Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a scale measuring the psychic income associated with hosting the Olympic Games

Purpose The purpose of this paper is to develop a scale to measure the legacy of psychic income associated with the Olympic Games. Design/methodology/approach Using the 2008 Beijing Games as an example, data were collected from Beijing residents through structured questionnaires. A scale of measuring psychic income (SPI) was developed through conducting a confirmatory factor analysis (CFA) based on a sample of 375. Findings In the CFA estimation, a seven-factor SPI was identified with 24 pertinent items retained. This seven-factor model displays good fit to the data, construct validity, and reliability. Originality/value Despite the importance of psychic income widely recognized in the existing literature, there has been a lack of valid scales to measure major sports events psychic income in general and Olympic psychic income in particular. This paper develops a multidimensional scale from the host community perspective, which can provide academics and local organizers with a reliable and valid tool to assess Olympic psychic income.
A Novel Approach for Semi Supervised Document Clustering with Constraint Score based Feature Supervision

Text document clustering provides an effective technique to manage a huge amount of retrieval outcome by grouping documents in a small number of meaningful classes. In unsupervised clustering method the unlabeled input data is used to estimate the parameter values. In a semi supervised document clustering both labeled and unlabeled input data is used for document clustering. A semi supervised clustering with feature supervision and constraint score is proposed in this paper. This proposed system which handles document clustering and feature Supervision simultaneously and this system finds the number of clusters automatically. Feature supervision uses pairwise constraints that performs supervision between the each documents. The semisupervised constraint score that uses both pairwise constraints and the constraint score is to compute relevant features and irrelevant feature on document data set. A variational inference algorithm uses the Dirichlet Process Mixture model for the document clustering.
A robust approach to optimum widely linear MVDR beamformer

In many array processing, the received signals are nonstationary, or in particular, noncircular. Widely linear minimum variance distortionless response (WL MVDR) beamformers can exploit the noncircularity of received signals and improve the performance of the conventional MVDR beamformer. However, in the optimum WL MVDR beamformer, the array steering vector (ASV) and the signal noncircularity coefficient should be known a priori for the signal of interest. This requirement puts strict limitation to the implementation of this beamformer. We therefore in this paper propose a robust approach to the optimal WL MVDR beamformer that can deal with the uncertainties in the ASV and noncircularity coefficient. Two variants of the proposed approach are developed based on the treatment of the uncertainties. By doing so, the requirement on the exact information is relaxed while the performance improvement can still be obtained. Simulation studies are also provided to illustrate the performance of the proposed approach.
Model and Methodology for the Synthesis of Heterogeneous and Partially Reconfigurable Systems

When reconfigurable devices are used in modern embedded systems and their capability to adapt to changing application requirements becomes an issue, comprehensive modeling and design methods are required. Such methods must respect the whole range of functionality of the reconfigurable fabrics. In particular, the heterogeneity and reconfiguration delay of modern FPGAs are important details. Comprehensive methods to exploit these characteristics within the integrated design of embedded systems are still not available. In this paper, we introduce a synthesis methodology for reconfigurable systems that respects the specific requirements of run-time reconfiguration. The methodology bases on profound concepts, and expands known notations and model techniques.
JENNA: A Jamming Evasive Network-Coding Neighbor-Discovery Algorithm for Cognitive Radio Networks

In this paper we address the problem of neighbor discovery in cognitive radio networks. Cognitive radios operate in a particularly challenging wireless environment. In such an environment, besides the strict requirements imposed by the opportunistic co-existence with licensed users, cognitive radios may have to deal with other concurrent (either malicious or selfish) cognitive radios which aim at gaining access to most of the available spectrum resources with no regards to fairness or other behavioral etiquettes. By taking advantage of their highly flexible radio devices, they are able to mimic licensed users behavior or simply to jam a given channel with high power. This way these concurrent users (jammers) are capable of interrupting or delaying the neighbor discovery process initiated by a normal cognitive radio network which is interested in using a portion of the available spectrum for its own data communications. To solve this problem we propose a Jamming Evasive Network-coding Neighbor-discovery Algorithm (JENNA) which assures complete neighbor discovery for a cognitive radio network in a distributed and asynchronous way. We compare the proposed algorithm with baseline schemes that represent existing solutions, and validate its feasibility in a single hop cognitive radio network.
Adaptation to Shift Work: Physiologically Based Modeling of the Effects of Lighting and Shifts’ Start Time

Shift work has become an integral part of our life with almost 20% of the population being involved in different shift schedules in developed countries. However, the atypical work times, especially the night shifts, are associated with reduced quality and quantity of sleep that leads to increase of sleepiness often culminating in accidents. It has been demonstrated that shift workers’ sleepiness can be improved by a proper scheduling of light exposure and optimizing shifts timing. Here, an integrated physiologically-based model of sleep-wake cycles is used to predict adaptation to shift work in different light conditions and for different shift start times for a schedule of four consecutive days of work. The integrated model combines a model of the ascending arousal system in the brain that controls the sleep-wake switch and a human circadian pacemaker model. To validate the application of the integrated model and demonstrate its utility, its dynamics are adjusted to achieve a fit to published experimental results showing adaptation of night shift workers (n = 8) in conditions of either bright or regular lighting. Further, the model is used to predict the shift workers’ adaptation to the same shift schedule, but for conditions not considered in the experiment. The model demonstrates that the intensity of shift light can be reduced fourfold from that used in the experiment and still produce good adaptation to night work. The model predicts that sleepiness of the workers during night shifts on a protocol with either bright or regular lighting can be significantly improved by starting the shift earlier in the night, e.g.; at 21∶00 instead of 00∶00. Finally, the study predicts that people of the same chronotype, i.e. with identical sleep times in normal conditions, can have drastically different responses to shift work depending on their intrinsic circadian and homeostatic parameters.
Prevalence and temporal pattern of hospital readmissions for patients with type I and type II diabetes

Objective Repeated hospitalisation for patients is common and costly, yet partly preventable. However, we know little about readmissions for patients with diabetes in China. The current study aims to assess the frequency and temporal pattern of and risk factors for all-cause readmission among hospitalised patients with diabetes in Tianjin, China. Method This retrospective, cohort analysis used the Tianjin Basic Medical Insurance Register System data of 2011. The patterns of and the reasons for all-cause readmissions for patients with diabetes were described. The differences of readmission-free survival (RFS) between newly and previously diagnosed patients were compared. Time-dependent Cox models were established to identify the risk factors for readmission at different time intervals after discharge. Results Readmission rates were approximately 30%, with the most common diagnoses of cerebral infarction (for type I) or diabetes (for type II) for patients with diabetes. The majority of patients were readmitted to the hospital after more than 90 days, followed by 8–30 days (all p=0.002). Approximately 37.2% and 42.8% of readmitted patients with type I and type II diabetes were diagnosed previously, and the RFS rates for previously diagnosed patients were significantly lower than for newly diagnosed patients at any time interval after discharge. Prior history of diabetes (all p<0.05), length of stay (all p<0.01) and reimbursement ratio (90% vs >92%, all p<0.0002) were consistently associated with the RFS for patients readmitted to the hospital at <7, 8–30, 31–60 and 61–90 days. Conclusions Hospital readmissions among patients with diabetes were affected by the diagnosis status. Patient characteristics and the quality of healthcare might regulate short-interval and long-interval hospital readmission, respectively, after discharge.
A Distributed Adaptive Control Architecture for Autonomous Agents

Recently considerable interest in behavior-based robots has been generated by industrial, space and defence related activities. Such independent robots are envisioned to perform tasks where safety or economic factors prevent direct human control and communication difficulties prevent easy remote control. Although many successes have been reported using behavior-based robots with prespecified skills and behaviors, it is clear that there are many more applications where learning and adaptation are required. In this research, a method whereby reinforcement learning can be combined into a behavior based control system is presented. Skills and behaviors which are impossible or impractical to embed as predetermined responses are learned by the robot through exploration and discovery using a temporal difference reinforcement learning technique. This results in what is referred to as a Distributed Adaptive Control System (DACS), effect the robot’s artificial nervous system. Presented in this paper is only a general overview of the DACS architecture with many details neglected. A DACS is then developed for a simulated quadruped mobile robot. The locomotion and body coordination behavioral levels are isolated and evaluated.
A search for distant radio galaxies from SUMSS and NVSS – III. Radio spectral energy distributions and the z–α correlation

This is the third in a series of papers that present observations and results for a sample of 76 ultrasteep-spectrum radio sources designed to find galaxies at high redshift. Here we present multifrequency radio observations, from the Australia Telescope Compact Array, for a subset of 37 galaxies from the sample. Matched resolution observations at 2.3, 4.8 and 6.2 GHz are presented for all galaxies, with the z < 2 galaxies additionally observed at 8.6 and 18 GHz. New angular size constraints are reported for 19 sources based on high-resolution 4.8- and 6.2-GHz observations. Functional forms for the rest-frame spectral energy distributions are derived: 89 per cent of the sample is well characterized by a single power law, whilst the remaining 11 per cent show some flattening towards higher frequencies: not one source shows any evidence for high-frequency steepening. We discuss the implications of this result in light of the empirical correlation between redshift and spectral index seen in flux-limited samples of radio galaxies. Finally, a new physical mechanism to explain the redshift - spectral index correlation is posited: extremely steep-spectrum radio galaxies in the local universe usually reside at the centres of rich galaxy clusters. We argue that if a higher fractions of radio galaxies, as a function of redshift, are located in environments with densities similar to nearby rich clusters, then this could be a natural interpretation for the correlation. We briefly outline our plans to pursue this line of investigation.
An efficient image indexing algorithm in JPEG compressed domain

Considering the fact that majority of images stored in computers, such as those from the Internet, are in JPEG compressed format, it would be highly desirable to have the content-based image indexing and retrieval conducted directly in the compressed domain. In this paper, we present a partial decoding algorithm to index images directly in the compressed domain for all JPEG compressed images. Following that, an empirical study is also carried out to compare the performance of such approaches in DCT domain and that of original images in the pixel domain. This technology will prove invaluable in applications where fast image key generation is required such as on the Internet or embedded in future imaging equipment. This is necessary as not only the amount of media available on the Internet will continue to grow but also the amount stored in homes-on digital televisions, computers, cameras and media-will grow.
Multitaper estimation of the coherence spectrum in low SNR

A pseudo coherence estimate using multitapers is presented. The estimate has better localization for sinusoids and is shown to have lower variance for disturbances compared to the usual coherence estimator. This makes it superior in terms of finding coherent frequencies between two sinusoidal signals; even when observed in low SNR. Different sets of multitapers are investigated and the weights of the final coherence estimate are adjusted for a low-biased estimate of a single sinusoid. The proposed method is more computationally efficient than data dependent methods, and does still give comparable results.
Sampling based bundle adjustment using feature matches between ground-view and aerial images

This paper proposes a new pipeline of Structure-from-Motion that uses feature matches between ground-view and aerial images for removing accumulative errors. In order to find good matches from unreliable matches, we newly propose RANSAC based outlier elimination methods in both feature matching and bundle adjustment stages. To this end, in the feature matching stage, the consistency of orientation and scale extracted from images by a feature descriptor is checked. In the bundle adjustment stage, we focus on the consistency between estimated geometry and matches. In experiments, we quantitatively evaluate performances of the proposed feature matching and bundle adjustment.
Moving pictures: from ethnographic to autoethnographic documentary in the internationalization of the geography curriculum

Abstract Documentary films have often taken a pivotal role in strategies to internationalize (geography) curricula and classrooms, being used as a method of bringing the world to the classroom. These documentaries overwhelmingly take ethnographic form. Problematically, the documentary gaze is characteristically that of an outside film crew and narrator mediating relationships between the “subjects” of the documentary and the ways they are heard and seen. Yet other forms of documentary also exist, including those offering autoethnographic perspectives to viewers. Autoethnographic documentaries offer a highly promising resource for internationalization of the geography curriculum, providing careful, analytic, theoretically-informed understandings of documentarians’ own worlds – which may be in the next neighbourhood or on the other side of the planet. This paper reviews documentaries as curriculum-internationalizing learning-and-teaching resources before going on to examine the flaws of “traditional” ethnographic documentary in this endeavour. It makes the case for greater – though not necessarily exclusive – use of autoethnographic films in our work to educate global citizens and provides some preliminary resources for locating and evaluating this form of film. As such it contributes not only to critical pedagogy surrounding internationalization of the geography curriculum but also to filmic geography.
Extending . NET Remoting with Distributed Garbage Collection

The memory management of distributed objects, when done manually, is an error-prone task. It leads to memory leaks and dangling references causing applications to fail. Avoiding such errors requires automatic memory management, named distributed garbage collection (DGC). Current DGC solutions are either not safe, not complete or not portable to widely used platforms such as . NET. As a matter of fact, most solutions either run on specialized environments or require modifications of the underlying virtual machine (e.g. Rotor) hindering its immediate utilization. This paper describes the architecture, implementation and performance measurements of a DGC algorithm that: i) is capable of reclaiming both acyclic and cyclic garbage, while ii) being portable in the sense that it does not require the underlying virtual machine to be modified. The distributed garbage collector was implemented on top of two realizations of the Common Language Infrastructure (.NET virtual machine specification): Common Language Runtime (CLR) and Shared Source CLI (SSCLI), commonly known as Rotor. The implementation requires no modification of the environment, it makes use of the aspect-oriented functionalities provided, and the preliminary results are encouraging.
Joint Annotation and Knowledge Building in Collaborative E-Learning

Tools to enable joint annotation help in collaborative knowledge building in elearning. EDUCOSM is a system that offers two types of annotation, namely highlights and comments, on any Web-page that are visible to other learners immediately. The tool is built to enable straightforward interaction between the students and the material. Empirical data suggests that the concept of joint annotation can help in processing large amount of material by a large number of students.
A new version of k-random walks algorithm in peer-to-peer networks utilizing learning automata

One of the most important issues in peer-to-peer networks is locating objects among a lot of data. There have been different search methods to find data with certain advantages and disadvantages. In this paper, we propose a new version of k-random walk algorithm utilizing learning automata. In this distributed method, the value of k for k-random walk is not selected randomly but it is selected in an adaptive manner. It is decided which walker is more useful to be selected in order to keep on the search according to past experience of each node. Simulation results show that the novel search algorithm improves the number of hits per query, success rate, generated messages per query and objects discovery delay in comparison with the k-random walk algorithm.
Geometry-Based Edge Clustering for Graph Visualization

Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edge-clustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edge-clustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edge-clustering methods, our approach is intuitive, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method.
Compressive sensing reconstruction using collaborative sparsity among color channels

This study describes a reconstruction method for compressive sensing using collaborative sparsity among multi-frame images and color channels. The proposed method reduces the artifact for compressive sensing and obtains better image quality. Experimental results reveal that the proposed method reconstructs 6.1 dB higher quality images than the conventional one for complex texture at occlusion boundaries.
Bayesian Inference for Bilingual Word Learning

The indeterminacy problem describes the challenge that infants face in deciding which words refer to which objects in their environment. Bayesian models use probabilistic inferences to resolve this induction problem and show improved performance over other computational models in constructing potential lexicons and inferring speakers’ referential intentions. In this study we investigate a Bayesian model’s ability to learn in more complex situations, first with more objects than in previous research and then in a bilingual scenario where more than one word refers to the same object. We found that the model’s absolute and relative performance was attenuated with increased complexity.
5,7-Dihydroxytryptamine lesions of crayfish serotonin-containing neurons: effect on the lateral giant escape reaction

The crayfish's lateral giant escape response, a relatively simple behavioral reaction, is readily modulated in certain situations. For example, when a crayfish is restrained, its lateral giant (LG) fibers-- command neurons that mediate the escape response--are strongly inhibited (Krasne and Wine, 1975). Previous work (Glanzman and Krasne, 1983) had suggested that serotonin (5-HT) might mediate this restraint- induced inhibition of the escape response. To test this possibility, we attempted to lesion serotonergic neurons in crayfish with the 5-HT neurotoxin, 5,7-dihydroxytryptamine (5,7-DHT). We compared the levels of 5-HT-immunoreactive staining in nerve cords from 5,7-DHT-treated and from normal crayfish to assess 5,7-DHT's effectiveness. Levels of immunoreactive staining, as judged by ratings of the visibility of immunofluorescence, were significantly lower in nerve cords from crayfish that had received injections of 5,7-DHT (1.0-4.0 mg) than in nerve cords from normal crayfish. In addition, some serotonergic neurons in the neurotoxin-treated crayfish developed an abnormal brown pigmentation. To assess the behavioral consequence of central serotonergic lesions, we compared the responsiveness of escape in crayfish treated with 5,7-DHT (2.0–2.75 mg) and in normal crayfish. The threshold for firing the LGs was significantly lower in restrained neurotoxin-treated animals than in restrained normal animals. Furthermore, the responsiveness of the LGs in neurotoxin-treated crayfish approximated that in crayfish whose nerve cords had been severed between the thorax and abdomen, a procedure known to abolish restraint-induced inhibition (Krasne and Wine, 1975). (ABSTRACT TRUNCATED AT 250 WORDS)
Genetic programming operators applied to genetic algorithms

Like other learning paradigms, the performance of the genetic algorithms (GAs) is dependent on the parameter choice, on the problem representation, and on the fitness landscape. Accordingly, a GA can show good or weak results even when applied on the same problem. Following this idea, the crossover operator plays an important role, and its study is the object of the present paper. A mathematical analysis has led us to construct a new form of crossover operator inspired from genetic programming (GP) that we have already applied in field of information retrieval. In this paper we extend the previous results and compare the new operator with several known crossover operators under various experimental conditions.