Miracle-dz/UniFAR-FTUs huggingface dataset:
2010 2nd International Conference on Industrial and Information Systems Study on Assessment Framework of Software Process in Agile

Agility means the capability of operating profitably in a competitive environment of continuous and unpredictable changes with software process regarded as one of its main method. The research focuses on the assessment of software process to support agUe. The assessment framework is constructed upon a series of competitive bases and agility attributes identified in the literature. The framework is validated through a survey research instrument, as weD analysis with statistical. The statistical analysis enabled us to determine the attributes identified as predictors for the set of agUe software process questions. The framework can be presented as a hierarchy like AHP techniques. The results will enable the organizer to identify the trends adopted by organization in the utllization of agile software process to gain competitive predominance. Software processes were rooted in the traditions of software engineering, structured methods, software life cycles, and software environments dating back to the late mainframe and early mid range eras . The maturity process fr�ework was directly adapted from ffiM's process grid, which was finally turned into the capability maturity mo�el for U.S. military use (Weber, Paulk & Wise 1992). Agde software development is basically a software engineering discipline-specific implementation of the gene�-purp ? se agile capabilities. Agile methods gained prommence m the late Internet and early personalized eras to accommodate the uniquely flexible nature of Internet technologies. Agile methods are an approach for managing the development of software, which is based upon obtaining early customer feedback on a large scale software releases. In order to survive in a business environment in continuous change, organizations must become agile. Agile manufacturing is to give organizations the capability to survive and prosper in an environment of continuous and unpredictably change driven by customer­ designed products and services [1]. The success in the implementation of agility in manufacturing organizations depends on a number of different enablers. Agile proponents have noted that "each situation calls for a different methodology". Thus, one of the principles behind agile manifesto suggests that the team should regularly reflect on how to become more effective, and fine-tune and adjust its behavior accordingly Information systems are among those enablers addressed in the literature [2], making imperative the definition of an adequate evaluation framework. In the literature, Because the definition of information systems to support the concept of agility is superficial, a deep understanding of the concept required to design assessment framework of agility process that satisfy …
Comparison of three updating schemes using artificial neural network in flow forecasting

Abstract. Three updating schemes using artificial neural network (ANN) in flow forecasting are compared in terms of model efficiency. The first is the ANN model in the simulation mode plus an autoregressive (AR) model. For the ANN model in the simulation model, the input includes the observed rainfall and the previously estimated discharges, while the AR model is used to forecast the flow simulation errors of the ANN model. The second one is the ANN model in the updating mode, i.e. the ANN model uses the observed discharge directly together with the observed rainfall as the input. In this scheme, the weights of the ANN model are obtained by optimisation and then kept fixed in the procedure of flow forecasting. The third one is also the ANN model in the updating mode; however, the weights of the ANN model are no longer fixed but updated at each time step by the backpropagation method using the latest forecast error of the ANN model. These three updating schemes are tested for flow forecasting on ten catchments and it is found that the third updating scheme is more effective than the other two in terms of their efficiency in flow forecasting. Moreover, compared to the first updating scheme, the third scheme is more parsimonious in terms of the number of parameters, since the latter does not need any additional correction model. In conclusion, this paper recommends the ANN model with the backpropagation method, which updates the weights of ANN at each time step according to the latest forecast error, for use in real-time flow forecasting. Keywords: artificial neural network (ANN), updating, flow forecasting, backpropagation method
Energy-efficient dual-voltage soft real-time system with (m,k)-firm deadline guarantee

Soft real-time systems can tolerate some occasional deadline misses. This feature provides a unique opportunity to reduce system's energy consumption. In this paper, we study the system with (m,k)-firm deadline, a popular model for soft real-time systems. It basically requires at least m successful completions in any k consecutive executions. Our goal is to design such system with dual supply voltages for energy efficiency. To reach this goal, we first propose an on-line greedy deterministic scheduler that provides the (m,k)-firm guarantee with the provably minimum energy consumption. We then develop a novel exact method to compute the scheduler's average energy consumption per iteration. This leads us to the numerical solution to the voltage set-up problem, which seeks for the values of the two supply voltages to achieve the most energy efficiency with (m,k)-firm guarantee. Simulation shows that dual-voltage system can reduce significant amount of energy over single voltage system. Our numerical method finds the best voltage set-ups in seconds, while it takes hours to obtain almost identical solutions by simulation.
Hierarchical Density-Based Clustering for Multi-Represented Objects

In recent years, the complexity of data objects in data mining applications has increased as well as their plain numbers. As a result, there exist various feature transformations and thus multiple object representations. For example, an image can be described by a text annotation, a color histogram and some texture features. To cluster thesemulti-representedobjects,dedicateddataminingalgorithms have been shown to achieve improved results. In this paper, we will therefore introduce a method for hierarchical density-based clustering of multi-represented objects which is insensitive w.r. t. the choice of parameters. Furthermore, we will introduce a theoretical model that allows us to draw conclusions about the interaction of representations. Additionally, we will show how these conclusions can be used for defining a suitable combination method for multiple representations. To back up the usability of our proposed method, we present encouraging results for clustering a real world image data set that is described by 4 dierent representations.
Tensor-Based Active Appearance Model

The active appearance model (AAM) is a well-known model that can represent a nonrigid object effectively. However, the fitting result is often unsatisfactory when the input image has pose, expression, and illumination variations. To overcome this problem, we propose a tensor-based AAM which consists of two kinds of tensors: image tensor and model tensor. The image tensor is used to estimate the image variation such as the pose, the expression, and the illumination by finding the basis subtensor with minimal reconstruction error. The model tensor is used to generate the specific AAM basis vectors by indexing the model tensor in terms of the estimated image variations. Experimental results show that the proposed tensor-based AAM reduces the fitting error of the conventional AAM by about four pixels.
The Fine-Scale Functional Correlation of Striate Cortex in Sighted and Blind People

To what extent are spontaneous neural signals within striate cortex organized by vision? We examined the fine-scale pattern of striate cortex correlations within and between hemispheres in rest-state BOLD fMRI data from sighted and blind people. In the sighted, we find that corticocortico correlation is well modeled as a Gaussian point-spread function across millimeters of striate cortical surface, rather than degrees of visual angle. Blindness produces a subtle change in the pattern of fine-scale striate correlations between hemispheres. Across participants blind before the age of 18, the degree of pattern alteration covaries with the strength of long-range correlation between left striate cortex and Broca's area. This suggests that early blindness exchanges local, vision-driven pattern synchrony of the striate cortices for long-range functional correlations potentially related to cross-modal representation.
Graph theory model based automatic test platform design

Traditional functional testing tools are always based on recording/ playback mode, this need to rely on the reality of system GUI and source code, test activities has to wait to begin until the end of software development life cycle, the cost for fixing bugs is very… With the present and widely accept of test-first concept, model-based testing has be presented. This paper designs an automatic test platform which based on graph theory model. In this platform, System requirement is formulized described based on graph theory using UML, and abstracted into a graph model. This makes it possible to automatically check the requirement correctness according to model checking technique; the generated test cases could also be used in integration test phase.
Security Modeling with BDMP: From Theory to Implementation

This paper discusses the implementation and use of the BDMP (Boolean logic Driven Markov Processes) formalism, recently adapted to graphical attack modeling. Theoretically, it offers an attractive trade-off between readability, scalability, modeling power and quantification capabilities. In practice, efficient model construction and analysis need complementary tools and enhancements. They have been developed only once the implementation and the first security studies have been realized. In particular, attack sequence filtering based on attacker profiles and sensitivity analysis provide a significant help. Perspectives include the addition of a security pattern library or the connection with other modeling frameworks.
Quantifying Uncertainty Towards Information-Centric Unmanned Navigation

Highly imperfect, inconsistent informa- tion and incomplete a priori knowledge introduce uncer- tainty in sensor-centric unmanned navigation systems. Understanding and quantifying uncertainty yields a measure of useful information that plays a critical role in several robotic navigation tasks such as sensor fu- sion, mapping, localization, path planning, and con- trol. In this paper, within a probabilistic framework, we demonstrate the utility of estimation- and information- theoretic concepts towards quantifying uncertainty us- ing entropy and mutual information metrics in various contexts of unmanned navigation via experimental re- sults.
Exploiting idle CPU cores to improve file access performance

Many-core CPUs require many parallel computation tasks to reach their full potential because CPU cores become idle if they do not have enough computation tasks. How best to utilize a number of cores in many-core CPUs should be examined. In this paper, we propose exploitation of idle cores for improving file access performance. Idle cores are used to extract file access patterns from access logs and the extracted patterns are used to improve file cache efficiency by reordering the LRU (Least Recently Used) list based on the extracted patterns. Data mining techniques are used to extract access patterns to reduce computation overhead. Our method was evaluated by simulation and also implemented on Linux kernel 2.6. 26 as a prototype system. In the simulation experiment, our method improved the cache-hit ratio up to 1.09% on DBT-2 (TPC-C) trace logs. Our prototype implementation on Linux improves DBT-2 performance up to 5.24% on a real machine.
Turbo detection for UWB communication

In February 2002, the Federal Communications Commission allocated huge frequency band (3.6 - 10.1 GHz) for UWB communication. Now many wireless communication experts are considering using a variety of techniques to utilize the huge bandwidth. Based on the fact that the large bandwidth of UWB waveform significantly increases the ability of the receiver to resolve the different reflections in the channel, most receivers are basically developed from the RAKE receiver [S. Roy et al., Feb 2004]. This paper proposes a modified receiver for UWB communication system. The receiver is composed of a symbol detector and a channel decoder separated by a deinterleaver. The principle of turbo-code and an iterative detecting-decoding process is applied in the receiver.
Character recognition by adaptive statistical similarity

Handwriting recognition and OCR systems need to cope with a wide variety of writing styles and fonts, many of them possibly not previously encountered during training. This paper describes a notion of Bayesian statistical similarity and demonstrates how it can be applied to rapid adaptation to new styles. The ability to generalize across different problem instances is illustrated in the Gaussian case, and the use of statistical similarity Gaussian case is shown to be related to adaptive metric classification methods. The relationship to prior approaches to multitask learning, as well as variable or adaptive metric classification, and hierarchical Bayesian methods, are discussed. Experimental results on character recognition from the NIST3 database are presented.
Migration to Cloud as Real Option: Investment Decision under Uncertainty

If cloud is so good then why aren't companies using it more? In this paper we look at how companies should make a decision to move some IT services or their IT infrastructure into the cloud. The move may initially look attractive in that it offers cost benefits but there is also considerable uncertainty, not least around security and information stewardship. Within the paper we propose the use of a real option model to help think about when to switch to cloud based on the expected benefits, uncertainties and the value a company puts on money.
Envelope Theorems and Dilation with Convex Conditional Previsions

This paper focuses on establishing envelope theorems for convex conditional lower previsions, a recently investigated class of imprecise previsions larger than coherent imprecise conditional previsions. It is in particular discussed how the various theorems can be employed in assessing convex previsions. We also consider the problem of dilation for these kinds of imprecise previsions, and point out the role of convex previsions in measuring conditional risks.
Binding patterns of 3-methylcholanthrene to replicating and nonreplicating DNA of preneoplastic nodule of mouse mammary gland.

The binding of 3-methylcholanthrene (MC) to DNA and its relation to DNA synthesis in the mammary tumor virus-free hyperplastic alveolar nodule outgrowth of BALB/c mouse mammary gland were determined. As measured by tritium radioactivity, the carcinogen was found to bind to nodule DNA at 12 and 24 hr after a single intragastric feeding of 500 µg MC-3H to mice carrying the outgrowth. The binding of MC to DNA in vivo was slightly lower when DNA synthesis in the nodule cells was more active at 4 weeks compared to the period of reduced DNA synthesis at 8 weeks after fat-pad transplantation. In a separate experiment both 4- and 8-week-old outgrowths were incubated in vitro and DNA was double labeled with MC-3H and thymidine-2-14C. During the 6-hr incubation in a shaking water bath at 37°, both MC-3H and thymidine-2-14C radioactivity was present in nodule DNA, but no direct relation to MC-3H and thymidine-2-14C radioactivity was present between the 4- and 8-week-old nodule outgrowth cells in vitro . When the nodule DNA, double labeled with 5-bromodeoxyuridine and MC-3H, was centrifuged to equilibrium in CsCl, the radioactive carcinogen was almost equally distributed between the preexisting and the newly synthesized, 5-bromodeoxyuridine-labeled heavier fractions of the nodule DNA. The results indicate that binding of MC to DNA of the preneoplastic nodule outgrowth of mouse mammary gland is not influenced by or dependent upon the rate of DNA synthesis in the tissue.
Public Goods, Public Gains: Calculating the Social Benefits of Public R&D

In Public Goods, Public Gains, Link and Scott discuss the systematic application of alternative evaluation methods to estimate the social benefits of publicly-financed research and development (R&D). The authors argue that economic theory should be the guiding criterion for any method of program evaluation because it focuses attention on the value and the opportunity costs of the program. The evaluation methods discussed and illustrated are both economics and, for comparison, non-economics based. The book is motivated by four foundation chapters that discuss government's role in innovation from the perspective of economic theory, review public accountability issues from both a constitutional and an historical perspective, overview systematic approaches to program evaluation, and describe the evaluation metrics typically used. Four case studies illustrate the four alternative evaluation approached discussed. These case studies are for the U.S. Advanced Technology Program's intramural research awards program, the U.S. National Institute of Standards and Technology's research on wavelength references for optical fiber communications, the U.S. Malcolm Balridge National Quality Award, and the Advanced Technology Program's focused program on the integration of manufacturing applications. Available in OSO: http://www.oxfordscholarship. com/oso/public/content/economicsfinance/9780199729685/toc.html
Program Evaluation Using Spatial Instruments: Evidence from Indonesia*

This paper estimates the eects of non-randomly distributed government programs in Indonesia using a new instrumental variables technique. Based on the maintained assumptions that governments are responsive to regional attributes and not to individual characteristics, and that conditional on the presence of programs, individual outcomes are uncorrelated with mean characteristics, regional level averages of individual attributes may be used as instruments to correct for the endogeneity of government programs. Three categories of spatial instruments are constructed; averages of exogenous variables over districts, averages over neighboring districts, and averages over provinces. An application of these instruments to cross-sectional data from Indonesia demonstrates that public programs such as grade schools, junior schools, senior schools, maternal/child health clinics, health clinics associated with the Puskesmas program, and family planning clinics, have signicant eects. Moreover, a comparison of these instrumental variables estimates with \naive estimates" that treat programs exogenously clearly highlights the extent of bias.
Reinforcement Learning of Robotic Legged Locomotion

Humans and animals show a remarkable level of proficiency in their ways of locomotion. They exploit the dynamics of the whole body to perform a variety of motions such as jumping and running. Hereby, the elasticity in the muscles and tendons carries a key role in enabling robust, dynamic and energy efficient locomotion [1]. At the Autonomous Systems Lab, we have developed the robotic leg ScarlETH [2] as a test bed to study the mechanism that can be observed in nature. The bio-inspired articulated leg is electrically driven by highly compliant Series Elastic Actuators (SEA) [3] in the hip and knee joints. The properties of the springs in the joints can be compared to the elasticity of the tissue in nature. Our goal is to maximize ScarlETH’s maneuver performance and locomotion efficiency by developing controllers that excite the robot in step with the dynamics of the system.
Why Keep Arguing? Predicting Engagement in Political Conversations Online

Individuals acquire increasingly more of their political information from social media, and ever more of that online time is spent in interpersonal, peer-to-peer communication and conversation. Yet, many of these conversations can be either acrimoniously unpleasant or pleasantly uninformative. Why do we seek out and engage in these interactions? Who do people choose to argue with, and what brings them back to repeated exchanges? In short, why do people bother arguing online? We develop a model of argument engagement using a new dataset of Twitter conversations about President Trump. The model incorporates numerous user, tweet, and thread-level features to predict user participation in conversations with over 98% accuracy. We find that users are likely to argue over wide ideological divides, and are increasingly likely to engage with those who are different from themselves. In addition, we find that the emotional content of a tweet has important implications for user engagement, with negative and unpleasant tweets more likely to spark sustained participation. Although often negative, these extended discussions can bridge political differences and reduce information bubbles. This suggests a public appetite for engaging in prolonged political discussions that are more than just partisan potshots or trolling, and our results suggest a variety of strategies for extending and enriching these interactions.
Inapproximabilty of Densest κ-Subgraph from Average Case Hardness

We establish two results about the inapproximability of the Densest κ-Subgraph (DκS) problem. Both results are of similar flavor: ruling out constant factor approximations in polynomial time for the DκS problem under an “average case” hardness assumption. The first result asserts that if Random k-AND formulas are hard to distinguish from ones that are 2−c √ k satisfiable, then the Densest κ-Subgraph problem is hard to approximate to within any constant factor. The second result, which is of a similar flavor, asserts that if the problem of finding a planted clique of size n in the random graph G(n, 1/2) is hard, then so is approximating the Densest κ-Subgraph to within any constant factor, for a subgraph of size κ = N1− for any 2/3 ≥ > 0 in an N vertex graph. Depending on the hardness of the Hidden Clique problem, this result carries over to superconstant hardness factors for approximating DκS. Our result also implies the optimality (assuming appropriate hardness of the planted clique problem) of an existing algorithm by Feige and Seltser [FS97], for the problem of distinguishing between a graph containing a clique of size κ and one in which the densest subgraph of size κ is of density at most δ. Both results are based on gap-amplification arguments: we believe that these arguments can be useful elsewhere as well. ∗Sackler School of Mathematics and Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel and Institute for Advanced Study, Princeton, New Jersey, 08540, USA. Email: nogaa@tau.ac. il. Research supported in part by an ERC Advanced grant, by a USA-Israeli BSF grant and by NSF grant No. DMS-0835373. †Princeton University. Email: {arora,rajsekar,oweinste}@cs.princeton. edu ‡MIT. Email: dmoshkov@mit.edu