Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Enquiry into Automatically Orchestrated Live Video Communication in Social Settings

'Orchestration' refers to the ability of a live video communication system to adapt in real-time to the communication context with a view to enhance the quality of mediation and subsequently the quality of interaction between participants. For example, this can be done by reframing the cameras and changing the way in which the video content is mixed on each screen. To be a feasible solution, orchestration has to be an automatic process. This paper reports a study of orchestration carried out in the social setting of a group of friends playing social games from two separate living rooms. The quality of the communication was assessed via two measures: one objective, in the form of task efficiency, and one subjective, in the form of a questionnaire. The objective measure indicated that mediated communication can be improved through orchestration, but the subjective measure was inconclusive. The paper also uncovers some of the complexities of the experimental space associated with orchestrated mediated communication and aims to provide motivation for further research into this new communication paradigm.
Learning to understand parameterized commands through a human-robot training task

We propose a method to enable a robot to learn simple, parameterized commands, such as “Please switch on the TV! ” or “Can you bring me a coffee? “ for human-robot interaction. The robot learns through natural interaction with a user in a special training task. The goal of the training phase is to allow the user to give commands to a robot in his preferred way instead of learning predefined commands from a handbook. Learning is done in two successive steps. First the robot learns object names. Then it uses the known object names to learn parameterized command patterns and determine the position of parameters in a spoken command. The algorithm uses a combination of Hidden Markov Models and Classical Conditioning to handle alternative ways to utter the same command and integrate information from different modalities.
Is There a Relationship Between Obstructive Sleep Apnea (OSA) and Hearing Loss?

Background Obstructive sleep apnea (OSA) is a common disorder with an estimated prevalence in the general population of 2–5%. Its main clinical features are loud snoring and breathing stoppage during sleep. Ischemia could be a consequence of noise-induced hearing loss because cochlear oxygen tension is reduced during and after noise exposure. In this study, we evaluated auditory function in patients affected by OSA and simple snoring. Material/Methods A total of 66 participants (male to female ratio: 40:26) were included in the study, of which 21 were in the control group, 18 were in the simple snoring group, and 27 were in the OSA patient group. Polysomnography and audiometric examination were performed in all participants. Results The mean ages of the participants in the control, simple snoring, and OSA groups were 39.14±9.9, 37.28±8.2, and 41.56±8.99 years, respectively. There were no statistically significant differences among groups regarding age or sex; however, there were statistically significant differences among groups in body mass index, apnea-hypopnea index scores, mean saturation, and duration under 90% saturation. In addition, statistically significant differences were found between the patient group and the control and simple snoring groups concerning the mean saturation, duration under 90% saturation, and the extended high frequency of hearing. Conclusions These data show that snoring may cause hearing loss at extended high frequencies.
A goal- and dependency-directed algorithm for learning hierarchical task networks

This paper describes a system for learning domain-dependent knowledge in the form of goal-indexed Hierarchical Task Networks (HTNs). DLIGHT is a goal-directed incremental learning algorithm which observes solution traces and generates rules for solving problems. One of the main challenges in learning this kind of knowledge is determining a good level of generality. Analytical methods, such as explanation-based macro-operator learning, construct very specific structures that guarantee a successful execution when applicable but generalize poorly to new problems. Previous goal-directed learning approaches produce hierarchical rules with more relaxed preconditions, but the learned knowledge suffers from over-generality. Our approach builds on one such approach but it strikes a better balance between generality and specificity. This is done by carrying out a goal-dependency analysis to determine the structure of the hierarchy and precondition of each rule to follow the successful solutions more closely while maintaining generality. We hypothesize that this algorithm produces HTNs that generalize well and can solve problems efficiently. We evaluate the system's behavior experimentally in several planning scenarios and conclude with related work and future research paths.
An Investigation of Psychometric Measures for Modelling Academic Performance in Tertiary Education

Increasing college participation rates, and a more diverse student population, is posing a challenge for colleges in facilitating all learners achieve their potential. This paper reports on a study to investigate the usefulness of data mining techniques in the analysis of factors deemed to be significant to academic performance in first year of college. Measures used include data typically available to colleges at the start of first year such as age, gender and prior academic performance. The study also explores the usefulness of additional psychometric measures that can be assessed early in semester one, specifically, measures of personality, motivation and learning strategies. A variety of data mining models are compared to assess the relative accuracy of each.
The genome-wide expression response to telomerase deletion in Saccharomyces cerevisiae

Loss of the protective function of telomeres has previously been hypothesized to cause a DNA damage response. Here, we report a genome-wide expression response, the telomerase deletion response (TDR), that occurs when telomeres can no longer be maintained by telomerase. The TDR shares features with other DNA damage responses and the environmental stress response. Unexpectedly, another feature of the TDR is the up-regulation of energy production genes, accompanied by a proliferation of mitochondria. Finally, a discrete set of genes, the “telomerase deletion signature”, is uniquely up-regulated in the TDR but not under other conditions of stress and DNA damage that have been reported. The telomerase deletion signature genes define new candidates for involvement in cellular responses to altered telomere structure or function.
Embodied Knowledge – Embodied Memory

The distinction between representational and embodied knowledge (knowing-that versus knowing-how) has gained new significance through the investigation of implicit memory. This kind of memory is formed in the course of the interaction of organism and environment: Recurring patterns of interaction are sedimented in the form of sensorimotor, but also affect-motor schemes. We may speak of an implicit “body memory” that underlies our habits and skills, connecting body and environment through cycles of perception and action. This embodied knowledge is actualized by suitable situations or by overarching volitional acts, without necessarily being made explicit. The paper analyses the structure of embodied knowledge by taking the example of learning social skills through dyadic interactions in early childhood. It argues that the non-representational, enactive knowledge acquired in these interactions is the basis of intercorporeality and empathy. Explicit or propositional forms of knowing others (“theory of mind”) are derived from later steps of development; they are not sufficient for explaining the interactive and empathic human capacities. This will finally be illustrated by the example of autism.
Looking and Listening Patterns in 4- and 8- Month-Old Infants: Correspondence between Measures of Attention

The development of perceptual-cognitive processes during infancy has been traditionally studied using visual habituation and paired-comparison techniques. There has been extensive work within the field of infant attention that has focused on the development of visual attention. Within this field, it has been well established that there are two distinct classifications of infants’ visual behavior; infants with short visual fixations who perform well in a recognition task following familiarization and infants with long visual fixation with impaired performance. There are two hypotheses for the differences underlying these groups. First, that visual fixation duration is reflective of the speed of information processing such that long-looking infants process information more slowly than shortlooking infants. The second hypothesis is that infants who are long-looking have difficulty disengaging and shifting their attention to another location. There has not been any work exploring how these differences manifest themselves in other modalities. Thus, this project has three purposes: (1) to explore whether group differences emerge in an auditory recognition task similar to those found in the visual recognition phase of the paired-comparison task, (2) to better understand how performance differences in the visual task correspond to any observed differences in an auditory task, and (3) to identify any potential mechanisms which may account for the observed differences in group performance on an auditory task. Infants’ Looking and Listening Patterns iii Table of
MCMC and EM-based methods for inference in heavy-tailed processes with /spl alpha/-stable innovations

In this paper we present both stochastic and deterministic iterative methods for inference about random processes with symmetric stable innovations. The proposed methods use a scale mixtures of normals (SMiN) representation of the symmetric stable law to express the processes in conditionally Gaussian form. This allows standard procedures for dealing with the Gaussian case to be re-used directly as part of the scheme. In contrast with other recently published work on the topic, we propose a novel hybrid rejection sampling method for simulating the scale parameters from their full conditional distributions, making use of asymptotic approximations for the tail of a positive stable distribution when rejection rates are too high. This hybrid approach potentially leads to improved performance compared with straightforward rejection sampling or Metropolis-Hastings (M-H) approaches. The methods can be applied to any model with symmetric stable terms, but we illustrate their application to linear models and present simulations for AR time series with stable innovations.
Data Stream Subspace Clustering for Anomalous Network Packet Detection

As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer networks from external attacks, two common types of Intrusion Detection Systems (IDSs) are often deployed. The first type is signature-based IDSs which can detect intrusions efficiently by scanning network packets and comparing them with human-generated signatures describing previously-observed attacks. The second type is anomaly-based IDSs able to detect new attacks through modeling normal network traffic without the need for a human expert. Despite this advantage, anomaly-based IDSs are limited by a high false-alarm rate and difficulty detecting network attacks attempting to blend in with normal traffic. In this study, we propose a StreamPreDeCon anomaly-based IDS. StreamPreDeCon is an extension of the preference subspace clustering algorithm PreDeCon designed to resolve some of the challenges associated with anomalous packet detection. Using network packets extracted from the first week of the DARPA '99 intrusion detection evaluation dataset combined with Generic Http, Shellcode and CLET attacks, our IDS achieved 94.4% sensitivity and 0.726% false positives in a best case scenario. To measure the overall effectiveness of the IDS, the average sensitivity and false positive rates were calculated for both the maximum sensitivity and the minimum false positive rate. With the maximum sensitivity, the IDS had 80% sensitivity and 9% false positives on average. The IDS also averaged 63% sensitivity with a 0.4% false positive rate when the minimal number of false positives is needed. These rates are an improvement on results found in a previous study as the sensitivity rate in general increased while the false positive rate decreased.
Infrared observations of gravitational lensing in Abell 2219 with CIRSI

We present the first detection of a gravitational depletion signal at near-infrared wavelengths, based on deep panoramic images of the cluster Abell 2219 (z = 0.22) taken with the Cambridge Infrared Survey Instrument (CIRSI) at the prime focus of the 4.2-m William Herschel Telescope. Infrared studies of gravitational depletion offer a number of advantages over similar techniques applied at optical wavelengths, and can provide reliable total masses for intermediate-redshift clusters. Using the maximum-likelihood technique developed by Schneider, King & Erben, we detect the gravitational depletion at the 3 sigma confidence level. By modelling the mass distribution as a singular isothermal sphere and ignoring the uncertainty in the unlensed number counts, we find an Einstein radius of theta (E) = 13.7(-4.2)(+3.9) arcsec (66 per cent confidence limit). This corresponds to a projected velocity dispersion of sigma (upsilon) similar to 800 km s(-1), in agreement with constraints from strongly lensed features. For a Navarro, Frenk & White mass model, the radial dependence observed indicates a best-fitting halo scalelength of 125h(-1) kpc. We investigate the uncertainties arising from the observed fluctuations in the unlensed number counts, and show that clustering is the dominant source of error. We extend the maximum-likelihood method to include the effect of incompleteness, and discuss the prospects of further systematic studies of lensing in the near-infrared band.
Cross Sentence Inference for Process Knowledge

For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them. Our goal is to extract this kind of role-based knowledge about processes, from multiple sentence-level descriptions. This knowledge is hard to acquire; while semantic role labeling (SRL) systems can extract sentence level role information about individual mentions of a process, their results are often noisy and they do not attempt create a globally consistent characterization of a process. To overcome this, we extend standard within sentence joint inference to inference across multiple sentences. This cross sentence inference promotes role assignments that are compatible across different descriptions of the same process. When formulated as an Integer Linear Program, this leads to improvements over within-sentence inference by nearly 3% in F1. The resulting role-based knowledge is of high quality (with a F1 of nearly 82).
Rigged Configurations and Catalan, Stretched Parabolic Kostka Numbers and Polynomials: Polynomiality, Unimodality and Log-concavity

We will look at the Catalan numbers from the {\it Rigged Configurations} point of view originated \cite{Kir} from an combinatorial analysis of the Bethe Ansatz Equations associated with the higher spin anisotropic Heisenberg models . Our strategy is to take a combinatorial interpretation of Catalan numbers $C_n$ as the number of standard Young tableaux of rectangular shape $(n^2)$, or equivalently, as the Kostka number $K_{(n^2),1^{2n}}$, as the starting point of research. We observe that the rectangular (or multidimensional) Catalan numbers $ C(m,n)$ introduced and studied by P. MacMahon \cite{Mc}, \cite{Su1}, see also \cite{Su2}, can be identified with the Kostka number $K_{(n^m),1^{mn}}$, and therefore can be treated by Rigged Configurations technique. Based on this technique we study the stretched Kostka numbers and polynomials, and give a proof of `` a strong rationality `` of the stretched Kostka polynomials. This result implies a polynomiality property of the stretched Kostka and stretched Littlewood--Richardson coefficients \cite{KT}, \cite{Ras}, \cite{Ki1}. Another application of the Rigged Configuration technique presented, is a new family of counterexamples to Okounkov's log-concavity conjecture \cite{Ok}. Finally, we apply Rigged Configurations technique to give a combinatorial prove of the unimodality of the principal specialization of the internal product of Schur functions. In fact we prove a combinatorial formula for generalized $q$-Gaussian polynomials which is a far generalization of the so-called $KOH$-identity \cite{O}, as well as it manifests the unimodality property of the $q$-Gaussian polynomials.
Using Patient‐Reported Information to Improve Clinical Practice

Objective To assess what is known about the relationship between patient experience measures and incentives designed to improve care, and to identify how public policy and medical practices can promote patient‐valued outcomes in health systems with strong financial incentives. Data Sources/Study Setting Existing literature (gray and peer‐reviewed) on measuring patient experience and patient‐reported outcomes, identified from Medline and Cochrane databases; evaluations of pay‐for‐performance programs in the United States, Europe, and the Commonwealth countries. Study Design/Data Collection We analyzed (1) studies of pay‐for‐performance, to identify those including metrics for patient experience, and (2) studies of patient experience and of patient‐reported outcomes to identify evidence of influence on clinical practice, whether through public reporting or private reporting to clinicians. Principal Findings First, we identify four forms of “patient‐reported information” (PRI), each with distinctive roles shaping clinical practice: (1) patient‐reported outcomes measuring self‐assessed physical and mental well‐being, (2) surveys of patient experience with clinicians and staff, (3) narrative accounts describing encounters with clinicians in patients' own words, and (4) complaints/grievances signaling patients' distress when treatment or outcomes fall short of expectations. Because these forms vary in crucial ways, each must be distinctively measured, deployed, and linked with financial incentives. Second, although the literature linking incentives to patients experience is limited, implementing pay‐for‐performance systems appears to threaten certain patient‐valued aspects of health care. But incentives can be made compatible with the outcomes patients value if: (a) a sufficient portion of incentives is tied to patient‐reported outcomes and experiences, (b) incentivized forms of PRI are complemented by other forms of patient feedback, and (c) health care organizations assist clinicians to interpret and respond to PRI. Finally, we identify roles for the public and private sectors in financing PRI and orchestrating an appropriate balance among its four forms. Conclusions Unless public policies are attentive to patients' perspectives, stronger financial incentives for clinicians can threaten aspects of care that patients most value. Certain policy parameters are already clear, but additional research is required to clarify how best to collect patient narratives in varied settings, how to report narratives to consumers in conjunction with quantified metrics, and how to promote a “culture of learning” at the practice level that incorporates patient feedback.
Data modeling of time-based media

Many aspects of time-based media—complex data encoding, compression, “quality factors,” timing—appear problematic from a data modeling standpoint. This paper proposes timed streams as the basic abstraction for modeling time-based media. Several media-independent structuring mechanisms are introduced and a data model is presented which, rather than leaving the interpretation of multimedia data to applications, addresses the complex organization and relationships present in multimedia.
The study and the comparison of the trained and untrained staff from the perspectives of job satisfaction, organizational commitment and job involvement in Hegmatan Cement Company

The objective of the present study is the comparison of the trained and untrained staff from the perspective of job satisfaction, organizational commitment and job involvement. To test the study hypotheses, 130 individuals of Hegmatan Cement Company employees were selected based on a simple random method from among the staff population and among the selected sample volume 117 individuals, 62 trained and 55 untrained employees, answered to and returned the administered questionnaire. The applied questionnaires included the job description index (JDI) and Edward’s and Cleo-Patrick’s job involvement questionnaire and Allen’s and Mayer’s organizational commitment questionnaire. To analyze the data mean, standard deviation, t-test, Pierson correlation test and so on were taken advantage of. According to the study findings and results it was determined that there is a significant and positive difference between the trained and untrained staff regarding job satisfaction. There is a significant and positive difference between the trained and untrained staff regarding affective commitment. There is a significant and positive difference between the trained and untrained staff regarding job satisfaction. There was not found any statistically significant relationship between the trained and untrained staff regarding continuous commitment, there is a positive and significant difference between trained and untrained staff regarding normative commitment. Job involvement-related study hypothesis findings are indicative of a positive and significant difference between the trained and untrained staff regarding job involvement.