Miracle-dz/UniFAR-FTUs huggingface dataset:
Prosodic structure and consonant development across languages

This paper relates consonant development in first-language acquisition to the mastery of rhythmic structure, starting with the emergence of the “core syllable” in babbling. We first review results on very early phonetic development that suggest how a rich hierarchy of language-specific metrical structures might emerge from a universal developmental progression of basic utterance rhythms in interaction with ambient language input. We then describe salient differences in prosodic structures across the languages being studied in a cross-language investigation of phonological development, in which we are eliciting and analyzing recordings from hundreds of children aged two years through five years who are acquiring Cantonese, English, Greek, or Japanese. Finally, we present examples of how patterns of disfluent consonant production differ across children acquiring the different languages in this set, in ways that seem to be related to the differences in metrical organization across the languages.
Progressive image transmission applying multipath routing in mobile ad hoc networks

We investigate different allocation problems in mobile ad hoc networks. The objective is to minimize the power consumption and end-to-end distortion when transmitting images. Therefore, we use progressive image coding in combination with unequal erasure protection. We investigate two different scenarios, independent packet loss events and path loss events. For both cases, the expected distortion for progressive image transmission without error protection as well as for unequal erasure protection has been derived. Also, the expected power consumption for a certain path allocation pattern is obtained. The construction of operational distortion-power curves is presented by optimizing the unequal error protection and extracting the convex hull of all possible path allocation patterns. The profit of applying UEP to the progressively coded image bitstream in a multipath environment is shown. The presented error protection and rate allocation can be used as a framework for studying the allocation problem concerning the tradeoff between power and distortion for progressive image transmission in different multipath environments.
TESTED SHALES The two Mesozoic shales considered in the present study come from the northern region of Switzerland : the Opalinus Clay sample

The development of engineering activities involving shales such as the extraction of shale gas and shale oil, the nuclear waste geological storage and the sequestration of CO2, has led to an increasing interest toward the geomechanical behaviour of this geomaterial. In the context of such engineering applications, a deep understanding of the hydro-mechanical behaviour of shales is of primary significance. The water retention mechanisms play a major role in either fluid trapping due to the capillary forces present in low permeability formations or in the resaturation of shale formations after desiccation. The paper presents the experimental methodologies that have been developed by the authors for the analysis of the retention behaviour of shales. Selected test results are presented for two shales from Switzerland.
The design of component-based workflow management system

Workflow technology can quickly adapt to changes in needs, providing great flexibility for the software system. Component Technology implements the code reuse by certain encapsulation, being regarded as one of the key factors of software reuse. The research work of this paper provides a theory by component technology and workflow technology combining, illustrating that component-based development of workflow system not only simplify the complexity of the process definition but also maintain the flexibility of the system.
Towards collaborative Strategy Content Management using Ontologies

We propose the use of ontologies for managing strategy content, which is generated and processed during strategy planning processes. We introduce a Protégé extension aiming at facilitating this task. It provides views on the strategy content codified in the ontology in form of diagrams and supports the concurrent graphical editing of these by multiple users at the same time.
Examining the Relevance of Indonesian Vocational High School Career Outcomes to the Labor Market

When coping with tight competition in the Asian Economic Community, career guidance from educators can be very beneficial to students. This research looks at vocational high schools in Pasuruan, East Java, Indonesia and aims to: see the attainments of vocational school graduates in labor markets, analyze the guidance-counseling program to bridge students’ student’s outcome and labor market, and the partnership systems for connecting with their stakeholders as users of labor. This research employed mixed qualitative and quantitative methods with an explorative survey. The findings reveal that graduates achieve successful attainment in the labor market in three areas: a variety of jobs, satisfied standard competence in the job market, and satisfied income. However, in preparing production markets, most schools lack: loyal customer bases for production outputs, marketing networks, and showrooms; adequate promotional efforts for their production outputs; personnel for quality assurance activities, resulting in graduates being unprepared for employment; and adequate industry support for production, such as by outsourcing production to schools’ production units. Half of the schools have employed liaison officers to seek orders from industrial partners or companies for joint production, and such staff has been key in empowering school committees to enlarge their partnership networks.
An energy efficient MAC protocol for cluster based event driven WSN applications

Energy efficiency is one of the most important design criteria for long lasting WSN applications. There are many interesting works related to Medium Access Control (MAC) protocols proposed for low energy consumption and high system performance in the literature. TDMA based MAC protocols are more energy-efficient than their traditional contention based counterparts. However, conventional TDMA based MAC protocols are not well suited to the event driven WSN applications. There are some MAC protocols allocating time slots to only source nodes. The distinctive feature of our proposed MAC protocol is that it assigns a time slot to only one of the source nodes all with the same data sensed and to be sent. Thus it reduces data transmission redundancy and achieves energy savings. The analytical performance metrics and expressions are obtained for the proposed MAC protocol and its performance is compared to the conventional TDMA, E-TDMA, BMA and EA-TDMA protocols.
Understanding how HR Systems Work: The Role of HR Processes and HR Practices

Despite the proliferation of studies of HR systems, there are still substantial gaps in our understanding of how such systems actually work. This article, by focusing on the neglected areas of HR philosophy and HR processes in the composition of HR systems, and by using a qualitative, employee-centred approach in the collection and analysis of data, provides new insights into the working of HR systems. Using data from interviews with 56 knowledge workers employed in the information and communications technology sector in Ireland and the UK, we explore employee-level reactions to two different types of HR systems. We highlight the various ways in which HR processes interact with HR practices and the different outcomes that may result, and we identify the key role of HR philosophy in HR system operation.
Efficient Active Learning with Boosting

This paper presents an active learning strategy for boosting. In this strategy, we construct a novel objective function to unify semi-supervised learning and active learning boosting. Minimization of this objective is achieved through alternating optimization with respect to the classifier ensemble and the queried data set iteratively. Previous semi-supervised learning or active learning methods based on boosting can be viewed as special cases under this framework. More important, we derive an efficient active learning algorithm under this framework, based on a novel query mechanism called query by incremental committee. It does not only save considerable computational cost, but also outperforms conventional active learning methods based on boosting. We report the experimental results on both boosting benchmarks and real-world database, which show the efficiency of our algorithm and verify our theoretical analysis.
Low-cost interactive active monocular range finder

This paper describes a low-cost interactive active monocular range finder and illustrates the effect of introducing interactivity to the range acquisition process. The range finder consists of only one camera and a laser pointer to which three LEDs are attached. When a user scans the laser along surfaces of objects, the camera captures the image of spots (one from the laser and the others from LEDs), and triangulation is carried our using the camera's viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover the processes of separating objects from the background and/or finding parts in the object can be achieved using the operator's knowledge of the objects.
Size and Motion of the Mitral Valve Annulus in Man: II. Abnormalities in Mitral Valve Prolapse

Changes in mitral annular areas and cyclic area changes were measured in 17 patients with mitral valve prolapse (MVP) and compared with those in 12 normal subjects. Using wide-angle, phased-array, two-dimensional echocardiography, mitral valve annular attachments were recorded in a view close to the apical four-chamber view, then at 30° rotational intervals around the circumference of the annulus. Diameters and chords from planes at 30° rotational intervals were used to reconstruct the annulus at selected times during the cardiac cycle. Annular areas were measured by planimetry, corrected for body surface area and expressed as area index. In normal subjects, the maximal mitral annular area index was 3.9 ± 0.7 cm2/m2 (mean ±SD). Of 11 MVP patients with minimal mitral regurgitation (MR), five had a dilated annulus (5.8 ± 0.3 cm2/m2, p < 0.001) and six had a normal-sized annulus (4.2 ± 0.5 cm2/m2). In six MVP patients with at least moderate MR, annular dilatation was marked (area index 8.5 ± 1.4 cm2/m2, p < 0.001) and systolic annular area reduction (16 ± 5%) was less than in normal subjects (27 3%, p < 0.001). Cyclic annular area changes in MVP patients with minimal MR were similar to those in normal subjects. Mitral annular size in patients with MVP ranges from normal to markedly dilated. The occurrence of mitral annular dilatation in MVP patients with minimal MR indicates primary or intrinsic annular dilatation rather than annular dilatation secondary to left atrial or left ventricular enlargement. Patients with MVP and significant MR had marked annular dilatation and less-than-normal systolic annular area reduction.
Unsupervised feature selection for proportional data clustering via expectation propagation

In this paper, an expectation propagation (EP) inference framework for unsupervised feature selection is proposed for modeling proportional data which naturally appear in many applications such as text and image modeling, in the context of finite mixture-based clustering. Within our framework, simultaneous clustering and feature selection is formalized using finite mixtures of generalizing Dirichlet (GD) distributions. The proposed EP-based inference framework allows to obtain a full posterior distribution on all our unsupervised feature selection model's parameters. Moreover, the complexity of the deployed mixture models and all the involved model parameters can be evaluated simultaneously. The effectiveness and efficiency of the proposed algorithm are evaluated on both synthetic data and two challenging applications namely human action videos categorization and facial expression recognition.
Semantic Web Mining and its application in Human Resource Management

The Semantic Web is a project and vision of the World Wide Web Consortium to extend the current Web, so that information is given a well-defined meaning and structure, enha ncing computers and people to work in cooperation. Semantic web mining is the combination of web mining and semantic web. The knowledge of semantic web makes web mining easier to achieve and can also improve the effectiveness of web minin g. Semantic web mining technologies are being added to enterpri se solutions to accommodate new techniques for discovering relationships across different database, business applications an d Web services. Since this is an interdisciplinary concep t in both engineering and management; we first review web mining, semantic web, semantic web mining and finally propose an application of semantic web mining in human resource management.
When will Feature Feedback help ? Quantifying the Complexity of Classification Problems

Supervised learning typically requires human effort to label a large number of training instances. Active learning strives to decrease the nu mber of labeled training examples needed by actively engaging the learner and the human in an interactive process. Active learning has proven to be effecti ve in many domains. With few training examples, past work has found that user prio r knowledge on the importance of features, or interactive feature feedback, c n guide the learner to converge faster, that is, with lower labeling costs. In this paper we aim to understand the kinds of problems for which such extra feedback are significantly beneficial. In other words, we ask what kind of problems can sign ifica tly benefit from interactive learning and whether for some problems the us er has no choice but to engage in the tedious process of labeling many examples. To wards this goal, we define a set of four difficulty measures, 2 each of instance and feature complexity, for linear classification problems. These measures can effi ciently be computed for real world problems for which linear classifiers are effe ctiv , such as text classification. We quantify the difficulty of 358 text classification problems and 9 corpora using our measures, illustrating the spectrum of problems that exist in text class ific tion in addition to quantifying results that have only been qualitatively discusse d in the text classification literature. We verify the intimate relationship (a high pos itive correlation) between feature complexity and instance complexity using our measures. We then use these measures to understand when feature fe edback is likely to be very useful. We observe that many problems in the commonly us ed data sets are of low to medium complexity, that is, only roughly 10s of well se lected features are required to gain most of the maximum attained perform ance on such concepts. We find that learning these kinds of problems especia lly stands to benefit from feature feedback. We note that our empirical difficulty measures and the rankings of proble ms and domains are of independent interest, beyond the active learning setting.
The Transcription Factor DBP Affects Circadian Sleep Consolidation and Rhythmic EEG Activity

Albumin D-binding protein (DBP) is a PAR leucine zipper transcription factor that is expressed according to a robust circadian rhythm in the suprachiasmatic nuclei, harboring the circadian master clock, and in most peripheral tissues. Mice lacking DBP display a shorter circadian period in locomotor activity and are less active. Thus, although DBP is not essential for circadian rhythm generation, it does modulate important clock outputs. We studied the role of DBP in the circadian and homeostatic aspects of sleep regulation by comparing DBP deficient mice (dbp−/−) with their isogenic controls (dbp+/+) under light–dark (LD) and constant-dark (DD) baseline conditions, as well as after sleep loss. Whereas total sleep duration was similar in both genotypes, the amplitude of the circadian modulation of sleep time, as well as the consolidation of sleep episodes, was reduced in dbp−/− under both LD and DD conditions. Quantitative EEG analysis demonstrated a marked reduction in the amplitude of the sleep–wake-dependent changes in slow-wave sleep delta power and an increase in hippocampal theta peak frequency in dbp−/− mice. The sleep deprivation-induced compensatory rebound of EEG delta power was similar in both genotypes. In contrast, the rebound in paradoxical sleep was significant in dbp+/+ mice only. It is concluded that the transcriptional regulatory protein DBP modulates circadian and homeostatic aspects of sleep regulation.
IS THE FUTURE OF THE SCANDINAVIAN INFORMATION SYSTEMS DEVELOPMENT APPROACHES PASSÉ?

: This essay discusses possible intellectual contributions of the Scandinavian IS research traditions to the future of IS as a discipline. It suggests that the infological problems identified by Langefors still capture much of the essence of IS as a discipline of computing. The essay also revisits the infological equation, showing its continued relevance in the field. Finally, it discusses some trends in ISD.
Online Learning Techniques for Improving Robot Navigation in Unfamiliar Domains

Abstract : Many mobile robot applications require robots to act safely and intelligently in complex unfamiliar environments with little structure and limited or unavailable human supervision. As a robot is forced to operate in an environment that it was not engineered or trained for, various aspects of its performance will inevitably degrade. Roboticists equip robots with powerful sensors and data sources to deal with uncertainty, only to discover that the robots are able to make only minimal use of this data and still find themselves in trouble. Similarly, roboticists develop and train their robots in representative areas, only to discover that they encounter new situations that are not in their experience base. Small problems resulting in mildly sub-optimal performance are often tolerable, but major failures resulting in vehicle loss or compromised human safety are not. This thesis presents a series of online algorithms to enable a mobile robot to better deal with uncertainty in unfamiliar domains in order to improve its navigational abilities, better utilize available data and resources and reduce risk to the vehicle. We validate these algorithms through extensive testing onboard large mobile robot systems and argue how such approaches can increase the reliability and robustness of mobile robots, bringing them closer to the capabilities required for many real-world applications.
Finding All Solutions if You can Find One

Author(s): Dechter, Rina; Itai, Alon | Abstract: We address the problem of enumerating (producing) all models of a given theory. We show that the enumeration task can be performed in time proportional to the product of the number of models and the effort needed to generate each model in isolation. In other words, the requirement of generating a new solution in each iteration does not in itself introduce substantial complexity. Consequently, it is possible to decide whether any tractably satisfiable formula has more than K solutions in time polynomial in the size of the forumla and in K. In the special cases of Horn formulas and 2-CNFs, although counting is #P-complete, to decide whether the count exceeds K, is polynomial in K.
DEA-Based Comprehensive Evaluation of Intelligent Knowledge

The paper is about how to evaluate the intelligent knowledge. We think that the criteria is not only distinguishing ability and accuracy of the algorithm, but also algorithm robustness, stability and so on. Besides positive characters listed above, a fair evaluation system of intelligent knowledge should also include negative characters, such as storage space, running time, training and testing time and costs and etc. In this paper we look positive and negative characters of data mining algorithm as the outputs and inputs of a decision making unit, and proposed a model to evaluating intelligent knowledge comprehensively using DEA.
Predictive Control for Performance Guarantees in Soft Real-Time Scheduling Systems

To provide performance guarantee for real-time scheduling applications working in open and unpredictable environments, a model predictive control approach is applied to regulate multiple performance metrics of soft real-time scheduling systems with uncertain task set characteristic. Utilization and deadline miss control of real-time scheduling is modeled as a multivariable optimization problem. A single input multi-output predictive control framework and algorithm is presented based on a standard quadratic programming solver and a difference equation model that describes soft real-time scheduling systems. The simulation results demonstrate that predictive control scheduling can regulate the utilization and the deadline miss of real-time scheduling systems and show robustness when the actual execution time of tasks deviate from design value at runtime
eath by protein damage in irradiated cells

A founding concept of radiobiology that deals with X-rays, -rays and ultraviolet light is that radiation indiscriminately damages cellular macromolecules. Mounting experimental evidence does not fit into this theoretical framework. Whereas DNA lesion-yields in cells exposed to a given dose and type of radiation appear to be fixed, protein lesion-yields are highly variable. Extremely radiation resistant bacteria such as Deinococcus radiodurans have evolved extraordinarily efficient antioxidant chemical defenses which specifically protect proteins and the functions they catalyze. In diverse prokaryotes, the lethal effects of radiation appear to be governed by oxidative protein damage, which inactivates enzymes including those needed to repair and replicate DNA. These findings offer fresh insight into the molecular mechanisms of radiation resistance and present themselves as new opportunities to study and control oxidative stress anganese (II) antioxidant complexes eactive oxygen species (ROS) etabolite accumulation
Stochastic Game Formulation for Cognitive Radio Networks- Short Paper

In this paper, we model the various wireless users in a cognitive radio network as a collection of selfish, autonomous agents that strategically interact in order to compete for the dynamically available spectrum opportunities. We propose a stochastic game framework to model how the competition among users for spectrum opportunities evolves over time. At each stage of the dynamic resource allocation, a spectrum moderator auctions the available resources and the users strategically bid for the required resources. Based on the observed resource allocation and corresponding rewards from previous allocations, we propose a best response learning algorithm that can be deployed by wireless users to improve their bidding policy at each stage. The simulation results show that by deploying the proposed best response learning algorithm, the wireless users can significantly improve their own performance in terms of both the packet loss rate and the incurred cost for the used resources.
The idiographic approach: where do we come from and where do we go?

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