Miracle-dz/UniFAR-FTUs huggingface dataset:
Universit at Konstanz a Software Engineering Perspective on Algorithmics | Revised Version | a Software Engineering Perspective on Algorithmics

An algorithm component is an implementation of an algorithm which is not intended to be a stand{alone module, but to perform a speciic task within a large software package or even within several distinct software packages. Hence, the design of algorithm components must also incorporate software{engineering aspects. A key design goal is adaptability. This goal is important for maintenance throughout a project, prototypical development, and reuse in new, unforeseen contexts. From a theoretical viewpoint most algorithms apply to a range of possible use scenarios. Ideally, each algorithm is implemented by one algorithm component, which is easily, safely, and eeciently adaptable to all of these contexts. Various techniques have been developed for the design and implementation of algorithm components. However, a common basis for systematic, detailed evaluations and comparisons in view of the real practical needs is still missing. Basically, this means a set of concrete criteria, which specify what sort of adaptability is really required in practice, and which are well justiied by convincing, representative use scenarios. This paper is intended as a rst \milestone" on the way towards such a system of criteria. We will present a set of concrete goals, which are general and problem{independent and might appear ubiquitously in the algorithmic realm. These goals are illustrated, motivated, and justiied by an extensive requirements analysis for a particular algorithm from a particular algorithmic domain: Dijkstra's algorithm for shortest paths in networks. Clearly, the eld of algorithmics might be too versatile to allow a comprehensive, yet concise set of precise, justiied criteria. Even a domain as restricted as graph and network algorithms includes aspects that are not at all understood. The analysis will include a discussion of the limits of the case study and the scope of the goals. The case study was chosen because it seems to be close to the \borderline" between the aspects that are well understood and the aspects that are not. Hence, this example may well serve as an \acid test" for programming techniques in view of the state of the art.
A Longitudinal Study of Emotion Recognition and Preschool Children's Social Behavior.

Little is known about relations between preschoolers' social behavior and emotion recognition accuracy and bias with familiar classmates, and no study has examined changes in emotion recognition over time. Participants were 90 4-year-old pre schoolers (47 male). Three weeks after preschool began and again 5 months later, children were photographed posing emotional expressions. At each time point classmates were asked to identify the expressions. Social behavior measures in cluded teacher ratings and peer sociometric ratings. Major conclusions were: (a) Recognition biases were more important than recognition accuracy in predicting social behavior, (b) Angry recognition biases negatively influenced social behavior, (c) Recognition biases that are beneficial may vary depending on children's familiar ity. (d) Recognition accuracy is not stable over time.
A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast

Cells must be able to process multiple information in parallel and, moreover, they must also be able to combine this information in order to trigger the appropriate response. This is achieved by wiring signalling pathways such that they can interact with each other, a phenomenon often called crosstalk. In this study, we employ mathematical modelling techniques to analyse dynamic mechanisms and measures of crosstalk. We present a dynamic mathematical model that compiles current knowledge about the wiring of the pheromone pathway and the filamentous growth pathway in yeast. We consider the main dynamic features and the interconnections between the two pathways in order to study dynamic crosstalk between these two pathways in haploid cells. We introduce two new measures of dynamic crosstalk, the intrinsic specificity and the extrinsic specificity. These two measures incorporate the combined signal of several stimuli being present simultaneously and seem to be more stable than previous measures. When both pathways are responsive and stimulated, the model predicts that (a) the filamentous growth pathway amplifies the response of the pheromone pathway, and (b) the pheromone pathway inhibits the response of filamentous growth pathway in terms of mitogen activated protein kinase activity and transcriptional activity, respectively. Among several mechanisms we identified leakage of activated Ste11 as the most influential source of crosstalk. Moreover, we propose new experiments and predict their outcomes in order to test hypotheses about the mechanisms of crosstalk between the two pathways. Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other.
Effect of food processing on degradation of hexachlorocyclohexane and its isomers in milk

Aim: To study the effect of different food processing techniques on the degradation of organochlorine compounds (α, β, ɣ and δ hexachlorocyclohexane isomers (HCH)) residues in both natural and fortified samples of milk. Materials and Methods: Raw milk samples are collected from the local areas of Hyderabad, India. Naturally and fortified milk samples (HCH) were subjected to various food processing techniques, pasteurization (63ºC for ½ h), sterilization (121ºC for 15 min) and boiling for 5 min and analyzed by gas chromatography with electron capture detector using quick, easy, cheap, effective, rugged and safe method for multiresidue analysis of pesticides in milk with slight modification. Results: The final mean residual concentration of pesticide in milk after heat processing and percentage of degradation were calculated with respective treatments. Conclusion: Heat treatments are highly effective on reduction of mean residual concentration of HCH in milk. In which Sterilization and boiling proved to be more effective in degradation of HCH isomers.
A Refinement Driven Component-Based Design

Modern software applications ranging from enterprise to embedded systems are becoming increasingly complex, and require very high levels of dependability assurance. The most effective means to handle complexity is separation of concerns and incremental development, and assurance of dependability requires formal methods. We report here our experience on these issues in an application of a formal calculus, rCOS, to a component-based design of the point of sale system (POS). We demonstrate the possibility in scaling-up correctness by design and discuss how rCOS may be integrated with current and emerging software engineering tools.
A Systematic Review of Barriers and Facilitators of Disability Disclosure and Accommodations for Youth in Post-Secondary Education

ABSTRACT This review critically appraised the literature on disability disclosure and accommodations for youth with disabilities in post-secondary education (PSE). Systematic searches of 8 databases identified 36 studies meeting our inclusion criteria. These studies were analysed with respect to the characteristics of the participants, methodology, results of the studies and quality of evidence. Five thousand, one hundred and seventy four participants (mean age 26.4) were represented across six countries. Barriers to disability disclosure and requests for accommodations in PSE included stigma, discrimination, lack of knowledge of supports and how to access them, type of course and instructor, coping styles, and nature of the disability. Facilitators included supports and resources, coping and self-advocacy skills, mentorship, and realising the benefits of disclosure. Factors affecting the process and timing of disability disclosure in PSE included the type of disability, and mode of disclosure. There was a lack of consensus on the timing of disclosure.
Medical image fusion using enhanced gradient exposure bracketed pairs

Multimodal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on Gradient Orientation Bracketed pairs. The source medical images are first transformed by Image separation (DOF) followed by combining background and foreground parts frequency components. The Standard Dynamic Range (SDR) device traces image details like contrasts and gradient filter direction in a series of SDR images with different levels. The image overall luminance levels are calculated with the image series, which maximizes the observable distinctions, and then the gradients embedded in these images. The fusion algorithm techniques are used for fusion of images based on Luminance and gradient level. This is done in a multi-resolution of illumination variation in the sequence. Experimental results and comparative study show that the proposed fusion framework provides an effective way to enable more accurate analysis of multimodality images.
Parallel genetic programming: an application to trading models evolution

We present a parallel implementation of genetic programming on distributed memory machines. To overcome the time overhead due to uneven load associated with program evaluation, we propose and evaluate a non-preemptive dynamic scheduling algorithm for load balancing. The system is applied to the evolution of trading model strategies which is a compute-intensive application. Our results show that reasonable trading models can be inferred and that the system can produce a nearly linear speedup for that application.
Bio-Inspired Design of Soft Robotic Assistive Devices: The Interface of Physics, Biology, and Behavior

Wearable assistive robotic devices are characterized by an interface, a meeting place of living tissue and mechanical forces, at which potential and kinetic energy are converted to one or the other form. Ecological scientists may make important contributions to the design of device interfaces because of a functional perspective on energy and information exchange. For ecological scientists, (a) behavioral forms are an assembly of whole functional systems from available parts, emerging in energy flows, and (b) nature explores for informationally based adaptive solutions to assemble behavioral forms by generating spontaneous patterns containing fluctuations. We present data from ongoing studies with infants that demonstrate how infants may explore for adaptive kicking solutions. Inspired by the ecological perspective and data from developing humans, ecological scientists may design interfaces to assist individuals with medical conditions that result in physical and/or mental impairment. We present one such device, what is called the “second skin,” to illustrate how a soft, prestressed material, worn on the skin surface, may be used synergistically with synthetic and biological muscles for assisting action. Our work on the second skin, thus far, suggests a set of ecologically inspired principles for design of wearable assistive robotic devices.
The Application of Photometric Redshifts to the SDSS Early Data Release

The Early Data Release (EDR) from the Sloan Digital Sky Survey provides one of the largest multicolor photometric catalogs currently available to the astronomical community. In this paper we present the first application of photometric redshifts to the ∼6 million extended sources in these data (with 1.8 million sources having r′ < 21). Utilizing a range of photometric redshift techniques, from empirical to template and hybrid techniques, we investigate the statistical and systematic uncertainties present in the redshift estimates for the EDR data. For r′ < 21, we find that the redshift estimates provide realistic redshift histograms with an rms uncertainty in the photometric redshift relation of 0.035 at r′ < 18 and rising to 0.1 at r′ < 21. We conclude by describing how these photometric redshifts and derived quantities, such as spectral type, rest-frame colors, and absolute magnitudes, are stored in the SDSS database. We provide sample queries for searching on photometric redshifts and list the current caveats and issues that should be understood before using these photometric redshifts in statistical analyses of the SDSS galaxies.
Towards a Visualization Process Model for Online Visualization

With today’s technical possibilities, a stable visualization scenario can no longer be assumed, as underlying data and operations are much more in flux than in traditional scenarios. We term such dynamic visualization scenarios online visualization. In contrast to traditional offline visualization that rely on monolithic visualization operators and a monolithic dataset, it permits the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this poster, we detail a possible extension of the Data State Reference Model (DSRM) [2] to capture not only offline, but also online visualization processes, as well as online/offline hybrids. We further showcase our extended model as a prospective user interface to communicate an online visualization’s progress, as well as to interactively steer it.
Scalable and Order-robust Continual Learning with Hierarchically Decomposed Networks.

While recent continual learning methods largely alleviate the catastrophic problem on toy-size datasets, there are issues that remain to be tackled in order to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely vary based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameter for each task as a sum of task-shared and sparse task-adaptive parameters. With our hierarchically decomposed networks (HDN), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, with hierarchical knowledge consolidation which clusters the task-adaptive parameters to obtain hierarchically shared parameters, HDN becomes highly scalable. We validate HDN on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, efficiency, scalability, and order-robustness.
An ansatz for the singularities of hypergeometric multisums

Sequences that are defined by multisums of hypergeometric terms occur frequently in enumeration problems of combinatorics, algebraic geometry and perturbative quantum field theory. When properly normalized, the generating series of the above sequences are convergent in a neighborhood of zero and holonomic, that is they satisfy a linear differential equation with polynomial coefficients. It follows that the generating series have analytic continuation as multivalued functions in the complex plane minus a finite set of singular points. Moreover, the singularities of the generating series determines the exponential growth rate of the sequence in question. The paper proposes (and in some cases proves) an efficient ansatz for an exact computation of the set of singularities, directly from a hypergeometric term. The ansatz is given by the solutions of a balanced system of polynomial equations of a rather special form, reminiscent of the Bethe ansatz.
Learning Curves for Automating Content Analysis: How Much Human Annotation is Needed?

In this paper, we explore the potential for reducing human effort when coding text segments for use in content analysis. The key idea is to do some coding by hand, to use the results of that initial effort as training data, and then to code the remainder of the content automatically. The test collection includes 102 written prepared statements about Net neutrality from public hearings held by the U.S Congress and the U.S. Federal Communications Commission (FCC). Six categories used in this analysis: wealth, social order, justice, freedom, innovation and honor. A support vector machine (SVM) classifier and a Naïve Bayes (NB) classifier were trained on manually annotated sentences from between one and 51 documents and tested on a held out of set of 51 documents. The results show that the inflection point for a standard measure of classifier accuracy (F1) occurs early, reaching at least 85% of the best achievable result by the SVM classifier with only 30 training documents, and at least 88% of the best achievable result by NB classifier with only 30 training documents. With the exception of honor, the results show that the scale of machine classification would reasonably be scaled up to larger collections of similar documents without additional human annotation effort.
Towards detecting inactivity using an in-home monitoring system

The ageing of the world population underscores the need for in-home monitoring systems that help elderly people remain safely in their homes. This paper introduces a statistical model that addresses one of the main concerns of elderly people and their carers, viz detection of safe movement in the house. Our model identifies unusually long periods of inactivity within different regions in a home using non-intrusive sensors. Our evaluation on two real-life datasets yields encouraging results.
Financing Constraints and Firm Dynamics with Durable Capital

We consider a dynamic financing problem of a firm subject to moral hazard problems. With respect to the existing literature (e.g. as Clementi and Hopenhayn [2] and Quadrini [12]), we enrich the model by introducing durable capital and a stochastic liquidation value. The existence of durable capital allows us to make predictions based on the firm size, independently of age, while the stochastic liquidation value makes it possible to have liquidation with positive probability under the first best. We find that a higher level of capital decreases the probability of liquidation, increases future size and reduces the average return and volatility of the firm. Also, under certain parameter values a stochastic liquidation value makes it possible to achieve the first best. The results are broadly in agreement with the empirical results on the effects of firm size.
An Overview of Swarm Robotics for Search and Rescue Applications

The area of research presented in this chapter focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this chapter overviews the applicability of swarm robotic solutions into search and rescue (SaR) real-world missions. In this chapter, SaR operations are considered as a vital target application wherein swarm robotics can be applied due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to traditional MRS applicability. This chapter focuses on these problems highlighting the challenges that cannot be handled appropriately by simple adaptation of traditional algorithms, planning, control and decision-making techniques.
The Discrete Multi-Hybrid System for the Simulation of Solid-Liquid Flows

This study proposes a model based on the combination of Smoothed Particle Hydrodynamics, Coarse Grained Molecular Dynamics and the Discrete Element Method for the simulation of dispersed solid-liquid flows. The model can deal with a large variety of particle types (non-spherical, elastic, breakable, melting, solidifying, swelling), flow conditions (confined, free-surface, microscopic), and scales (from microns to meters). Various examples, ranging from biological fluids to lava flows, are simulated and discussed. In all cases, the model captures the most important features of the flow.
Union Density and European Integration: Diverging Convergence

This article uses multivariate regression analysis to investigate trends in union density in western Europe in the period 1970-95. While countries with union-managed unemployment insurance schemes (Ghent system) saw density rates increase or at least remain stable, the others typically experienced a decline in unionization. Economic factors (shifts in the structure of employment, and also the business cycle and internationalization of trade) exert an influence, though less substantial, on changes in union density. The evidence is ambiguous as to the impact of European economic integration as such on unionization.
Sentiment Classification in Hindi

: Traditional approaches for classification of sentiments depend on lexical or syntax based feature or on both. Different methods for sentiments classifications are described . The main goal of analysis of the sentiments is to obtain the writer’s feelings whether +ve or -ve. Sentiment Analysis is having its importance because in this world of Internet the opinion of public is very important.So the need for analysis of sentiment is increasing heavily.
VLIW operation refinement for reducing energy consumption

The demand for mobile computer power has exploded in the recent years. Variable length VLIW processors offer the necessary performance at low power. Software optimizations are necessary to further decrease the energy consumption. In this article we present a compiler optimization which reduces the dynamic power dissipation resulting from the switching activities during instruction fetch. Energy consumption can be reduced by minimizing the Hamming distance between successively fetched instruction words. Using a dynamic programming approach we first compute a set of optimal instruction arrangements of the execution bundles in a basic block. These sets are used in an enumerative optimal algorithm and a genetic evolution, in order to minimize an objective function for the Hamming distance. We evaluated our algorithms on different variable length VLIW architectures with 3 to 6 parallel functional units. On a large set of DSP benchmark programs the Hamming distance can be reduced by about 10% on average. Maximum reductions range up to 30%.
PDBTool: A Prototype Object Oriented Toolkit for Protein Structure Verification

Abstract : The Protein Data Bank (PDB) is a large and rapidly growing standard repository of complex biological macromolecules. Existing software tools for the PDB, including storage management, are quite inflexible and work in isolation. We are using object-oriented design and object-oriented database techniques in the construction of a toolbox for the PDB. The main components of the object- oriented toolbox include: (1) a uniform graphical user interface for fast, intuitive, and interactive access to the PDB; (2) several interoperable storage managers. compatible with present and future PDB formats; (3) a new set of efficient, modular, and flexible software tools. The toolbox is being implemented in C(++). We describe our experience in the implementation and use of the first prototype, PDBTool, to display, manipulate, and verify the structure of proteins. This project is a successful collaboration of a Biophysics and a Computer Science research and development team.