Miracle-dz/UniFAR-FTUs huggingface dataset:
Max Min Fair Scheduling Algorithm Using In Grid Scheduling With Load Balancing

This paper shows the importance of fair scheduling in grid environment such that all the tasks get equal amount of time for their execution such that it will not lead to starvation. The load balancing of the available resources in the computational grid is another important factor. This paper considers uniform load to be given to the resources. In order to achieve this, load balancing is applied after scheduling the jobs. It also considers the Execution Cost and Bandwidth Cost for the algorithms used here because in a grid environment, the resources are geographically distributed. The implementation of this approach the proposed algorithm reaches optimal solution and minimizes the make span as well as the execution cost and bandwidth cost.
Families First: the development of a new mentalization-based group intervention for first-time parents to promote child development and family health

Aim The aim of the present study was to describe the development of Families First, a new mentalization-based group intervention model for supporting early parenthood. The general aim of the intervention was to support well-functioning models of parenting and prevent transmission of negative parenting models over generations, and thus promote child development and overall family health. Background In the Finnish society, great concern has aroused during the last decade regarding the well-being and mental health of children and adolescents. Increased number of divorces, poverty, substance abuse, and mental health problems among parents enhance the risk for child neglect and abuse. New effective, preventive, and health-promoting intervention tools are greatly needed to support families with young children. At present, the Families First intervention is being implemented in primary social and healthcare units all over Finland. Methods and findings This article will provide a theoretical understanding of the importance of parental mentalization for the development of the parent–child relationship and the development of the child as well as proposed mechanisms of actions in order to enhance mentalizing capacity. The cultural context will be described. The article will also provide a description of the scientific evaluation protocol of the intervention model. Finally, possible limitations and challenges of the intervention model are discussed.
Valuing Equity-Indexed Annuities

Abstract Equity-indexed annuities have generated a great deal of interest and excitement among both insurers and their customers since they were first introduced to the marketplace in early 1995. Because of the embedded options in these products, the insurers are presented with some challenging mathematical problems when it comes to the pricing and management of equity indexed annuities. This paper explores the pricing aspect of three of the most common product designs: the point-to-point, the cliquet, and the lookback. Based on certain assumptions, we are able to present the pricing formulas in closed form for the three product designs. The method of Esscher transforms is the fundamental tool for pricing such deferred annuities.
Optimized tone mapping with LDR image quality constraint for backward-compatible high dynamic range image and video coding

Backward compatibility to low dynamic range (LDR) displays is an important requirement for high dynamic range (HDR) image and video coding in order to enable a successful transition to HDR technology. In a recent work [1], an optimized solution for tone mapping and inverse tone mapping of HDR images is achieved in terms of mean square error (MSE) of the logarithm of luminance values of HDR image pixels for backward-compatible compression. Although this pioneer optimization approach provides a well settled mathematical framework for tone mapping, one of its important shortcomings is not to take the quality of the resulting LDR images into account during the formulation. In this paper, we include the LDR image quality as a constraint to optimization problem and develop a methodology to compromise the trade-off between HDR image quality and LDR image quality during HDR image and video coding. The developed methodology is verified on HDR images by showing the increase (decrease) in the quality of generated LDR images while losing (gaining) from the rate-distortion performance of HDR image coding.
On integrability of classical superstrings in AdS5 × S5

We explore integrability properties of superstring equations of motion in AdS5 × S5. We impose light-cone kappa-symmetry and reparametrization gauges and construct a Lax representation for the corresponding hamiltonian dynamics on subspace of physical superstring degrees of freedom. We present some explicit results for the corresponding conserved charges by consistently reducing the dynamics to AdS3 × S3 and AdS3 × S1 subsectors containing both bosonic and fermionic fields.
Image segmentation algorithm based on swarm intelligence technology

Image segmentation is one of the key technologies in image processing, image segmentation quality relates to subsequent processing directly such as image measurement and image recognition, etc. This paper presents a new intelligent optimization algorithm: (artificial fish swarm algorithm, artificial bacterial swarm algorithm and artificial bee colony swarm algorithm), a new method of image segmentation, namely the wavelet transform for segmented images, combined with gray-scale morphology and rough sets theory to solve the problem of image noise, uses a new intelligent optimization algorithm to improve the effect of segmentation, the segmentation performance better, faster, and has important theoretical significance and practical value.
Using changes in land use and land cover to predict avian species occurrences at

Open-canopy oak forests not cleared for agriculture rapidly shift to closed-canopy forests. These changes have produced feedback loops that increasingly favor shade-tolerant tree species over fire-adapted species. Studies observing changes from oak-to mapledominated forests have found decreased avian species richness and abundance. The primary goal of this study was to examine changes in forest bird communities over a relatively short period of time, and assess these changes in light of habitat alterations during this same period. We specifically used recent survey data to predict forest bird occurrences. Point counts were conducted during the breeding season in 2006-07 at 49 survey stations that had been surveyed in 1995-96. Local-habitat variables were measured within 50-m and land cover and land use variables were digitized (200-m, 1-km, and 6-km) for each period. Single-species logistic and autologistic models were created using the 1995-96 dataset. Using the 2006-07 dataset, models were evaluated using receiver operating characteristic (ROC) curves. Predictive models performed best for species associated with open habitats and dense understories, suggesting that we have a relatively good understanding of their habitat requirements. The inclusion of variables measured at relatively fine scales suggests that relying on remotely sensed imagery alone will not be sufficient to make predictions on species occurrences at the scales we studied.
A GMM-supervector approach to language recognition with adaptive relevance factor

Gaussian mixture model (GMM) supervector has been proven effective for language recognition. While a speech utterance can be represented with a GMM which can be obtained through maximum a posteriori (MAP) criterion, it is observed that the supervector formed from the GMM encounters shifting problem in the supervector space due to varying duration of the utterance. We propose an adaptive relevance factor for the MAP estimation to mitigate the negative effect of the variability of individual utterances. Moreover, we develop a language recognition system with a Bhattacharyya-based kernel where the information from the mean vectors and covariance matrices are separately assigned into corresponding dissimilarities. We show the effectiveness of the proposed adaptive relevance factor and the Bhattacharyya-based kernel on the National Institute of Standards and Technology (NIST) language recognition evaluation (LRE) 2009 task.
Matching geospatial ontologies

In recent years, multiple geospatial ontologies have been developed for a wide range of different spatial databases. In addition, the development of volunteered geographic information both challenges and provides opportunities to the traditional authenticated geospatial information. Though volunteered geographic information is typically not as reliable and structured as the authenticated geospatial information, it often reflects changes in the real world more quickly and contains richer information related to human activity [1]. It is therefore desirable to link the corresponding information from disparate geospatial information sources, allowing users to use them synergistically. Aligning disparate geospatial ontologies is an essential element to realizing this. We propose a new semi-automatic method to align geospatial ontologies, based on coherence and consistency checking in description logic, as well as domain experts' knowledge. We evaluate it on real world data and compare it to two state of the art ontology mapping systems, CODI [2] and LogMap [3]. By a geospatial ontology we mean an ontology which contains both definitions of geospatial concepts in its TBox and facts about geospatial individuals in its ABox. When designing our approach, we assume that the TBox is not very large, but contains concepts which are more ambiguous, compared to for example biomedical ontologies. We also assume that geospatial individuals have geometry and location information. In common with other approaches, we use additional disjointness axioms to improve the quality of mapping. Since they are not part of the original ontology and may be wrong, we treat generated disjointness axioms as assumptions retractable by users. We treat original ontol-ogy axioms as correct and not retractable. Given two geospatial ontologies, our method has two main steps: generating assumptions and calculating a consistent and coherent assumption set (CAS) which contains the mapping. Step 1 : Retractable assumptions include disjointness axioms and mapping axioms. For TBoxes, disjointness axioms are generated for sibling classes. Initial mapping axioms between TBoxes are generated by stating equivalence of atomic concepts with identical names. Initial mapping axioms between ABoxes are generated based on three criteria: location, lexical labelling, and cardinality of mapping (one-to-one or one-to-many). We ensure that the geospatial instances from different sources are first represented at the same scale and using the same coordinate reference system scaling and transforming the input data as necessary. Given two instances, if their geometries are not spatially disjoint, we first generate a candidate 'sameAs' axiom for them. (When dealing with polygon geometries, …
Reconstructing audio signals from modified non-coherent hilbert envelopes

In this paper, we present a speech and audio analysis-synthesis method based on a Basilar Membrane (BM) model. The audio signal is represented in this method by the Hilbert envelopes of the responses to complex gammatone filters uniformally spaced on a critical band scale. We show that for speech and audio signals, a perceptually equivalent signal can be reconstructed from the envelopes alone by an iterative procedure that estimates the associated carrier for the envelopes. The rate requirement of the envelope information is reduced by low-pass filtering and sampling, and it is shown that it is possible to recover a signal without audible distortion from the sampled envelopes. This may lead to improved perceptual coding methods. Index Terms: speech and audio coding, auditory representation, auditory model inversion
Experiences with Occupancy based Building Management Systems

Buildings are one of the largest consumers of electricity. Dominant electricity consumption within the buildings, contributed by plug loads, lighting and air conditioning, can be significantly improved using Occupancy-based Building Management Systems (Ob-BMS). In this paper, we address three critical aspects of Ob-BMS i.e. 1) Modular sensor node design to support diverse deployment scenarios; 2) Building architecture to support and scale fine resolution monitoring; and 3) Detailed analysis of the collected data for smarter actuation. We present key learning across these three aspects evolved over more than one year of design and deployment experiences. The sensor node design evolved over a period of time to address specific deployment requirements. With an opportunity at the host institute where two dorm buildings were getting constructed, we planned for the support infrastructure required for fine resolution monitoring embedded in the design phase and share our preliminary experiences and key learning thereof. Prototype deployment of the sensing system as per the planned support infrastructure was performed at two faculty offices with effective data collection worth 45 days. Collected data is analyzed accounting for efficient switching of appliances, in addition to energy conservation and user comfort as performed in the earlier occupancy based frameworks. Our analysis shows that occupancy prediction using simple heuristic based modeling can achieve similar performance as more complex Hidden Markov Models, thus simplifying the analytic framework.
OUT-OF-BAG ESTIMATION

In bagging, predictors are constructed using bootstrap samples from the training set and then aggregated to form a bagged predictor. Each bootstrap sample leaves out about 37% of the examples. These left-out examples can be used to form accurate estimates of important quantities. For instance, they can be used to give much improved estimates of node probabilities and node error rates in decision trees. Using estimated outputs instead of the observed outputs improves accuracy in regression trees. They can also be used to give nearly optimal estimates of generalization errors for bagged predictors.
Minimal Books of Rationales

Kalai, Rubinstein, and Spiegler (2002) propose the rationalization of choice functions that violate the “independence of irrelevant alternatives” axiom through a collection (book) of linear orders (rationales). In this paper we present an algorithm which, for any choice function, gives (i) the minimal number of rationales that rationalizes the choice function, (ii) the composition of such rationales, and (iii) information on how choice problems are related to rationales. As in the classical case, this renders the information given by a choice function completely equivalent to that given by a minimal book of rationales. We also study the structure of several choice procedures that are prominent in the literature.
Persistent Lipophilic Environmental Chemicals and Endometriosis: The ENDO Study

Background: An equivocal literature exists regarding the relation between persistent organochlorine pollutants (POPs) and endometriosis in women, with differences attributed to methodologies. Objectives: We assessed the association between POPs and the odds of an endometriosis diagnosis and the consistency of findings by biological medium and study cohort. Methods: Using a matched cohort design, we assembled an operative cohort of women 18–44 years of age undergoing laparoscopy or laparotomy at 14 participating clinical centers from 2007 to 2009 and a population-based cohort matched on age and residence within a 50-mile catchment area of the clinical centers. Endometriosis was defined as visualized disease in the operative cohort and as diagnosed by magnetic resonance imaging in the population cohort. Logistic regression analysis was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for each POP in relation to an endometriosis diagnosis, with separate models run for each medium (omental fat in the operative cohort, serum in both cohorts) and cohort. Adjusted models included age, body mass index, breast-feeding conditional on parity, cotinine, and lipids. Results: Concentrations were higher in omental fat than in serum for all POPs. In the operative cohort, γ-hexachlorocyclohexane (γ-HCH) was the only POP with a significant positive association with endometriosis [per 1-SD increase in log-transformed γ-HCH: adjusted OR (AOR) = 1.27; 95% CI: 1.01, 1.59]; β-HCH was the only significant predictor in the population cohort (per 1-SD increase in log-transformed β-HCH: AOR = 1.72; 95% CI: 1.09, 2.72). Conclusions: Using a matched cohort design, we found that cohort-specific and biological-medium–specific POPs were associated with endometriosis, underscoring the importance of methodological considerations when interpreting findings.
A robust FIR filter with in situ error detection

We propose a novel FIR filter architecture that mitigates sub-critical timing violations as they occur in the pipeline structure by momentarily bypassing affected coefficients. Timing violations are detected using known in situ circuit-level techniques based on late transition detection at timing end points. The approach enables operation with a small but non-zero logical error rate, such that process, voltage and temperature margins can be eliminated without compromising stop-band attenuation. The proposed architecture is implemented in a 90nm CMOS process technology using a typical commercial standard cell implementation flow and verified using full model SPICE simulations. The filter operates at a maximum clock frequency of 420 MHz at 1 V, with an estimated area and power overhead of 26% and 24% respectively compared to a conventional implementation. At the typical process and temperature corner, the proposed architecture can be scaled in voltage down to the point of first failure at 730 mV, thereby achieving a 53% power saving, with no detectable degradation in stop-band attenuation characteristics.
Proximity neighbor selection using IP prefix matching in Kademlia-based Distributed Hash Table

KAD is one of the most popular Peer-to-Peer (P2P) networks on the Internet. It uses Kademlia-based Distributed Hash Tables (DHTs) to form a tree-structured P2P network. Iterative routing algorithm is used on Kademlia to perform key-value DHT lookup operation based on XOR distance of peer identifiers, which are randomized. Therefore the lookup operation might increase the lookup latency, as well as introduce a lot of cross-network traffic to other Internet Service Providers (ISPs). In this paper, we propose a new efficient proximity neighbor selection in Kademlia-based DHT. The locality among the peers is simply justified based only on the IP prefix matching of peers' IP addresses. Unlike other existing approaches, there are no needs of local database of geographic information about peers, or modification of peer identification structure. Results from simulation on 2000 nodes network show that the proposed technique can reduce both the lookup latency and cross network traffic significantly, comparing to the traditional Kademlia lookup algorithm.
The heterogeneity of crime victims : Variations in procedural and outcome preferences

The authors conducted a systematic review on the topic of victim satisfaction with criminal justice to examine which aspects of the procedure and the legal outcome are associated with satisfaction. The systematic review resulted in 25 articles. Factors were conceptualized into (1) variables related to the procedure, (2) variables related to the outcome and (3) contextual variables. The study uncovered covariates of satisfaction in all categories. Findings, however, were ambiguous. The mixed findings suggest there is a need to understand the differences among victims, and when certain factors are more important in influencing satisfaction with the judicial process.
Validity and Reproducibility of the Measurements Obtained Using the Flexicurve Instrument to Evaluate the Angles of Thoracic and Lumbar Curvatures of the Spine in the Sagittal Plane

Objective. to verify the validity and reproducibility of using the flexicurve to measure the angles of the thoracic and lumbar curvatures. Method. 47 subjects were evaluated by: (1) palpation and marking of the spinous processes using lead markers, (2) using X-rays in the sagittal plane to measure the Cobb angles, (3) molding the flexicurve to the spine, and (4) drawing the contour of the flexicurve onto graph paper. The angle of curvature was determined with the flexicurve based on a 3rd order polynomial. Results. No differences were found between the Cobb angles and the angles obtained using the flexicurve in thoracic and lumbar curvatures (P > 0.05). Correlations were strong and significant for the thoracic (r = 0.72, P < 0.01) and lumbar (r = 0.60, P < 0.01) curvatures. Excellent and significant correlations were found for both the intraevaluator and interevaluator measurements. Conclusion. The results show that there is no significant difference between the values obtained using the flexicurve and those obtained using the X-ray procedure and that there is a strong correlation between the two methods. This, together with the excellent level of inter- and intraevaluator reproducibility justifies its recommendation for use in clinical practice.
Improving the Robustness of Location-Based Routing for Underwater Sensor Networks

This paper investigates a fundamental networking problem in underwater sensor networks: robust and energy-efficient routing. We present an adaptive location-based routing protocol, called hop-by-hop vector-based forwarding (HH-VBF). It uses the notion of a "routing vector" (a vector from the source to the sink) acting as the axis of the "routing pipe", similar to the vector based forward (VBF) routing in the work of P. Xie, J.-H. Cui and L. Lao (VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks. Technical report, UCONN CSE Technical Report: UbiNet-TR05-03 (BECAT/CSE-TR-05-6), Feb. 2005). Unlike the original VBF approach, however, HH-VBF suggests the use of a routing vector for each individual forwarder in the network, instead of a single network-wide source-to-sink routing vector. By the creation of the hop-by-hop vectors, HH-VBF can overcome two major problems in VBF: (1) too small data delivery ratio for sparse networks; (2) too sensitive to "routing pipe" radius threshold. We conduct simulations to evaluate HH-VBF, and the results show that HH-VBF yields much better performance than VBF in sparse networks. In addition, HH-VBF is less sensitive to the routing pipe radius threshold. Furthermore, we also analyze the behavior of HH-VBF and show that assuming proper redundancy and feedback techniques, HH-VBF can facilitate the avoidance of any "void" areas in the network.
A statistical segment-based approach for spoken language understanding

In this paper we propose an algorithm to learn statistical language understanding models from a corpus of unaligned pairs of sentences and their corresponding semantic representation. Specifically, it allows to automatically map variablelength word segments with their corresponding semantic units and thus, the decoding of user utterances to their corresponding meanings. In this way we avoid the time consuming work of manually associate semantic labels to words, process which is needed by almost all the corpus-based approaches. We use the algorithm to learn the understanding component of a Spoken Dialog System for railway information retrieval in Spanish. Experiments show that the results obtained with the proposed method are very promising, whereas the effort employed to obtain the models is not comparable with this of manually segment the training corpus. Index Terms: Spoken Language Understanding, Semantic Classification, Statistical modelization