Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance comparison of mobile support strategies

This paper presents performance comparison among five strategies for mobile support. The major facilities that are required for a network protocol to support mobile hosts are location management and packet forwarding. Based on this observation, we consider five basic strategies which use distinct methods to achieve these facilities and compare their performance. These five strategies are Broadcast Kotification (BN), Broadcast Forwarding (BF), Broadcast Query (BQ), Default Forwarding (DF), and Default Query (DQ). As a result of analytical evaluation and comparison, it is shown that under different network conditions, such as number of routers, network topology, migration/communication ratio, data/control packet size ratio, different strategies produce minimum network traffic. In short, DF and DQ show the best performance in scalability, while BF and BQ are efficient for frequent migration. On the other hand, BN is suitable for a small network which has hosts with rare migration.
Tailoring the Software Process Model to Project requirements

Software process modeling has undergone extensive changes in the last three decades, impacting process' structure, degree of control, degree of visualization, degree of automation and integration. This evolution resulted in a diversity of models that tended to suit software project requirements from different angles. This paper investigates the main streams of process models in an effort to build taxonomy of super classes of process models and their interrelationships. This investigation involved a literature survey of both existing process models and process modeling assessments. Based on this taxonomy an eight-step evaluation process was carried out. By utilizing a manual and an automated decision tables, a final assessment was presented. According to project management literature, a set of decision criteria was then established to facilitate tailorability among process models. The paper concludes with the optimized selection of process models most successful in meeting a variety of software projects requirements. It was also concluded that process diversity could be the rationale for integration rather than differentiation as to capture projects needs in adaptive and flexible combinations. Finally, several frameworks were presented to address similarities, differences, comparisons, and evolutionary aspects among software process models.
Algorithms for Internal Validation Clustering Measures in the Post Genomic Era

Inferring cluster structure in microarray datasets is a fundamental task for the -omic sciences. A fundamental question in Statistics, Data Analysis and Classification, is the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. In this dissertation, a study of internal validation measures is given, paying particular attention to the stability based ones. Indeed, this class of measures is particularly prominent and promising in order to have a reliable estimate the number of clusters in a dataset. For those measures, a new general algorithmic paradigm is proposed here that highlights the richness of measures in this class and accounts for the ones already available in the literature. Moreover, some of the most representative validation measures are also considered. Experiments on 12 benchmark datasets are performed in order to assess both the intrinsic ability of a measure to predict the correct number of clusters in a dataset and its merit relative to the other measures. The main result is a hierarchy of internal validation measures in terms of precision and speed, highlighting some of their merits and limitations not reported before in the literature. This hierarchy shows that the faster the measure, the less accurate it is. In order to reduce the time performance gap between the fastest and the most precise measures, the technique of designing fast approximation algorithms is systematically applied. The end result is a speed-up of many of the measures studied here that brings the gap between the fastest and the most precise within one order of magnitude in time, with no degradation in their prediction power. Prior to this work, the time gap was at least two orders of magnitude.
He Votes or She Votes? Female and Male Discursive Strategies in Twitter Political Hashtags

In this paper, we conduct a study about differences between female and male discursive strategies when posting in the microblogging service Twitter, with a particular focus on the hashtag designation process during political debate. The fact that men and women use language in distinct ways, reverberating practices linked to their expected roles in the social groups, is a linguistic phenomenon known to happen in several cultures and that can now be studied on the Web and on online social networks in a large scale enabled by computing power. Here, for instance, after analyzing tweets with political content posted during Brazilian presidential campaign,we found out that male Twitter users, when expressing their attitude toward a given candidate, are more prone to use imperative verbal forms in hashtags, while female users tend to employ declarative forms. This difference can be interpreted as a sign of distinct approaches in relation to other network members: for example, if political hashtags are seen as strategies of persuasion in Twitter, imperative tags could be understood as more overt ways of persuading and declarative tags as more indirect ones. Our findings help to understand human gendered behavior in social networks and contribute to research on the new fields of computer-enabled Internet linguistics and social computing, besides being useful for several computational tasks such as developing tag recommendation systems based on users' collective preferences and tailoring targeted advertising strategies, among others.
Investigations on Detection Model of Large Scale Rotation Shaft Torsional Vibration in Precision Heavy Machinery

For solving on-line extract torsional vibrating parameter detecting and metrical problem of spanless point of complex and precise heavy machinery large scale rotary driving shaft, a new torsional vibration continuous shaft segment mass detecting and analytic model was proposed by using kinetics theory and measuring theory. The new method established the vibrational transmission relationship between the measurable point and the spanless point. New model was verified correctness, feasibility and effectiveness by means of gathering torque signal in the production field and comparing the consistency between measured value and theoretical calculation value of spanless point torque signal. Based on the experiment’s result, new model can supply a engineered means for acquiring the vibration detailed information of the point on precise heavy machinery large scale driving shaft which was inconvenience fitted sensor. It also can program composition for implement on-line monitor and fault forecast on rotary shaft and then guarantee the precise heavy complex rotary machine even running.
A theory of regression testing for behaviourally compatible object types

A behavioural theory of object compatibility is presented, which has implications for object‐oriented regression testing. The theory predicts that only certain models of state refinement yield compatible types, dictating the legitimate design styles to be adopted in object statecharts. The theory also predicts that conformity‐testing using regression tests is inadequate. Functionally complete test‐sets that are applied as regression tests to subtype objects are usually expected to cover the functionality of the original type, even though they are clearly not expected to cover extra functionality introduced in the subtype. However, such regression testing is proven to cover strictly less than the original state‐space in the new context and so provides much weaker confidence than expected. A different retesting model is proposed, based on full automatic test regeneration from the subtype's specification. This method can guarantee equivalent levels of confidence after retesting. The behavioural conformity desired by regression testing can then be proven by verification in the theory. Copyright © 2006 John Wiley & Sons, Ltd.
Mesoscale inversion: first results from the CERES campaign with synthetic data

We investigate the ability of a mesoscale model to reconstruct CO2 fluxes at regional scale. Formally, we estimate the reduction of error for a CO2 flux inversion at 8 km resolution in the South West of France, during four days of the CarboEurope Regional Experiment Strategy (CERES) in spring 2005. Measurements from two towers and two airplanes are available for this campaign. The lagrangian particle dispersion model LPDM was coupled to the non- hydrostatic model Meso-NH and integrated in a matrix inver- sion framework. Impacts of aircraft and tower measurements are quantified separately and together. We find that the con- figuration with both towers and aircraft is able to significantly reduce uncertainties on the 4-day averaged CO2 fluxes over about half of the 300◊300 km 2 domain. Most of this reduc- tion comes from the tower measurements, even though the impact of aircraft measurements remains noticeable. Imper- fect knowledge of boundary conditions does not significantly impact the error reduction for surface fluxes. We test alter- native strategies to improve the impact of aircraft measure- ments and find that most information comes from measure- ments inside the boundary layer. We find that there would be a large improvement in error reduction if we could improve our ability to model nocturnal concentrations at tower sites.
The Impact of Creativity and Information Load on Escalation of Commitment

This experimental study analyzes how two key factors, information load and creativity, influence decision making in escalation situations in which decision makers reinvest further resources in a losing course of action even when information indicates that the IT project is performing poorly and should be discontinued. Whereas previous studies focus on the complexity of information, this study investigates how different quantities of information influence escalation of commitment and how information load interacts with a decision maker’s interpretation of negative feedback regarding earlier decisions. In situations in which escalation of commitment can occur, information load is relevant for decision making for two reasons. First, in escalation situations in which decision makers face negative feedback, information load exacerbates the tendency to escalate commitment. Second, when an escalation tendency is absent, information load can lead to a decision to continue a losing course of action, which can be mistaken for escalation of commitment. Furthermore, decision makers with a higher level of creativity tend to invest more than decision makers with a lower level of creativity. Surprisingly, creative decision makers do not change their behavior when facing negative consequences while less creative decision makers show an escalation of commitment tendency.
Texture mapping 3 D planar models of indoor environments with noisy camera poses

Automated 3D modeling of building interiors is used in applications such as virtual reality and environment mapping. Texturing these models allows for photo-realistic visualizations of the data collected by such modeling systems. While data acquisition times for mobile mapping systems are considerably shorter than for static ones, their recovered camera poses often suffer from inaccuracies, resulting in visible discontinuities when successive images are projected onto a surface for texturing. We present a method for texture mapping that starts by selecting images whose camera poses are well-aligned in two dimensions. We then align images to geometry as well as to each other, producing visually consistent textures even in the presence of inaccurate surface geometry and noisy camera poses. The final step is to select and composite images into a texture mosaic. The effectiveness of the proposed method is demonstrated on a number of different indoor environments.
A Study on Online Aspect Mining

In aspect-oriented programming, we can encapsulate cross-cutting concerns scattered over many modules in a system. There are many stud- ies for detecting candidates of cross-cutting concerns called aspect mining, and automation of it. However, existing all aspect mining tools are batch processing system aiming for large systems. Compared with ex- isting batch-type tools, this paper propose an online- type aspect mining tool. The online-type aspect min- ing tool enables us to make cross-cutting concerns as- pect modules during a coding stage. Therefore, it is possible to use it also for a small-scale project which has just started.
From literal meaning to veracity in two hundred milliseconds

Do the integration of semantic information and that of world knowledge occur simultaneously or in sequence during sentence processing? To address this question, we investigated event-related brain potentials elicited by the critical word of English sentences in three conditions: (1) correct; (2) semantic violation; (3) world knowledge violation (semantically correct but factually incorrect). Critically, we opted for low constraint sentence contexts (i.e., whilst being semantically congruent with the sentence context, critical words had low cloze probability). The processing of semantic violations differed from that of correct sentences as early as the P2 time-window. In the N400 time-window, the processing of semantic and world knowledge violations both differed significantly from that of correct sentences and differed significantly from one another. Overall, our results show that the brain needs approximately 200 ms more to detect a world knowledge violation than a semantic one.
Large-Scale Syntactic Processing : Parsing the Web Final Report of the 2009 JHU CLSP Workshop

Scalable syntactic processing will underpin the sophisticated language technology needed for next generation information access. Companies are already using nlp tools to create web-scale question answering and “semantic search” engines. Massive amounts of parsed web data will also allow the automatic creation of semantic knowledge resources on an unprecedented scale. The web is a challenging arena for syntactic parsing, because of its scale and variety of styles, genres, and domains. The goals of our workshop were to scale and adapt an existing wide-coverage parser to Wikipedia text; improve the efficiency of the parser through various methods of chart pruning; use self-training to improve the efficiency and accuracy of the parser; use the parsed wiki data for an innovative form of bootstrapping to make the parser both more efficient and more accurate; and finally use the parsed web data for improved disambiguation of coordination structures, using a variety of syntactic and semantic knowledge sources. The focus of the research was the c&c parser (Clark and Curran, 2007c), a stateof-the-art statistical parser based on Combinatory Categorial Grammar (ccg). The parser has been evaluated on a number of standard test sets achieving state-of-the-art accuracies. It has also recently been adapted successfully to the biomedical domain (Rimell and Clark, 2009). The parser is surprisingly efficient, given its detailed output, processing tens of sentences per second. For web-scale text processing, we aimed to make the parser an order of magnitude faster still. The c&c parser is one of only very few parsers currently available which has the potential to produce detailed, accurate analyses at the scale we were considering.
HYBRID NEURAL MODELS FOR TIME-SERIES FORECASTING

Abstract Three new hybrid neural models which are based upon the basic neural model put forth by McCulloch and Pitts (Haykin, 1999) and the compensatory neural models by Sinha et al. (2000), (2001) are proposed in this paper. The basic neural and the compensatory neural models are modified to take into account any linear dependence of the outputs on the inputs. This makes the hybrid models suitable for the solution of some complex problems such as chaotic nonlinear time-series and more simple problems such as linear time-series. These models are verified using a simulation example. It is shown that the hybrid neural models are superior to the basic neural model and the compensatory neural models for time-series forecasting problems.
An algorithm for computing the formal solutions of differential systems in the neighborhood of an irregular singular point

We discuss in this paper an algorithm for the computation of the formal solutions of differential systems in the neighborhood of an irregular singular point. In the reduction of the differential systems, we use its Arnold-Wasow's canonical form. We discuss also an algorithm for the reduction of the differential system to its Arnold-Wasow's canonical form. Then we discuss the results of a shearing transformation on this canonical form and we get the convergence of the algorithm. This paper and [3] consist of a complete study of the problem of computations of the formal solutions of differential systems in the neighborhood of a singular point (regular or irregular).
Rendering Molecular Surfaces using Order-Independent Transparency

In this paper we present a technique for interactively rendering transparent molecular surfaces. We use Puxels, our implementation of per-pixel linked lists for order-independent transparency rendering. Furthermore, we evaluate the usage of per-pixel arrays as an alternative for this rendering technique. We describe our real-time rendering technique for transparent depiction of complex molecular surfaces like the Solvent Excluded Surface which is based on constructive solid geometry. Additionally, we explain further graphical operations and extensions possible with the Puxels approach. The evaluation benchmarks the performance of the presented methods and compares it to other methods.
Rapid Screening of Lectins for Multivalency Effects with a Glycodendrimer Microarray

Multivalency is an important phenomenon in protein–carbohydrate interactions. In order to evaluate glycodendrimers as multivalent inhibitors of carbohydrate binding proteins, we displayed them on a microarray surface. Valencies were varied from 1 to 8, and corrections were made for the valencies so that all surfaces contained the same amount of the sugar ligand. Five different carbohydrates were attached to the dendrimers. A series of fluorescent lectins was evaluated, and for each of them a binding profile was obtained from a single experiment showing both the specificity of the lectin for a certain sugar and whether it prefers multivalent ligands or not. Very distinct binding patterns were seen for the various lectins. The results were rationalized with respect to the interbinding distances of the lectins.
Combination of graph theoretic grouping and time-frequency analysis for image segmentation

We introduce a nonparametric approach to multiscale segmentation of images using a hierarchical matrix analysis framework called diffusion wavelets. This approach benefits from the advantages of both graph theory and wavelet transform. Till now a broad range of multiscale transforms like wavelets (and other x-lets) have been introduced for image segmentation task. The graph theoretic formulation of grouping is also well-known to deal with this problem. The combination of multiscale transforms and graph based partitioning results in a scale-spectral method exploring through different scales of the image, over a great deal of spectral methods in graph partitioning. The method constructs multiscale basis functions and a series of dilation and orthogonalizations build a hierarchy, automatically. At each level, a set of basis functions is built by applying dyadic powers of a diffusion operator on the bases at the lower level. Two approaches are proposed for multiscale segmentation of images using diffusion wavelets. The first method is based on extended bases functions at each level and designing a competition between the bases value for partitioning. The second approach is defining a new distance for each level and clustering based on such distances.
Objective quality assessment for image retargeting based on hybrid distortion pooled model

With the increasing popularity of mobile devices, there are more and more screens with heterogeneous resolutions. In order to solve the mismatching problem of images displaying on different screens, various image retargeting techniques have been proposed. However, little effective objective quality assessment metric for image retargeting has been proposed. In this paper, we propose an objective image retargeting quality assessment method based on Hybrid Distortion Pooled Model (HDPM) considering image local similarity, content information loss and image structural distortion. The proposed HDPM method measures the retargeted image's local similarity based on matching the similar block by Scale-Invariant Features Transform (SIFT) features and computing the corresponding blocks' similarity by structural similarity (SSIM). Furthermore, the image content information loss in retargeted image, which is regarded as the SIFT feature loss, is taken into account. Besides, we also consider image's structural distortion in the proposed method, which is based on GLCM (Gray-level co-occurrence matrix). To evaluate the effectiveness of the proposed method, extensive experiments have been conducted, and the results show improved consistency between the proposed HDPM method and the corresponding subjective evaluations.
Mass spectrometry-based proteomic analysis of middle-aged vs. aged vastus lateralis reveals increased levels of carbonic anhydrase isoform 3 in senescent human skeletal muscle

The age-related loss of skeletal muscle mass and associated progressive decline in contractile strength is a serious pathophysiological issue in the elderly. In order to investigate global changes in the skeletal muscle proteome after the fifth decade of life, this study analysed total extracts from human vastus lateralis muscle by fluorescence difference in-gel electrophoresis. Tissue specimens were derived from middle-aged (47–62 years) vs. aged (76–82 years) individuals and potential changes in the protein expression profiles were compared between these two age groups by a comprehensive gel electrophoresis-based survey. Age-dependent alterations in the concentration of 19 protein spots were revealed and mass spectrometry identified these components as being involved in the excitation-contraction-relaxation cycle, muscle metabolism, ion handling and the cellular stress response. This indicates a generally perturbed protein expression pattern in senescent human muscle. Increased levels of mitochondrial enzymes and isoform switching of the key contractile protein, actin, support the idea of glycolytic-to-oxidative and fast-to-slow transition processes during muscle aging. Importantly, the carbonic anhydrase (CA)3 isoform displayed an increased abundance during muscle aging, which was independently verified by immunoblotting of differently aged human skeletal muscle samples. Since the CA3 isoform is relatively muscle-specific and exhibits a fibre type-specific expression pattern, this enzyme may represent an interesting new biomarker of sarcopenia. Increased levels of CA are indicative of an increased demand of CO2-removal in senescent muscle, and also suggest age-related fibre type shifting to slower-contracting muscles during human aging.