Miracle-dz/UniFAR-FTUs huggingface dataset:
Swot Analysis Of Software Quality Metrics For Global Software Development: A Systematic Literature Review Protocol

CONTEXT - Global Software Development (GSD) is a modern software engineering paradigm adopted by many client organisations in developed countries to get high quality product at low cost in low wage countries. Production of high quality software is considered as one of the key factor in the rapid growth of GSD. However GSD projects have put new challenges to practitioners and researchers. In order to address these challenges Software Quality Metrics (SQMs) are frequently used in organisations to fabricate high quality products. OBJECTIVE - The objective of this SLR protocol is to identify and assess strengths and weaknesses of the existing SQMs used in GSD to assist vendor organisations in choosing appropriate SQMs for measuring software quality. METHOD - Systematic Literature Review (SLR) will be used for the identification of the existing SQMs in GSD. SLR is based on a structured protocol, and is therefore, different from ordinary review. EXPECTED OUTCOME - We have developed the SLR protocol and are currently in process of its implementation. The expected outcomes of this review will be the identification of different SQMs for GSD along with their SWOT analysis to assist vendor organisations in choosing appropriate SQMs at the right time to produce a high quality product.
Rule-Based Run-Time Information Flow Control in Service Cloud

Service cloud provides added value to customers by allowing them to compose services from multiple providers. Most existing web service security models focus on the protection of individual web services. When multiple services from different domains are composed together, it is critical to ensure the proper information flow on the chain of services. In a service chain, each service needs to determine whether the sensitive information can be directly or indirectly disseminated to the subsequent services. Also, each service in the chain needs to decide whether to accept the data passed to it directly or indirectly from prior services. Moreover, the input data that service si receives from si-1, si. InF, may cause certain side effects inside si, such as updating si's backend database using data computed from si. InF. Service si may wish to allow such side effects in one situation while reject some side effects in another situation. All these decisions should be made based on the service's information flow control policies. To achieve fine-grained information flow control, it is also necessary to analyze the flow and processing of the data and derive the dependencies between the data dynamically generated or used in a service chain. In this paper, we develop a run-time information flow control model for service cloud. First, we develop a run-time dependency analysis mechanism which enables each service in the service chain to determine the correlation between the locally accessed data and the data dynamically generated by the services in the service chain. Then, we develop a model to enable each service in a service chain to specify policies on how its sensitive information can be released to its subsequent services and what types of input data from prior services can be accepted and how they can flow within the services. Finally, we design a run-time protocol to enforce these policies in a service chain.
Evolutionary History, Immigration History, and the Extent of Diversification in Community Assembly

During community assembly, species may accumulate not only by immigration, but also by in situ diversification. Diversification has intrigued biologists because its extent varies even among closely related lineages under similar ecological conditions. Recent research has suggested that some of this puzzling variation may be caused by stochastic differences in the history of immigration (relative timing and order of immigration by founding populations), indicating that immigration and diversification may affect community assembly interactively. However, the conditions under which immigration history affects diversification remain unclear. Here we propose the hypothesis that whether or not immigration history influences the extent of diversification depends on the founding populations’ prior evolutionary history, using evidence from a bacterial experiment. To create genotypes with different evolutionary histories, replicate populations of Pseudomonas fluorescens were allowed to adapt to a novel environment for a short or long period of time (approximately 10 or 100 bacterial generations) with or without exploiters (viral parasites). Each evolved genotype was then introduced to a new habitat either before or after a standard competitor genotype. Most genotypes diversified to a greater extent when introduced before, rather than after, the competitor. However, introduction order did not affect the extent of diversification when the evolved genotype had previously adapted to the environment for a long period of time without exploiters. Diversification of these populations was low regardless of introduction order. These results suggest that the importance of immigration history in diversification can be predicted by the immigrants’ evolutionary past. The hypothesis proposed here may be generally applicable in both micro- and macro-organisms.
Optimized Brain Extraction for Pathological Brains (optiBET)

The study of structural and functional magnetic resonance imaging data has greatly benefitted from the development of sophisticated and efficient algorithms aimed at automating and optimizing the analysis of brain data. We address, in the context of the segmentation of brain from non-brain tissue (i.e., brain extraction, also known as skull-stripping), the tension between the increased theoretical and clinical interest in patient data, and the difficulty of conventional algorithms to function optimally in the presence of gross brain pathology. Indeed, because of the reliance of many algorithms on priors derived from healthy volunteers, images with gross pathology can severely affect their ability to correctly trace the boundaries between brain and non-brain tissue, potentially biasing subsequent analysis. We describe and make available an optimized brain extraction script for the pathological brain (optiBET) robust to the presence of pathology. Rather than attempting to trace the boundary between tissues, optiBET performs brain extraction by (i) calculating an initial approximate brain extraction; (ii) employing linear and non-linear registration to project the approximate extraction into the MNI template space; (iii) back-projecting a standard brain-only mask from template space to the subject’s original space; and (iv) employing the back-projected brain-only mask to mask-out non-brain tissue. The script results in up to 94% improvement of the quality of extractions over those obtained with conventional software across a large set of severely pathological brains. Since optiBET makes use of freely available algorithms included in FSL, it should be readily employable by anyone having access to such tools.
Bandwidth Selection for Fixed-Kernel Analysis of Animal Utilization Distributions

Abstract In studies of animal space use, researchers often use kernel-based techniques for estimating the size of an animal's home range and its utilization distribution from radiotracking data. However, the kernel estimator is highly sensitive to the bandwidth value used. Previous ecological studies recommended least-squares cross-validation (LSCV) as the default bandwidth selection method, but some statisticians consider this technique inferior to newer methods. We used simulations to compare the performance of the scaling LSCV and reference approaches to plug-in and solve-the-equation (STE) bandwidth methods. We generated samples of 20, 50, and 150 points from mixtures of 2, 4, and 16 bivariate normal distributions. We selected the ranges of potential variances for these distributions to create 4 distribution types with varied levels of clumping to simulate the diversity of location patterns expected from radiotracking data. For most distribution types, plug-in and STE methods performed as well or better than LSCV in % absolute error of home-range size estimates and overlap of estimated and true utilization distributions. Although the relative differences usually were small, the plug-in and STE approaches provide good alternatives to LSCV. However, LSCV performed better with distribution types composed entirely of tight clumps of points. The reference bandwidth performed poorly for most distributions. Surprisingly, it often had the lowest absolute error at outer contours for distributions consisting of a single very tight cluster surrounded by more dispersed points. Although our results demonstrate the utility of plug-in and STE approaches, no method was best across all distributions. Rather, choice of a bandwidth selection method may vary depending on the study goals, sample size, and patterns of space use by the study species. In general, we recommend plug-in and STE approaches for estimating relatively smooth outer contours. The LSCV approach is better at identifying tight clumps, including areas of peak use, although risk of LSCV failure also increases when a distribution has a very tight cluster of points. When planning to use kernel methods, researchers should consider these factors to make preliminary decisions about the bandwidth method expected to be most appropriate in their study.
A methodology for evaluating JavaScript execution behavior in interactive web applications

JavaScript has gone from being a mechanism for providing dynamic web pages to an important component of many web applications. Currently one of the most popular type of web applications is so-called social networks, e.g., Facebook, Twitter, and MySpace. However, the workload and execution behavior of JavaScript in this context have not been fully explored or understood. In this paper we present a methodology for characterizing the JavaScript execution behavior in interactive web applications using deterministic execution of use cases. Then, we apply this methodology to evaluate a set of social network applications and compare their behavior to a set of established JavaScript benchmarks. Our results confirm previous studies that the execution behavior of social networks differ from established benchmarks. In addition, we identify one novel difference not published before, i.e., the use of anonymous functions in web applications.
Revenue‐Sharing vs. Wholesale‐Price Contracts in Assembly Systems with Random Demand

Assembly and kitting operations, as well as jointly sold products, are rather basic yet intriguing A decentralized supply chains, where achieving coordination through appropriate incentives is very important, especially when demand is uncertain. We investigate two very distinct types of arrangements between an assembler/retailer and its suppliers. One scheme is a vendor‐managed inventory with revenue sharing, and the other a wholesale‐price driven contract. In the VMI case, each supplier faces strategic uncertainty as to the amounts of components, which need to be mated with its own, that other suppliers will deliver. We explore the resulting components' delivery quantities equilibrium in this decentralized supply chain and its implications for participants' and system's expected profits. We derive the revenue shares the assembler should select in order to maximize its own profits. We then explore a revenue‐plus‐surplus‐subsidy incentive scheme, where, in addition to a share of revenue, the assembler also provides a subsidy to component suppliers for their unsold components. We show that, by using this two‐parameter contract, the assembler can achieve channel coordination and increase the profits of all parties involved. We then explore a wholesale‐price‐driven scheme, both as a single lever and in combination with buybacks. The channel performance of a wholesale‐price‐only scheme is shown to degrade with the number of suppliers, which is not the case with a revenue‐share‐only contract.
Application of multirate digital filter banks to wideband all-digital phase locked loops design

A new class of architecture for all-digital phase locked loops (DPLLS) is presented. This architecture, referred to as parallel DPLL (PDPLL), is based on employing multirate digital filter banks (DFBs) to track signals with a lower processing rate than the Nyquist rate, without reducing the input (Nyquist) bandwidth. The PDPLL basically trades complexity versus hardware processing speed by introducing parallel processing in the receiver. A reduced complexity DFB that is ideal for implementation of the PDPLL is introduced. It is demonstrated that the PDPLL performance is identical to that of a DPLL for both steady state and transient behavior. Various Doppler characteristics are used to compare the performance of the DPLL with the PDPLL. <<ETX>>
Multiresolution Interblock Interpolation in Direct Volume Rendering

We present a direct interblock interpolation technique that enables direct volume rendering of blocked, multiresolution volumes. The proposed method smoothly interpolates between blocks of arbitrary block-wise level-of-detail (LOD) without sample replication or padding. This permits extreme changes in resolution across block boundaries and removes the interblock dependency for the LOD creation process. In addition the full data reduction from the LOD selection can be maintained throughout the rendering pipeline. Our rendering pipeline employs a flat block subdivision followed by a transfer function based adaptive LOD scheme. We demonstrate the effectiveness of our method by rendering volumes of the order of gigabytes using consumer graphics cards on desktop PC systems.
Antisense Oligonucleotides Capable of Promoting Specific Target mRNA Reduction via Competing RNase H1-Dependent and Independent Mechanisms

Antisense oligonucleotides (ASOs) are most commonly designed to reduce targeted RNA via RNase H1-dependent degradation. In this paper we demonstrate that cellular proteins can compete for sites targeted by RNase H1-dependent ASOs. We further show that some ASOs designed to mediate RNase H1 cleavage can, in certain instances, promote target reduction both by RNase H1-mediated cleavage and by steric inhibition of binding of splicing factors at a site required for efficient processing of the pre-mRNA. In the latter case, RNase H cleavage was prevented by binding of a second protein, HSPA8, to the ASO/pre-mRNA heteroduplex. In addition, using a precisely controlled minigene system, we directly demonstrated that activity of ASOs targeting sites in introns is strongly influenced by splicing efficiency.
On-chip compression for HDR image sensors

An image compressing technique for High Dynamic Range (HDR) image sensors is introduced. Compression is performed in two steps: Pixel value coding optimization followed by DCT-based (Discrete Cosine Transform) compression. A floating point coding technique is first used with a common exponent shared between pixels of the same block, and then a DCT is applied to each group of pixels. This new concept, while maintaining low complexity architecture, shows a compression ratio of 75 % and retains a good image quality with a PSNR of about 40 dB.
Draft Genome Sequence of Gulbenkiania mobilis Strain MB1, a Sulfur-Metabolizing Thermophile Isolated from a Hot Spring in Central India

ABSTRACT This paper reports the draft genome sequence of the proteobacterium Gulbenkiania mobilis strain MB1, a sulfur-metabolizing thermophile isolated from a hot spring located in Pachmarhi, India. This study reports the first draft genome sequence of any species from the genus Gulbenkiania.
Modeling prosopagnosia using dynamic artificial neural networks

Prosopagnosia is a brain disorder causing the inability to recognize faces. Previous studies have shown that the lesions producing the disorder can occur in diverse areas of the brain. However, the most common region is the “fusiform face area” (FFA). In order to model the basic properties of prosopagnosia two networks have been used concurrently: the Feature Extracting Bidirectional Associative Memory (FEBAM-SOM) and the Bidirectional Associative Memory (BAM). The FEBAM-SOM creates a 2D topological map from correlated inputs through the categorization of various exemplars (faces and various objects). This model has the advantage of using a sparse representation which encompass both localist and distributed encoding. This process simulates the FFA in the brain by exhibiting attractor-like behavior for the categorization of all faces. Once the faces have been learned, the BAM model associates specific faces (and objects) to their corresponding semantic labels. Simulations were performed to study the recall performance in function of the size of the lesions. Results show that the recall performance of the names associated with faces decrease with the size of lesion without affecting the performance of the objects.
Theoretischer Beitrag: Soziale Prozesse beim Lernen mit digital präsentierten Lernmaterialien

Die Forschung zu digitalen Lernmaterialien wurde lange Zeit durch rein kognitiv ausgerichtete Theorien dominiert. Dieser Beitrag widmet sich der Fragestellung, inwieweit der individuelle Umgang mit digital prasentierten Lernmaterialien auch sozialen Prozessen unterliegt. Dafur werden Theorien herangezogen, die soziale Einflusse auf Lernprozesse prognostizieren konnen. In Zusammenhang mit diesen Theorien und der Kognitiv-Affektiven Theorie des Lernens mit Medien wird eine um soziale Prozesse erweiterte Theorie vorgeschlagen. Zur Untermauerung dieser Theorie werden konkrete soziale Effekte beschrieben, die beim Einsatz von Text-Bild-Kombinationen (statischen Medien) auftreten. Diese Effekte beziehen sich auf Personalisierungen in Texten (wie z. B. eine personliche Ansprache von Lernenden), kulturelle Hinweiszeichen (wie z. B. die Nutzung von Hoflichkeitsformen in Instruktionen) oder emotionale Einflusse (wie z. B. das Auslosen sozialer Reaktionen durch emotionale Bilder). Daruber hinaus werden Effekte beleuchtet, die vornehmlich in dynamischen Medien (wie z. B. Videos oder Animationen) auftreten. Als Beispiele konnen solche Effekte aus auditiven Informationen (wie z. B. Akzente bei Tonaufnahmen) oder bildhaften Informationen (wie z. B. die Perspektive von Lehrenden in Lernvideos) entstehen. Zusammenfassend soll geklart werden, inwieweit bisherige empirische Befunde die Kognitiv-Affektiv-Soziale Theorie des Lernens mit Medien abdecken und welche Konsequenzen aus diesen Befunden fur das Erstellen digital prasentierter Lernmaterialien entstehen.
WMMSE-based multiuser MIMO beamforming: A practice-oriented design and LTE system performance evaluation

This paper deals with the multiuser MIMO downlink beamforming scenario. In this scenario, a popular approach for designing the beamformer is the weighted minimum mean square error (WMMSE) approach for sum rate maximization. This work considers the WMMSE method from an implementation perspective, where we take into account practical factors - such as linear receivers (rather than successive interference cancellation receivers, which are implicitly assumed in some existing works but are more complex to implement) and imperfect channel knowledge at the transmitter - for deployment in systems such as long term evolution (LTE). A modified WMMSE algorithm that incorporates such factors is proposed. We evaluate the performance of the proposed WMMSE algorithm using a time division duplexing (TDD) LTE simulation platform, and show that our practice-oriented design can improve the system throughput by 3 ~ 30% in comparison with the conventional WMMSE algorithm.
Enforcement of Automaton-based Information Release Policies

Many organizations specify information release policies to describe the terms under which sensitive information may be released to other organizations. This paper presents a new approach for ensuring that security-critical software correctly enforces its information release policy. Our approach has two parts. First, an information release policy is specified as a security automaton written in a new language called AIR. Second, we enforce an AIR policy by translating it into an API for programs written in λAIR, a core formalism for a functional programming language. λAIR uses a novel combination of dependent, affine, and singleton types to ensure that the API is used correctly. As a consequence we can certify that programs written in λAIR meet the requirements of the original AIR policy specification.
Multiclass Clustering using a Semidefinite Relaxation

We propose a semidefinite relaxation for graph clustering known as Max-cut clustering. The clustering problem is formulated in terms of a discrete optimization problem and then relaxed to a SDP. The SDP is solved using a low-rank factorization trick that reduces the number of variables, and then using a simple projected gradient method. This is joint work with Nathan Srebro at the Toyota Technology Institute-Chicago and part of research was performed at the Toyota Technology Institute-Chicago.
Generating dark matter halo merger trees

We present a new Monte Carlo algorithm to generate merger trees describing the formation history of dark matter haloes. The algorithm is a modification of the algorithm of Cole et al. used in the galform semi-analytic galaxy formation model. As such, it is based on the Extended Press–Schechter theory and so should be applicable to hierarchical models with a wide range of power spectra and cosmological models. It is tuned to be in accurate agreement with the conditional mass functions found in the analysis of merger trees extracted from the Λ cold dark matter Millennium N-body simulation. We present a comparison of its predictions not only with these conditional mass functions, but also with additional statistics of the Millennium Simulation halo merger histories. In all cases, we find it to be in good agreement with the Millennium Simulation and thus it should prove to be a very useful tool for semi-analytic models of galaxy formation and for modelling hierarchical structure formation in general. We have made our merger tree generation code and code to navigate the trees available at http://star-www.dur. ac.uk/~cole/merger_trees.
Engineering clinically relevant volumes of vascularized bone

Vascularization remains one of the most important challenges that must be overcome for tissue engineering to be consistently implemented for reconstruction of large volume bone defects. An extensive vascular network is needed for transport of nutrients, waste and progenitor cells required for remodelling and repair. A variety of tissue engineering strategies have been investigated in an attempt to vascularize tissues, including those applying cells, soluble factor delivery strategies, novel design and optimization of bio‐active materials, vascular assembly pre‐implantation and surgical techniques. However, many of these strategies face substantial barriers that must be overcome prior to their ultimate translation into clinical application. In this review recent progress in engineering vascularized bone will be presented with an emphasis on clinical feasibility.
Self-Similar Solutions for ADAF with Toroidal Magnetic Fields

We examined the effect of toroidal magnetic fields on a viscous gaseous disk around a central object under an advection-dominated stage. We found self-similar solutions for the radial infall velocity, rotation velocity, and sound speed, with an additional parameter, β [= c 2 /(2 c 2