Miracle-dz/UniFAR-FTUs huggingface dataset:
Requirement Evolution Management: A Systematic Approach

Evolution of requirements is a natural phenomenon in the development life cycle of both software and hardware systems. As requirements evolve, new features are added and existing features are modified or deleted. To conform to the requirement evolution, the current working version of the implementation also has to change in most cases, to support the changed needs. In this paper, we propose an automated approach for analyzing and debugging requirement evolutions. Given two requirements (a reference one and a new, modified one) and a design implementation that conforms to the earlier requirement, our approach uses symbolic specification comparison and unfolding to extract the unsupported requirement core. In addition, to aid the programmer, our method also synthesizes a set of scenarios that may aid the developer address the changed requirements.
Flow Control for Cost-Efficient Peer-to-Peer Streaming

In this paper we address the issue of network cost efficiency for live streaming peer-to-peer systems. We formalize this as an optimization problem, which features a generic cost function. The latter is appropriate to capture not only ISP-specific link weights, but also non-linear, congestion-dependent costs. Our main contribution is the introduction of the Implicit-Primal-Dual scheme for flow control in live streaming peer-to-peer systems. It is fully distributed in that it relies only on local state variable exchanges. Moreover, we show that at a fluid scale, combined with random linear network coding, it admits the cost optimal operating point as a fixed point. We also prove asymptotic boundedness of fluid trajectories for particular cost functions. We finally show via experiments that these optimality properties are resilient to operational constraints such as finite generation size and finite field size.
VQA: vertica query analyzer

Database query monitoring tools collect performance metrics, such as memory and cpu usage, while a query is executing and make them available through log files or system tables. The metrics can be used to understand and diagnose query performance issues. However, analytic queries over big data presents new challenges for query monitoring tools. A long-running query may generate tens of thousands of values so simply reporting the metrics may overwhelm the user. Second, analytic queries may be written by database novices who have trouble interpreting the metrics. Third, analytic queries may access data or processing outside the database through user-defined functions and connectors. The impact of these on query performance must be understood. Vertica Query Analyzer (VQA) is a query monitoring tool to address these challenges. VQA is both a useful tool and a research platform for query analytics. It presents query performance metrics through a variety of views and granularities. In addition, it analyzes the metrics for typical performance problems and suggests corrective actions. We demonstrate VQA using TPC-DS queries which have a wide range of query duration and complexity.
Classifying driver workload using physiological and driving performance data: two field studies

Understanding the driver's cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algorithms on their ability to automatically identify elevated cognitive workload levels in drivers, leading towards the development of robust tools for automobile user interface evaluation. We look at using both driver performance as well as physiological data. These measures can be collected in real-time and do not interfere with the primary task of driving the vehicle. We report classification accuracies of up to 90% for detecting elevated levels of cognitive load, and show that the inclusion of physiological data leads to higher classification accuracy than vehicle sensor data evaluated alone. Finally, we show results suggesting that models can be built to classify cognitive load across individuals, instead of building individual models for each per-son. By collecting data from drivers in two large field studies on the highway (20 drivers and 99 drivers), this work extends prior work and demonstrates feasibility and potential of such measures for HCI research in vehicles.
Associations between subjective social status and DSM-IV mental disorders: results from the World Mental Health surveys.

IMPORTANCE The inverse social gradient in mental disorders is a well-established research finding with important implications for causal models and policy. This research has used traditional objective social status (OSS) measures, such as educational level, income, and occupation. Recently, subjective social status (SSS) measurement has been advocated to capture the perception of relative social status, but to our knowledge, there have been no studies of associations between SSS and mental disorders. OBJECTIVES To estimate associations of SSS with DSM-IV mental disorders in multiple countries and to investigate whether the associations persist after comprehensive adjustment of OSS. DESIGN, SETTING, AND PARTICIPANTS Face-to-face cross-sectional household surveys of community-dwelling adults in 18 countries in Asia, South Pacific, the Americas, Europe, and the Middle East (N=56,085). Subjective social status was assessed with a self-anchoring scale reflecting respondent evaluations of their place in the social hierarchies of their countries in terms of income, educational level, and occupation. Scores on the 1 to 10 SSS scale were categorized into 4 categories: low (scores 1-3), low-mid (scores 4-5), high-mid (scores 6-7), and high (scores 8-10). Objective social status was assessed with a wide range of fine-grained objective indicators of income, educational level, and occupation. MAIN OUTCOMES AND MEASURES The Composite International Diagnostic Interview assessed the 12-month prevalence of 16 DSM-IV mood, anxiety, and impulse control disorders. RESULTS The weighted mean survey response rate was 75.2% (range, 55.1%-97.2%). Graded inverse associations were found between SSS and all 16 mental disorders. Gross odds ratios (lowest vs highest SSS categories) in the range of 1.8 to 9.0 were attenuated but remained significant for all 16 disorders (odds ratio, 1.4-4.9) after adjusting for OSS indicators. This pattern of inverse association between SSS and mental disorders was significant in 14 of 18 individual countries, and in low-, middle-, and high-income country groups but was significantly stronger in high- vs lower-income countries. CONCLUSIONS AND RELEVANCE Significant inverse associations between SSS and numerous DSM-IV mental disorders exist across a wide range of countries even after comprehensive adjustment for OSS. Although it is unclear whether these associations are the result of social selection, social causation, or both, these results document clearly that research relying exclusively on standard OSS measures underestimates the steepness of the social gradient in mental disorders.
Microtiter Hemagglutination-Inhibition Assay for Staphylococcal Enterotoxin B

The rapid microtiter technique was investigated as a means of facilitating the de- tection of staphylococcal enterotoxin B. With this technique, many samples were assayed simultaneously, and readable results were obtained in 3 hr. Other advan- tages of this method, in addition to speed, were the small quantity of reactants used, ease of reading, and reproducibility. 1 a modification quantitative
Is Osteocalcin Implicated in the Regulation Of Energy Metabolism in Active Rheumatoid Arthritis

The purpose of this study was to determine the relationship of osteocalcin with insulin resistance, insulin-like growth factor-1 (IGF-1) and inflammatory markers in women with RA. The study population comprised of 35 postmenopausal women with RA. The significant negative correlations were noted between the osteocalcin and resistin levels (P <0.05), glucose and resistin levels (P ≤ 0.01), glucose and interleukin-6 levels (P ≤ 0.01), interleukin-6 and insulin-like growth factor binding protein-3 (IGFBP-3) levels (P < 0.05). The positive correlations were found between insulin level and disease duration (P < 0.05), index of insulin resistance (HOMA-IR) level and disease duration (P < 0.05), IGF-1 and IGFBP-3 levels (P ≤ 0.01), the IGF-1/IGFBP-3 ratio and erythrocyte sedimentation rate (P ≤ 0.01), and the IGF-1/IGFBP-3 ratio and C-reactive protein levels (P < 0.05). The present study suggests that inflammatory factors may modify in distinct ways the relationship between osteocalcin and carbohydrate metabolism in patients with RA. However, limitations of this study are: lack of control group, small sample size and that we did not measure uncarboxylated OC.
Cooperative Dynamics of Motor Proteins on Elastic Cytoskeletal Filaments

When many motor proteins work together, they exhibit rather fascinating dynamics. In this work we theoretically study the dynamics of a-polar actin-myosin II systems. Such a-polar systems will show bidirectional dynamics that is composed of directional section. Those directional section have a time length (known as the characteristic reversal time, τrev) that, before our work, was believed to have an exponential dependent on the size of the system (the number of motors) [1]. However, in a recent experimental work [2], it was found that there is no such exponential dependence. In our work, we suggest that the elasticity of the actin filaments is a key concept that is needed to be taken into account in such systems. We present an elastic ratchet model to describe our system and study it both analytically and numerically [3]. By introducing this elasticity, the exponential dependent of the characteristic reversal time is eliminated [3]. Further on, we predict that those elasticity effects will cause a broken symmetry dynamics in certain spatially symmetric configurations of actin-myosin II systems [4]. That is, we show how perfectly a-polar actin-myosin II systems, with specific internal configurations show a bidirectional dynamics with a non vanishing drift velocity.
An SLS5 Longitude System Based on the Rotational Modulation of Saturn Radio Emissions

Despite the axisymmetry of Saturn's internal field, Saturn radio emissions like Saturn kilometric radiation (SKR) are modulated due to planetary rotation. With the completion of Cassini mission in September 2017, we now have around 14 years of observation of Saturn radio emissions, roughly from southern solstice to northern solstice. In this study, we extend the SLS4 longitude system to the end of the Cassini mission using a phase tracing method. The new Saturn longitude system (SLS5) organizes the observed SKR maxima around 0° subsolar longitude in both northern and southern hemispheres and can be used to organize other phenomena observed in Saturn's magnetosphere, for example, hot plasma injection events. SKR is modulated like a clock when the main source on the morning side is visible. To convert the observed phase to the clock phase, the phase of the morning side source, we also define a second longitude system SLS5*, which takes the spacecraft position into account based on a simple visibility model.
DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells.

Identification of active gene regulatory elements is a key to understanding transcriptional control governing biological processes like cell-type specificity, differentiation, development, proliferation, and response to the environment. Mapping DNase I hypersensitive (HS) sites has historically been a valuable tool for identifying all different types of regulatory elements, including promoters, enhancers, silencers, insulators and locus control regions. This method utilizes DNase I to selectively digest nucleosome-depleted DNA (presumably by transcription factors), whereas DNA regions tightly wrapped in nucleosome and higher order structures are more resistant. The traditional low-throughput method for identifying DNase I HS sites uses Southern blots. Here, we describe the complete and improved protocol for DNase-seq, a high-throughput method that identifies DNase I HS sites across the whole genome by capturing DNase-digested fragments and sequencing them by high-throughput next generation sequencing. In a single experiment, DNase-seq can identify most active regulatory regions from potentially any cell type from any species with a sequenced genome.
Variable and thread bounding for systematic testing of multithreaded programs

Previous approaches to systematic state-space exploration for testing multi-threaded programs have proposed context-bounding and depth-bounding to be effective ranking algorithms for testing multithreaded programs. This paper proposes two new metrics to rank thread schedules for systematic state-space exploration. Our metrics are based on characterization of a concurrency bug using v (the minimum number of distinct variables that need to be involved for the bug to manifest) and t (the minimum number of distinct threads among which scheduling constraints are required to manifest the bug). Our algorithm is based on the hypothesis that in practice, most concurrency bugs have low v (typically 1-2) and low t (typically 2-4) characteristics. We iteratively explore the search space of schedules in increasing orders of v and t. We show qualitatively and empirically that our algorithm finds common bugs in fewer number of execution runs, compared with previous approaches. We also show that using v and t improves the lower bounds on the probability of finding bugs through randomized algorithms. Systematic exploration of schedules requires instrumenting each variable access made by a program, which can be very expensive and severely limits the applicability of this approach. Previous work has avoided this problem by interposing only on synchronization operations (and ignoring other variable accesses). We demonstrate that by using variable bounding (v) and a static imprecise alias analysis, we can interpose on all variable accesses (and not just synchronization operations) at 10-100x less overhead than previous approaches.
Discovering Near Symmetry in Graphs

Symmetry is a widespread phenomenon that can offer opportunities for powerful exploitation in areas as diverse as molecular chemistry, pure mathematics, circuit design, biology and architecture. Graphs are an abstract way to represent relational structures. The search for symmetry in many contexts can thus be reduced to the attempt to find graph automorphisms. Brendan McKay's NAUTY system (McKay 1990) is an example of one of the highly successful products of research into this task. Erdos and Renyi showed that almost all large graphs are asymmetric, but it is readily observed that many graphs representing structures of real Interest contain symmetry. Even more graphs are nearly symmmetric, in the sense that to each graph there is a closely Similar graph that is symmetric. In this paper we explore the problem of finding near symmetries in graphs and describe the techniques we are developing for performing this task.
Massachusetts Health Care Reform: Effects on Employer-Sponsored Insurance Coverage and Health

This study examines the effect of the Chapter 58 health care reform in Massachusetts four years after implementation on rates of coverage through employer-sponsored plans (ESI) and self-reported health status. It documents whether the relative increase in ESI coverage rates found in Massachusetts one and two years after the reform have persisted. A difference-indifference model is used to examine effects on coverage rates and health status after the reform. Non-elderly adults living in Massachusetts make up the treatment group and the control group is comprised of individuals living in other Northeastern states. The study uses data from the Current Population Survey from the pre-reform years 2001 to 2006 and post-reform years 2007 to 2010. Results show that for the non-elderly adult population coverage through ESI plans increased 3.4 percentage points in the four years since the reform, slightly more than indicated in earlier studies. Gains were especially large among the young adult, near elderly, less educated, and lower income populations. The Massachusetts population also showed a significant improvement in self-reported health status.
An orthographic descriptor for 3D object learning and recognition

Object representation is one of the most challenging tasks in robotics because it must provide reliable information in real-time to enable the robot to physically interact with the objects in its environment. To ensure reliability, a global object descriptor must be computed based on a unique and repeatable object reference frame. Moreover, the descriptor should contain enough information enabling to recognize the same or similar objects seen from different perspectives. This paper presents a new object descriptor named Global Orthographic Object Descriptor (GOOD) designed to be robust, descriptive and efficient to compute and use. The performance of the proposed object descriptor is compared with the main state-of-the-art descriptors. Experimental results show that the overall classification performance obtained with GOOD is comparable to the best performances obtained with the state-of-the-art descriptors. Concerning memory and computation time, GOOD clearly outperforms the other descriptors. Therefore, GOOD is especially suited for real-time applications.
On L_1-Norm Multi-class Support Vector Machines

Binary support vector machines (SVM) have proven effective in classification. However, problems remain with respect to feature selection in multi-class classification. This article proposes a novel multi-class SVM, which performs classification and feature selection simultaneously via L1-norm penalized sparse representations. The proposed methodology, together with our developed regularization solution path, permits feature selection within the framework of classification. The operational characteristics of the proposed methodology is examined via both simulated and benchmark examples, and is compared to some competitors in terms of the accuracy of prediction and feature selection. The numerical results suggest that the proposed methodology is highly competitive
A Survey on Web Application Vulnerabilities (SQLIA, XSS) Exploitation and Security Engine for SQL Injection

Today almost all organizations have improved their performance through allowing more information exchange within their organization as well as between their distributers, suppliers, and customers using web support. Databases are central to the modern websites as they provide necessary data as well as stores critical information such as user credentials, financial and payment information, company statistics etc. These websites have been continuously targeted by highly motivated malicious users to acquire monetary gain. Structured Query Language (SQL) injection and Cross Site Scripting Attack (XSS) is perhaps one of the most common application layer attack technique used by attacker to deface the website, manipulate or delete the content through inputting unwanted command strings. Structured Query Language Injection Attacks (SQLIA) is ranked 1st in the Open Web Application Security Project (OWASP) [1] top 10 vulnerability list and has resulted in massive attacks on a number of websites in the past few years. In this paper, we present a detailed review on various types of Structured Query Language Injection attacks, Cross Site Scripting Attack, vulnerabilities, and prevention techniques. Besides presenting our findings from the survey, we also propose future expectations and possible development of countermeasures against Structured Query Language Injection attacks.
A Rim Seal Ingress Model Based on Turbulent Transport

Copyright © 2017 ASME. Recent experimental work from the present authors demonstrated that interactions between the mainstream and cavity/rim seal flows lead to ingestion mechanisms with a range of length scales. In addition to the (well known) effect of the vane and blade pressure fields, it was demonstrated that the shear layer instabilities between the mainstream and rim seal flows can affect ingress. Building upon these observations and the understanding in the literature, this paper presents a model which relates rotor-stator cavity seal effectiveness to purge flow rate based on turbulent transport. The main assumption is that all length scales of ingress lead to an effective eddy diffusivity. This eddy diffusivity drives ingress across the seal concentration gradient. Following Prandtl's mixing length hypothesis for eddy viscosity, the model uses an empirical constant representing an equivalent mixing length. This assumption is shown to be sufficient across a limited range of dimensionless flow rates. An extension of the model is presented to account for the reduction in turbulent mixing in the rim seal recirculation region as it becomes washed out with increasing purge flow. The rate at which the effect of the rim seal recirculation region gets washed out is modelled with a purge-To-mainstream blowing ratio term and the volume fraction of the seal occupied by the rim seal recirculation. The differences in volume fraction and blowing ratio between the different experiments in literature are defined by the geometry and flow condition only. By fitting, it is shown that the model is sufficient to capture a wide variety of experimental data in the literature and that of the present authors. The results and the model derivation provide an encouraging first step and a framework towards a model that is sensitized to both geometry and flow conditions.
Reconstruction of S-waves from low-cost randomized and simultaneous acquisition by joint sparse inversion

Spatial Nyquist sampling rate, which is proportional to the apparent subsurface velocity, is the key deciding cost factor for conventional seismic acquisition. Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier, which increases the acquisition costs. This is one of the reasons why shear waves are usually not considered in practice. To avoid having the Nyquist criterion as the deciding cost factor and to utilize multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. Each component can then be interpolated separately, followed by elastic decomposition to recover upand down-going S-waves. Instead, we jointly interpolate and decompose the recorded multicomponent data by solving one sparsity promoting optimization problem. This way we ensure that the relative amplitudes of the multicomponent data is preserved. We compare two sparsifying transforms: the curvelet transform and the frequency-wavenumber transform. Another key cost deciding factor for seismic acquisition is the efficiency of acquiring data. This calls for simultaneous acquisition, which requires a source separation step. Similarly, instead of taking a two-step approach, we perform a sparsity-promoting joint source separation decomposition. Results on economically and efficiently acquired synthetic data of both joint methods show their ability of reconstructing accurate upand down-going S-waves.