Miracle-dz/UniFAR-FTUs huggingface dataset:
An Experiment to Assess the Cost-Bene ts of Code Inspections inLarge Scale Software

We are conducting a long-term experiment (in progress) to compare the costs and beneets of several diierent software inspection methods. These methods are being applied by professional developers to a commercial software product they are currently writing. Because the laboratory for this experiment is a live development eeort, we took special care to minimize cost and risk to the project, while maximizing our ability to gather useful data. This article has several goals: (1) to describe the experiment's design and show how we used simulation techniques to optimize it, (2) to present our preliminary results and discuss their implications for both software practitioners and researchers, and (3) to discuss how we expect to modify the experiment in order to reduce potential risks to the project. For each inspection we randomly assign 3 independent variables: (1) the number of reviewers on each inspection team (1, 2 or 4), (2) the number of teams inspecting the code unit (1 or 2), and (3) the requirement that defects be repaired between the rst and second team's inspections. The reviewers for each inspection are randomly selected without replacement from a pool of 11 experienced software developers. The dependent variables for each inspection include inspection interval (elapsed time), total eeort, and the defect detection rate. Program 0 To date we have completed 34 of the planned 64 inspections. Our preliminary results challenge certain long-held beliefs about the most cost-eeective ways to conduct inspections and raise some questions about the feasibility of recently proposed methods.
A Review of the e-Government Maturity Models: Are They Still Relevant?

In the past, a number of methodologies and approaches have been created to evaluate the development of e-Government and the public sector's digital transformation. Numerous e-Government maturity models have been created to close this gap. The variety of models, though, has come under criticism from several academics. The purpose of this research is to examine whether e-Government maturity models can still be regarded as relevant through the examination of 39 e-Government maturity models that have been established throughout the year. The results indicate that due to the numerous internal and external relevant inflectional factors that have not been taken into consideration by the models under study, e-Government maturity models have a limited ability to represent the growth of e-Government.
Optimal Solutions for Sparse Principal Component Analysis

Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. We formulate a new semidefinite relaxation to this problem and derive a greedy algorithm that computes a full set of good solutions for all target numbers of non zero coefficients, with total complexity O(n3), where n is the number of variables. We then use the same relaxation to derive sufficient conditions for global optimality of a solution, which can be tested in O(n3), per pattern. We discuss applications in subset selection and sparse recovery and show on artificial examples and biological data that our algorithm does provide globally optimal solutions in many cases.
Symmetric Publish/Subscribe via Constraint Publication

Abstract : Current publish / subscribe systems offer a range of expressive subscription languages for constraints. However, classical systems restrict the publish operation to be a single published object that contains only constants and no constraints. We introduce symmetric publish / subscribe, a novel generalization of publish / subscribe where both publications and subscriptions contain constraints in addition to constants. Published objects are matched to subscriptions by computing the intersection of their constraints. This generalization improves the performance of classical publish / subscribe systems and introduces a new class of applications for publish / subscribe. This paper describes the core algorithms of our publish / subscribe implementation, evaluates the performance of these algorithms both analytically and empirically, and documents cases where the additional expressive power of symmetric publish / subscribe can be gained with minimal additional computational cost compared to the classical system.
Supplementary Material to Private Protocols for U -Statistics in the Local Model and Beyond

We start by introducing some notations. We denote by k the number of bins of the quantization and by π : X → [k] the quantization scheme such that for any data point x ∈ X , π(x) denotes the quantized version of x (i.e., its image under π). Let ei denote the vector of length k with a one in the i-th position and 0 elsewhere. A kernel function fA on the quantized domain [k] is fully described by a matrix A ∈ Rk×k such that fA(i, j) = Ai,j = ei Aej. We denote by UA,π = Eμ×μ[Aπ(x),π(y))] the quantized analogue to the quantity Uf . The proposed protocol, described in Algorithm 1, applies generalized randomized response on data quantized with π and uses this to compute an unbiased estimate of a quantized U -statistic. The choice of the quantized kernel A will be discussed below. Crucially, there are two sources of error in this protocol. More precisely, the mean squared error of the estimate Ûf,n returned by Algorithm 1 can be bounded as follows:
Visualizing the computational intelligence field

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Wavelet based watermarking on 3D irregular meshes

This paper proposes a blind and high-capacitywatermarking framework for 3D triangle meshes. The basic idea is to use a substitutive procedure in the transform domain to embed the secret message. Our proposal can be applied to regular as well as irregular meshes by using irregular wavelet-based analysis. The watermark is inserted in an appropriate resolution level by quantizing the norms of wavelet coefficients vectors. Simulation results show that our watermarking framework is robust to common geometric attacks and can provide relatively high data embedding rate whereas keep a relative lower distortion.
Base Deployment for Multicast Listener Support in Proxy Mobile IPv6 (PMIPv6) Domains

This document describes deployment options for activating multicast listener functions in Proxy Mobile IPv6 domains without modifying mobility and multicast protocol standards. Similar to Home Agents in Mobile IPv6, PMIPv6 Local Mobility Anchors serve as multicast subscription anchor points, while Mobile Access Gateways provide MLD proxy functions. In this scenario, Mobile Nodes remain agnostic of multicast mobility operations.
Criticality-driven Token Dataflow Optimizations for FPGA-based Sparse LU Factorization Removed for blind review

Performance of FPGA-based token dataflow architectures is often limited by the long tail distribution of parallelism in the compute paths of dataflow graphs. This is known to limit speedup of dataflow processing of Sparse LU factorization to only 3– 10× over CPUs. In this paper, we show how to overcome these limitations by exploiting criticality information along compute paths; both statically during graph pre-processing and dynamically at runtime. We statically restructure the high-fanin dataflow chains using a technique inspired by Huffman encoding where we provide faster routes for late arriving inputs as predicted through our timing models. We also perform a fanout decomposition and selective node replication in order to distribute serialization costs across multiple PEs. This static restructuring overhead is small; roughly the cost of a single iteration, and is amortized across 1000s of LU iterations at runtime. Additionally, we modify the dataflow firing rule in hardware to prefer critical nodes when multiple nodes are ready for dataflow evaluation. We compute this criticality offline through a one-time slack analysis and implement this in hardware at virtually no cost through a trivial address encoding ordered by criticality. For dataflow graphs extracted for sparse LU factorization, we demonstrate up to 2.5× (mean 1.21×) improvement when using the static preprocessing alone, a 2.4× (mean 1.17×) improvement when using only runtime optimizations alone while an overall 2.9× (mean 1.39×) improvement when both static and runtime optimizations are enabled across a range of benchmark problems.
Problems in the control of anthracite mine fires: a case study of the Centralia mine fire (August 1980)

The Bureau of Mines has conducted a study of mine fires in the Anthracite Region of Pennsylvania to determine the most effective 2pproaches to fire control in this area. The problems involved in controlling an anthracite fire include the geological and mining conditions in this area, the propagation characteristics of anthracite, the fire control methods available, the hazards associated with these fires, and the marginal effectiveness of past fire control projects. From this genecal background, fire control options were discussed as they apply in ISBO to the Centralia mine fire. Probable effectiveness, feasibility, and costs were assessedv The options evaluated include four excavation plans, flooding, hydraulic flushing, water curtain isolation, mining to consteuet an underground ~arrier, burnout control, and relocation of the community. The most advantageous from a technical viewpoint is an excavation method combining complete excavation of the fire zone within the borough with trenching to confine the remaining fire. It appears that for many anthracite fires, as for the Centralia mine fire, there are no available control measures that have a moderate cost, are proven effective, and produce minimal disruption on the surface. 'Supervisory research chemist, Pittsburgh Research Center, Bureau of Mines, Pittsburgh, PA. 2supervisory mining engineer, Office of Surface Mining, Wilkes-Barre, PA. 3Writer-editor, Pittsburgh Research Center, Bureau of Mines, Pittsburgh, PA. 4Chemist, Pittsburgh Research Center, Bureau of Mines, Pittsburgh, PA. 5Supervisory mining engineer, Office of Surface Mining, Schuylkill Haven, PA.
Effective Dependency Rule-based Aspect Extraction for Social Recommender Systems

Social recommender systems capitalise on product reviews to generate recommendations that are both guided by experiential knowledge and are explained by user opinions centred on important product aspects. Therefore, having an effective aspect extraction algorithm is crucial. Previous work has shown that dependency relation approaches perform well in this task. However, they can also lead to erroneous extractions. This paper proposes an effective aspect extraction approach that combines strengths of both dependency relations and frequent noun approaches. Further, we demonstrate how aspect-level sentiment analysis can be used to enrich product representations and thereby positively impact recommendation effectiveness. We empirically evaluate our proposed approach with the objective to recommend products that are ‘better’ than a given query product. A computational measure of ‘better’ is used in our experiments with five real-world datasets. Results show that our proposed approach achieves significantly better results than the existing state-of-the-art dependency-based methods in recommendation tasks.
Universal delay-limited simulation

We consider the problem of universal delay-limited simulation of an unknown information source of a certain parametric family (e.g., the family of memoryless sources or Markov sources of a given order), given a training sequence from that source and a stream of purely random bits. In the delay-limited setting, the simulation algorithm generates a random sequence sequentially, by delivering one symbol for each training symbol that is made available after a given initial delay, whereas the random bits are assumed to be available on demand. The goal of universal simulation is that the probability law of the generated sequence be identical to that of the training sequence, with minimum mutual information between the random processes generating both sequences. We characterize the optimal delay-limited simulation scheme and upper-bound the expected number of random bits it consumes. As in the non-sequential case, this upper bound is related to the entropy rate of the source
A New Soft Sensor Modeling Method Based on Modified AdaBoost with Incremental Learning

Aiming at the characteristics of soft sensors, an ensemble learning algorithm AdaBoost.RT is used to establish the soft sensor models. According to the shortcoming of AdaBoost.RT and the difficulties of on-line updating for soft sensor models, a self-adaptive modifying threshold φ and an incremental learning method are proposed for improving the performance of original AdaBoost.RT. The new modified AdaBoost.RT can overcome the disadvantages of original AdaBoost.RT and update the soft sensor model in real time. The new method is used to establish the soft sensor model of molten steel temperature in 300t LF. Practical production data are used to test the model. The results demonstrate that the new soft sensor model based on modified AdaBoost.RT can improve the prediction accuracy and has good ability of update.
Latent Semantic Indexing with Selective Query Expansion

This thesis outlines research and experiments in Information Retrieval, specifically with the search method Latent Semantic Indexing (LSI). The design and implementation of a framework for performing LSI queries on large text databases with a focus on machine learning is documented. Also described and analyzed is our participation in TREC in 2010 and 2011. Finally, we describe our next LSI-based task, Cyberbullying detection, which involves applying our system to Internet-chat-based datasets to discover instances of online harassment. The results of our research show that EDLSI is an effective technique for E-Discovery. Preliminary observations also show promise for EDLSI’s effectiveness in Cyberbullying detection. We have shown that selective query expansion can be a useful mechanism for improving retrieval results when a specific initial query is provided. We believe that queries that are stated in general terms, however, may suffer from “expansion in the wrong direction” when certain iterative approaches to incorporating relevance feedback information are added into the search process.
A Fast Way to Produce Optimal Fixed-Depth Decision Trees.

Decision trees play an essential role in many classification tasks. In some circumstances, we only want to consider fixeddepth trees. Unfortunately, finding the optimal depth-d decision tree can require time exponential in d. This paper presents a fast way to produce a fixed-depth decision tree that is optimal under the Naı̈ve Bayes (NB) assumption. Here, we prove that the optimal depth-d feature essentially depends only on the posterior probability of the class label given the tests previously performed, but not on either the identity nor the outcomes of these tests. We can therefore precompute, in a fast pre-processing step, which features to use at the final layer. This results in a speedup of O(n/ log n), where n is the number of features. We apply this technique to learning fixed-depth decision trees from standard datasets from the UCI repository, and find this model improves the computational cost significantly. Surprisingly, this approach still yields relatively high classification accuracy, despite the NB assumption.
Does Organizational Structure Matter? an Adaptive Simulation Approach for Investigating Information Processing Structures in Organizations

We adopt the view of organizations as information processingentities. As such, we propose that various exogenous and endogenousfactors should affect the performance of organizations with respect toinformation processing tasks. We present a methodology for modelingorganizational structures and for determining which organizationalstructures, if any, distinguish themselves given various constraints.Our methodology relies upon computer simulations that combineMonte Carlo methods and genetic algorithms to represent dynamicorganizational operating environments and competition among firms,respectively.
Analytical modeling of matrix–vector multiplication on multicore processors

The efficiency of matrix–vector multiplication is of considerable importance. No current approaches can optimize this sufficiently well under severe time constraints. All major existing methods are based on either manual‐tuning or auto‐tuning and can therefore be time‐consuming. We introduce an alternative model‐driven approach, which is used to map the implementation of matrix–vector multiplication to a target architecture and analytically obtain its parameters. The approach yields the performance that is competitive with optimized Basic Linear Algebra Subprograms (BLAS)‐like dense linear algebra libraries without the need for manual‐tuning or auto‐tuning. Our method provides competitive performance across hardware architectures and can be utilized to obtain single‐threaded and multi‐threaded implementations on multicore processors. We expect that this approach allows the community to progress from valuable engineering solutions to techniques with a broader application.
Enhancing capabilities of Texture Extraction for Color Image Retrieval

of research in recent years. Efficient image retrieval with high precision would require an approach which combines usage of both the color and texture features of the image. In this paper we propose a method for enhancing the capabilities of texture based feature extraction and further demonstrate the use of these enhanced texture features in Texture-Based Color Image Retrieval.
An Advanced Approach to Polymorphic/Metamorpic Malware Detection using Hybrid Clustering Approach

Malware Classification has been a challenging problem in the recent past and several researchers have attempted to solve this problem using various tools. It is security threat which can break machine operation while not knowing user’s data and it's tough to spot its behavior. This paper proposes a novel technique using DBSCAN (Density based Kmeans) algorithmic rule to spot the behavior of malware. After classification from DBSCAN, pattern matching is applied using the instructions pattern in the generated reports. Among of these techniques a pattern based mostly technique is well famed for the detection of malware. For the moderation and improvement of the present system the signature based mostly technique is most popular. The results are found to be quite accurate and better than the existing ones in terms of accuracy.
Composable Information Gradients in Wireless Sensor Networks

In sensor networks we aim to achieve global objectives through local decisions at each node, based only on data available in the node’s neighborhood. In this paper, we diffuse information away from source nodes holding desired data, so as to establish information potentials that allow network queries to navigate towards and reach these sources through local greedy decisions, following information gradients. We compute these information potentials by solving for a discrete approximation to a partial differential equation over appropriate network neighborhoods, through a simple local iteration that can be executed in a distributed manner and can be re-invoked to repair the information field locally when links fail, sources move, etc. The solutions to this equation are classical harmonic functions, which have a rich algebraic structure and many useful properties, including the absence of local extrema, providing a guarantee that our local greedy navigation will not get stuck. Unlike shortest path trees, which can also be used to guide queries to sources, information potentials are robust to low-level link volatility as they reflect more global properties of the underlying connectivity. By exploiting the algebraic structure of harmonic functions such potentials can be combined in interesting ways to enable far greater path diversity and thus provide better load balancing than is possible with fixed tree structures, or they can be used to answer range queries about the number of sources in a certain regions by simply traversing the boundary of the region. Potentials for multiple information types can be aggregated and compressed using a variant of the q-digest data structure. The paper provides both analytic results and detailed simulations supporting these claims.
Identification of high conservation value forest HCVF in natural production forest to support implementation of SFM certification in Indonesia using remote sensing and GIS

Indonesian forests are in critical situation. Very high rate of forest degradation resulted from unsustainable forest management, rampant illegal logging, forest area encroachment, conversion and natural disaster, i.e. fire, all together urges rapid improvement of management system of Indonesia’s forest resources. Forest certification is one tool that can support the achievement of sustainable forest management goal. Under current operation of Joint Certification Protocol between FSC and LEI in Indonesia, forest management units must be able to show the required (process and) performance indicated in LEI Criteria and Indicators as well as FSC Principles and Criteria to attain certification of their products. Nevertheless, the gap between current practices and performance required by forest certification schemes is still enormous. The management of High Conservation Value Forest is one of FSC principles for sustainable forest management. However, some difficulties were found when forest management unit tries to identify the HCVF, since the definition, terms and available guideline for HCVF identification are necessarily generic and global. Therefore, this study tries to implement a detailed method for identification of High Conservation Value Forest (HCVF) in a natural production forest using remote sensing and GIS.
Alongshore Sediment Transport Rate Distribution

The initial analysis of a new set of experimental data on alongshore sediment transport rate distribution is presented. The distributions were generally found to be bimodal but tended to become single-peaked for lower wave heights and smaller grain sizes. The ratio of suspended load to bedload was fairly constant throughout these tests, even though grain size, wave height and wave period were varied substantially.
Lecture 1 : Probabilistically Checkable Proofs and Hardness of Approximation

The notion of reductions we will use in the course will be Karp reductions, meaning that an input to the initial problem is efficiently transformed to an input to the new problem. This is as opposed to the more general notion of Cook reductions, where the initial problem is solved using access to a black-box that solves the new problem. In fact, the reductions we will show will typically (but not always) be Levin reductions, meaning that a witness/proof for the new problem can be efficiently transformed to a witness/proof for the initial problem.
Intraseasonal Precipitation Variability Over Tropical Africa

iii DEDICATION . iv ACKNOWLEDGEMENTS . v LIST OF TABLES . ixx LIST OF FIGURES . x Chapter 1: Introduction . 1 1.1. Tropical Africa . 2 1.2. Dissertation Outline . 5 2. Chapter 2: Sub-seasonal analysis of precipitation variability in the Blue Nile River basin . 7 2.