with the highest deprivation. Our data clearly indicate that insufficient statin utilization is strongly linked to the so-called physician-factor, i.e., a statin prescription. Consequently, statin treatment is poor and represents a significant barrier to reducing mortality, particularly among people living in highly deprived areas of the country.
Exploiting shaper context to improve performance bounds of Ethernet AVB networks

New hard real-time Advanced Driver Assistance Systems such as the Collision-Avoidance System push the bandwidth requirements of the communication infrastructure to a new level. Controller Area Network (CAN) and FlexRay are reaching their limits. Ethernet-based automotive networks such as Ethernet AVB are capable of addressing these requirements. However, designing predictable Ethernet networks is more complex than the design of a traditional CAN bus. Formal real-time performance characteristics are key to a successful Ethernet integration. In this paper we present an improved Ethernet AVB performance analysis which exploits traffic-stream correlations. The results are significantly tighter compared to related work.
Estimation and Analysis of Facial Animation Parameter Patterns

We propose a framework for estimation and analysis of temporal facial expression patterns of a speaker. The proposed system aims to learn personalized elementary dynamic facial expression patterns for a particular speaker. We use head-and-shoulder stereo video sequences to track lip, eye, eyebrow, and eyelid motion of a speaker in 3D. MPEG-4 Facial Definition Parameters (FDPs) are used as the feature set, and temporal facial expression patterns are represented by the MPEG-4 Facial Animation Parameters (FAPs). We perform Hidden Markov Model (HMM) based unsupervised temporal segmentation of upper and lower facial expression features separately to determine recurrent elementary facial expression patterns for a particular speaker. These facial expression patterns coded by FAP sequences, which may not be tied with prespecified emotions, can be used for personalized emotion estimation and synthesis of a speaker. Experimental results are presented.
Environment Modeling Using Runtime Values for JPF-Android

Software applications are developed to be executed in a specific environment. This environment includes external/ native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified/abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on-the-y that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.
The Effects of Class-Wide Function-Related Intervention Teams in Three Art Classrooms

The Effects of Class-Wide Function-Related Intervention Teams in Three Art Classrooms Melanie April Nelson Department of Counseling Psychology and Special Education, BYU Educational Specialist Challenging and disruptive student behavior is a major concern for all teachers, including those who teach art. Unfortunately, there is a lack of research and resources available for art teachers to manage student behavior. School-wide positive behavior support (SWPBS) is a framework that has been shown to improve student behavior. Class-wide Function-Related Intervention Teams (CW-FIT) is an intervention that utilizes SWPBS principles including group contingency, social skills instruction, teacher praise, and positive reinforcement and has been shown to be effective in general education classrooms. This is the first study of CW-FIT in elementary art classrooms and examined the effects of the intervention on teacher praise-toreprimand rates and student on-task behavior in three classrooms. The first classroom utilized an AB design while the other two used a reversal (ABAB) design to evaluate impact. The results indicated the teacher was able to implement CW-FIT with fidelity, increase praise-to-reprimand ratios, and increase group on-task behavior. Finally, both the teacher and students found it to be socially valid. Limitations and implications of this study for researchers and practitioners are discussed.
Robust Transmission of Compressed Streams over Land Mobile Satellite Channel at Ku-Band

This paper presents a general framework for the efficient and robust transmission of compressed streams over land mobile satellite (LMS) channel at Ku-Band. Due to the extensive use of predictive coding, compressed data exhibit different sensitivities to transmission errors. Therefore, they are suitable for unequal error protection (UEP) techniques. UEP is mainly based on differentiated error control coding which raises the robustness of data, but also increases the transmission bandwidth. To cope with stringent satellite bandwidth restriction, wavelet packet orthogonally overlapped modulation with unequal power allocation (UPA) is used for UEP of data. UPA is driven by the sensitivities of data to LMS channel errors at Ku-Band.
Impact Of Channel Estimation Error On The Single-carrier Joint Tx / Rx MMSE-FDE

Abstract—In this paper, we discuss how the channel estimation (CE) error affects the bit error rate (BER) performance of singlecarrier (SC) block transmission using a previously proposed joint transmit/receive (Tx/Rx) frequency-domain equalization (FDE). First, the set of FDE weights is derived based on the minimum mean square error (MMSE) criterion by taking into account the CE error. The CE error is approximated as a zero-mean complex random Gaussian variable. We show by computer simulation that the proposed joint Tx/Rx MMSE-FDE is robust against the CE error. We consider pilot-assisted channel estimation (PACE) for joint Tx/Rx MMSE-FDE. A first order linear interpolation and extrapolation are introduced to achieve higher tracking ability against the time-variation of the channel gain. We discuss the impact of normalized Doppler frequency on the BER performance.
Kilohertz Quasi-periodic Oscillation Peak Separation Is Not Constant in the Atoll Source 4U 1608–52

We present new Rossi X-Ray Timing Explorer observations of the low-mass X-ray binary 4U 1608-52 during the decay of its 1998 outburst. We detect, by a direct fast Fourier transform method, the existence of a second kilohertz quasi-periodic oscillation (kHz QPO) in its power density spectrum, which was previously only seen by means of the sensitivity-enhancing "shift and add" technique. This result confirms that 4U 1608-52 is a twin kHz QPO source. The frequency separation between these two QPOs decreased significantly, from 325.5±3.4 to 225.3±12.0 Hz, as the frequency of the lower kHz QPO increased from 470 to 865 Hz, in contradiction with a simple beat-frequency interpretation. This change in the peak separation of the kHz QPOs is closely similar to that previously seen in Scorpius X-1 but takes place at a 10 times lower average luminosity. We discuss this result within the framework of models that have been proposed for kHz QPO. Beat-frequency models where the peak separation is identified with the neutron star spin rate, as well as the explanations previously proposed to account for the similar behavior of the QPOs in Sco X-1, are strongly challenged by this result.
Teachers’ Questions and Feedbacks in EFL Classrooms

Guided questions are crucial in teaching listening courses because they lead students to focus on the critical parts of the course. How teachers ask questions affects their students listening comprehension and output. The purpose of this study was to study the types of teacher questions and the feedback offered by teachers in two listening classes. Two teachers were selected as the subjects: one had taught English for seven years, and the other was a graduate student on an English language teaching master's program. Both subjects were teaching an English listening class as a teaching demonstration that was part of their graduate course. Three research questions were raised in this study: (1) What questioning models did the teachers use in the listening classes? (2) What types of questions were asked in these classes? and (3) What were the functions of the teachers’ feedback on students’ responses? The findings indicated that both teachers adopted a process approach in their teaching of listening classes. The questions that the experienced teacher asked with highest frequency were Wh-questions, whereas the intern asked Yes/No questions. The experienced teacher often used the feedback types of comprehension checking and expansion, whereas the intern often provided evaluations and used repetition in feedback.
Understanding and Identifying Advocates for Political Campaigns on Social Media

Social media is increasingly being used to access and disseminate information on sociopolitical issues like gun rights and general elections. The popularity and openness of social media makes it conducive for some individuals, known as advocates, who use social media to push their agendas on these issues strategically. Identifying these advocates will caution social media users before reading their information and also enable campaign managers to identify advocates for their digital political campaigns. A significant challenge in identifying advocates is that they employ nuanced strategies to shape user opinion and increase the spread of their messages, making it difficult to distinguish them from random users posting on the campaign. In this paper, we draw from social movement theories and design a quantitative framework to study the nuanced message strategies, propagation strategies, and community structure adopted by advocates for political campaigns in social media. Based on observations of their social media activities manifesting from these strategies, we investigate how to model these strategies for identifying them. We evaluate the framework using two datasets from Twitter, and our experiments demonstrate its effectiveness in identifying advocates for political campaigns with ramifications of this work directed towards assisting users as they navigate through social media spaces.
Minimal realization and dynamic properties of optimal smoothers

Smoothing algorithms of various kinds have been around for several decades. However, some basic issues regarding the dynamical structure and the minimal dimension of the steady-state algorithm are still poorly understood. In this paper, we derive a realization of minimal dimension of the optimal smoother for a signal admitting a state-space description of dimension n. It is shown that the dimension of the smoothing algorithm can vary from n to 2n, depending on the zero structure of the signal model. The dynamics (pole structure) of the steady-state smoother is also characterized explicitly and is related to the zero structure of the model. We use several recent ideas from stochastic realization theory. In particular, a minimal Markovian representation of the smoother is derived, which requires solving a nonsymmetric Wiener-Hopf factorization problem. In this way, the smoother is naturally expressed as the cascade of a whitening filter and a linear filter of least possible dimension, whose state space is a minimal Markovian subspace containing the smoothed estimate x/spl I.cap/. This, among other aspects, affords a very simple calculation of the error covariance matrix of the smoother. A reduced-order two-filter implementation of the type due to Mayne (1966) and Fraser (1967) is obtained by solving a Riccati equation of reduced dimension, which is in general smaller than the dimension of the Riccati equations considered in the literature.
Frequency Distribution of Mannose Binding Lectin-2 and Vitamin D Receptor Gene Variants: Putative Markers for Tuberculosis

Genetic polymorphism in Mannose Binding Lectin-2 (MBL-2) and Vitamin D Receptor (VDR) is known to influence the susceptibility to tuberculosis. The objective of the present study was to evaluate the frequency distribution of the MBL-2 promoter and structural polymorphism (−550 H/L, −221 Y/X, and +4 P/Q; R52C, G54D, and G57F) and VDR polymorphism (FokI, BsmI, TaqI, and ApaI) in healthy individuals of Indian population and comparative analysis with the global population. In Indian population, the frequency of VDR mutant alleles “f” for FokI, “b” for BsmI, “t” for TaqI, and “a” for ApaI was 25%, 54%, 30%, and 61%, respectively. The allelic frequency of MBL-2 promoter polymorphism −550 H/L was H versus L: 32% versus 68%, −221 Y/X was Y versus X: 68% versus 32%, and +4 P/Q was P versus Q: 78% versus 22%. Mutant allelic frequencies of the MBL-2 exon 1 D, B, and C allele were 6%, 11%, and 3%, respectively. Comparative analysis with global populations showed a noteworthy difference for MBL-2 and VDR polymorphism frequency distribution, indicating the ethnic variability of Indians. The study signifies the differential distribution of susceptibility genes in Indian population, which can influence the understanding of the pathophysiology of tuberculosis in Indian population.
Differential effects of emotionally versus neutrally cued autobiographical memories on performance of a subsequent cognitive task: effects of task difficulty

Attention is a limited resource, and in order to improve processing of the attended information, competing processes must be suppressed. Although it is well established that an experimentally induced change in mood state comprises one type of competing process that can impair performance on a subsequent task, no study has investigated whether an emotionally valenced autobiographical memory (AM) also can alter performance on a subsequent task. We therefore examined the effects of AM recall on cognitive performance. Healthy participants (n = 20 per experiment) recalled AMs in response to positive, negative, and neutral cue words. Following each AM participants completed a simple perceptual task (Experiment 1) or solved moderately difficult subtraction problems (Experiment 2). In Experiment 1 participants performed less accurately following exposure to positive or negative versus neutral cue words (ps < 0.001), and also were less accurate following negative versus positive cue words (p < 0.001). In Experiment 2, in contrast, no difference in accuracy or response times reached statistical significance. Performance accuracy even trended toward being higher following exposure to negative versus neutral cue words (p = 0.08). The results of Experiment 1 suggested that recalling emotionally salient AMs reduces the attention directed toward a simple continuous performance task administered immediately following the AM task, conceivably due to persistent contemplation of the AM. The negative results of Experiment 2 suggested that the effect of AMs on attention was attenuated, however, by increasing the difficulty of the subsequent task. Our results have implications for patients with major depressive disorder (MDD), as performing cognitively demanding tasks may allow them to attenuate the impairing effects of negative rumination on cognition.
Middleware support for real-time tasks on distributed and multicore platforms

While traditional real-time middleware such as Real-Time CORBA have shown promise as distributed software platforms for systems with time constraints, existing middle-ware systems lack (1) schedulability analysis and run-time enforcement mechanisms needed to give online real-time guarantees for aperiodic tasks, (2) flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different distributed real-time systems with both periodic and aperiodic tasks, and (3) support for scheduling soft real-time tasks on multicore platforms while guaranteeing their time constraints will be satisfied. To address the limitations of current generation real-time middleware, this dissertation makes three major contributions to the state of the art in real-time middleware: we have developed (1) the first instantiation of an integrated scheduling framework supporting both periodic and aperiodic tasks (on TAO, a widely used Real-Time CORBA middleware); (2) the first configurable component middleware services for admission control and load balancing of periodic and aperiodic tasks in distributed real-time systems (on CIAO, a Component-Integrated Real-Time middleware); and (3) MC-ORB, the first real-time object request broker (ORB) designed to exploit the features of multicore platforms, with admission control and task allocation services that can provide schedulability guarantees for soft real-time tasks on multicore platforms.