Miracle-dz/UniFAR-FTUs huggingface dataset:
Guaranteeing Delay of Live Virtual Machine Migration by Determining and Provisioning Appropriate Bandwidth

The proliferation of cloud services makes virtualization technology more important. One important feature of virtualization is live Virtual Machine (VM) migration. Two main metrics of evaluating a live VM migration mechanism are total migration time and downtime. Most existing literature on live VM migration focus on designing migration mechanisms to shorten the two metrics or making a tradeoff between them. Few of them can be applied to applications with delay requirements, such as a VM backup process that needs to be done in a specific time. This will negatively impact the user experiences and reduce the profit of cloud service providers. Besides, the frequently varied bandwidth required by the widely used pre-copy mechanism is difficult to be provided by current network technologies. In this work, we theoretically analyze how much bandwidth is required to guarantee the total migration time and downtime of a live VM migration, and then propose a novel transport control mechanism to guarantee the computed bandwidth. The experimental results demonstrate that the bandwidth obtained from the proposed reciprocal-based model guarantees the expected total migration time and downtime, and the proposed transport control mechanism ensures that the live VM migration flow obtains the expected bandwidth even if there are background flows.
Nahe biti: The philosophy and process of grassroots reconciliation (and justice) in East Timor

UNTAET and the East Timorese have pursued formal reconciliation processes as part of the nation-building effort in newly independent East Timor. These formal reconciliation processes aim for closure and operate in the realm of national politics. In the meantime local communities have been dealing with the issues of reintegrating refugees and rebuilding social relationships through an inventive adaptation of traditional practices of reconciliation. In contrast to the formal elite-level strategies, the grassroots strategies are more concerned with process than formal outcomes.
Channel Compensation for Speaker Recognition using MAP Adapted PLDA and Denoising DNNs

Abstract : AbstractOver several decades, speaker recognition performance hassteadily improved for applications using telephone speech. Abig part of this improvement has been the availability of largequantities of speaker-labeled data from telephone recordings.For new data applications, such as audio from room microphones,we would like to effectively use existing telephone datato build systems with high accuracy while maintaining goodperformance on existing telephone tasks. In this paper we compareand combine approaches to compensate models parametersand features for this purpose. For model adaptation weexplore MAP adaptation of hyper-parameters and for featurecompensation we examine the use of denoising DNNs. On amulti-room, multi-microphone speaker recognition experimentwe show a reduction of 61% in EER with a combination of theseapproaches while slightly improving performance on telephonedata.
WISE:: a wizard interface supporting enhanced usability

The current state of software which targets older adults' ability to use computers focuses on physical issues while largely ignoring the cognitive issues. As a larger percentage of Americans are considered "old" (60+), the lack of a system tailored to the needs of this age demographic has resulted in a part of the population that is disconnected from the rest of the world. This paper describes WISE, an alternative OS and application UI that specifically targets the cognitive deficits of older adults.
A known plaintext attack on the ISAAC keystream generator

Stream ciphers are often used in applications where high speed and low delay are a requirement. The ISAAC keystream generator is a fast software-oriented encryption algorithm. In this papers the security of the ISAAC keystream generator is investigated. Cryptanalytic algorithm is developed for a known plaintext attack where only a small segment of plaintext is assumed to be known.
An Extremal Optimization approach to parallel resonance constrained capacitor placement problem

Installation of capacitors in distribution networks is one of the most used procedure to compensate reactive power generated by loads and, consequently, to reduce technical losses. So, the problem consists in identifying the optimal placement and sizing of capacitors. This problem is known in the literature as optimal capacitor placement problem. Neverthless, depending on the location and size of the capacitor, it may become a harmonic source, allowing capacitor to enter into resonance with the distribution network, causing several undesired side effects. In this work we propose a parsimonious method to deal with the capacitor placement problem that incorporates resonance constraints, ensuring that every allocated capacitor will not act as a harmonic source. This proposed algorithm is based upon a physical inspired metaheuristic known as Extremal Optimization. The results achieved showed that this proposal has reached significant gains when compared with other proposals that attempt repair, in a post-optimization stage, already obtained solutions which violate resonance constraints.
Using Domain Models in Extensible Schema-based Software Synthesis

The software used in NASA missions is growing more and more complex, but at the same time it needs to satisfy increasingly stringent affordability and reliability requirements. Software synthesis provides a way of achieving these goals by generating code from highlevel specifications. Two applications developed within the ASE group, AutoBayes [7] and AutoFilter [15], use schema-based software synthesis to generate code for the data analysis and state estimation domains, respectively. The goal of our project for the summer internship was to research an extensible framework that would allow the use of software synthesis technology to other application domains within NASA. Schema-based software synthesis uses generic code templates called schemas to represent general knowledge about software generation in a reusable format. A synthesis system takes as an input a problem specification, and applies schemas recursively in an exhaustive fashion until a complete implementation of the problem in a chosen implementation language is created. One of the main advantages of using schema-based synthesis is the ability to easily explore the design space. If there are different solutions to the same problem, different schemas can be created to implement each solution. In this way, the result of the synthesis process will be a tree of equivalent implementations, and the user can choose the one that satisfies the required performance metrics. The synthesis system is divided into two distinct parts: the frontend and the backend (Figure 1). The frontend is responsible for applying the schemas necessary to generate an implementation that satisfies the problem specification. This implementation is written using an internal intermediate language. The backend translates the solution from the intermediate language in a specific programming language and for a specific architecture. The frontend and the backend are similar in the sense that they both use schemas to transform one representation of the generated program into other type of representation, with more information added. However, one thing that makes the frontend different is the fact that the schemas involved in the frontend are application domain oriented: an important part of the information of how to solve the problems and which algorithms to apply is an integral part of the application domain. The work presented in this report is concerned with how to achieve an extensible synthesis system to new application domains. Since the frontend is the part where the domain information is used, the work is scoped to the frontend part of the synthesis system. The problem of how to make the synthesis system extensible to support new target implementation platforms in the backend was studied by another intern in our team, Tomas Bures [2]. In order to experiment with different modeling aproaches, we have designed AutoLinear, a synthesis application in the domain of matrix calculations. In particular, AutoLinear generates code that solves systems of linear equations, commonly described in mathematical notation as Ax=b.
The caries experience and behavior of dental patients with autism spectrum disorder.

BACKGROUND Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder. The authors conducted a study to evaluate the demographics, caries experience and behavior of patients with ASD and compare these characteristics with those of patients without ASD (unaffected patients). METHODS The authors reviewed patients' charts and identified a group of 395 patients with ASD and a group of 386 unaffected patients. They obtained the following patient data for analysis: primary diagnosis, age, sex, residence (home versus institution or group home), presence of seizure disorder, additional diagnosis (mental retardation, cerebral palsy, self-injurious behavior or pica), medications, caries prevalence, caries severity and behavior. RESULTS The ASD group had a male:female ratio of 4:1, and patients had a diagnosis of autism, pervasive developmental disorder-not otherwise specified or Asperger syndrome. Sex distribution was equal in the unaffected group, which was younger and had a higher decayed, missing and filled teeth (DMFT) score than did the ASD group. When the authors controlled for age and sex, they noted a statistically significant association between ASD and dental caries prevalence. A significantly higher percentage of patients with ASD than unaffected patients were uncooperative and required dental treatment to take place under general anesthesia. Caries prevalence and severity in patients with ASD were not associated with institutionalization, presence of seizure disorder or additional diagnosis. CONCLUSIONS People with ASD were more likely to be caries-free and had lower DMFT scores than did their unaffected peers. Significantly more patients with ASD than unaffected patients were uncooperative and required general anesthesia to undergo dental treatment.
The psychology of unforgiveness and forgiveness and implications for clinical practice

Unforgiveness and forgiveness are distinct. One cannot forgive unless unforgiveness has occurred, but one might reduce unforgiveness by many ways—only one of which is forgiveness. We present a model intended to further assist and guide subsequent empirical exploration. The model explains the personal, relationship, and environmental factors that lead people to either unforgiveness or forgiveness. Related areas are reviewed to stimulate as yet unexplored research and clinical efforts related to forgiveness. Clinical protocols for promoting forgiveness in enrichment, preventative, and therapeutic contexts are described.
Finding le mot juste: Differences between bilingual and monolingual children's lexical access in comprehension and production*

By school age, some bilingual children can score equivalently to monolinguals in receptive vocabulary but still lag in expressive vocabulary. In this study, we test whether bilingual children have greater difficulty with lexical access, as has been reported for adult bilinguals. School-aged French–English bilingual children were given tests of receptive vocabulary and picture naming. The bilingual children's performance was compared to English monolinguals'. We found that bilingual children scored slightly lower on some measures of comprehension and lower on producing the target word. The bilinguals were more likely to correctly identify the target picture even if they had not produced the name. The differences in comprehension but not production could be statistically accounted for by the variation in receptive vocabulary. These results suggest that, school-aged bilinguals can be close to monolinguals in receptive vocabulary but have a harder time accessing the exact word for production. We discuss reasons for this difficulty with lexical access and strategies that children used when they did not produce the target word.
Collaborative filtering recommender systems

Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision- making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce . They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
Back Pain Exacerbations and Lost Productive Time Costs in United States Workers

Study Design. Cross-sectional with follow-up case-control component. Objectives. To measure the prevalence of back pain (BP) and back pain exacerbations, describe BP features and functional impairment, estimate BP-related lost productive time (LPT) and costs, and assess the relation between pain exacerbations and lost productive time. Summary of Background Data. BP is associated with substantial lost work time. However, little is known about the extent to which BP with or without exacerbation explains lost work time. Methods. A national telephone survey of the U.S. workforce identified 320 workers 40 to 65 years of age with BP defined by NHANES I criteria and 91 matched non-BP controls. Participants self-reported pain characteristics, lost productive time (absenteeism and presenteeism) in the previous 2 weeks, activity limitations, and demographics. A population-weighting adjustment was applied to estimates to account for selection bias and ensure that estimates of certain sample demographic subgroups’ totals conformed to the Current Population Survey. Results. The 2-week period prevalence of BP was 15.1%; 42% of workers with BP experienced pain exacerbations. BP prevalence was associated with demographic factors, but BP exacerbations were not. BP was reported by 42.6% of all workers. Workers with exacerbations reported more days with BP than those without exacerbations. Workers with exacerbations were significantly more likely than those without such exacerbations to report activity limitation (88.4% vs. 60.7%; P < 0.0001) and BP-related LPT (22.1% vs. 13.0%; P = 0.0259). BP in workers 40 to 65 years of age costs employers an estimated $7.4 billion/year. Workers with BP exacerbations account for 71.6% of this cost. Conclusions. Workers with BP exacerbations account for a disproportionate share of the cost of BP-related lost productive time.
On the $\mathcal{NP}$-hardness of GRacSim Drawing and k-SEFE Problems

We study the complexity of two problems in simultaneous graph drawing. The first problem, GRacSim Drawing, asks for finding a simultaneous geometric embedding of two graphs such that only crossings at right angles are allowed. The second problem, k-SEFE, is a restricted version of the topological simultaneous embedding with fixed edges (SEFE) problem, for two planar graphs, in which every private edge may receive at most $k$ crossings, where $k$ is a prescribed positive integer. We show that GRacSim Drawing is $\mathcal{NP}$-hard and that k-SEFE is $\mathcal{NP}$-complete. The $\mathcal{NP}$-hardness of both problems is proved using two similar reductions from 3-Partition.
Mortality and coronary heart disease among men studied for 23 years.

Twenty-year coronary heart disease (CHD) incidence is analyzed for 279 men, CHD-free at the ages of 47 through 57 years and characterized by three examinations before 20 annual follow-up examinations. Sixty men developed CHD, 42 dying or suffering infarction. Among 20 entry characteristics, three had major predictive power, especially for CHD death or infarction: cold pressor test, a high level of serum cholesterol, and systolic blood pressure. Smoking and relative weight seemed unimportant. Major CHD incidence was proportional to cholesterol to the 3.4 power. Five-variable multiple logistic analysis permitted placement of men into deciles of estimated risk differing more than tenfold in CHD incidence. The combination of only cholesterol and systolic pressure was nearly as good. The multiple logistic coefficients applied to five-year data on 1,287 men among whom 182 CHD developed in 182 yielded satisfactory agreement between observed and predicted distributions of CHD cases in classes of estimated risk.
EFFICIENT INTRUSION DETECTION SYSTEM BASED ON SUPPORT VECTOR MACHINES USING OPTIMIZED KERNEL FUNCTION 1

An efficient intrusion detection system requires fast processing and optimized performance. Architectural complexity of the classifier increases by the processing of the raw features in the datasets which causes heavy load and needs proper transformation and representation. PCA is a traditional approach for dimension reduction by finding linear combinations of original features into lesser number. Support vector machine performs well with different kernel functions that classifies in higher dimensional at optimized parameters. The performance of these kernels can be examined by using variant feature subsets at respective parametric values. In this paper SVM based intrusion detection is proposed by using PCA transformed features with different kernel functions. This results in optimal kernel of SVM for feature subset with fewer false alarms and increased detection rate.
Ground, Symmetric and Central Vortex States in Rotating Bose-Einstein Condensates

We study ground, symmetric and central vortex states, as well as their energy and chemical potential diagrams, in rotating Bose-Einstein condensates (BEC) analytically and numer- ically. We start from the three-dimensional (3D) Gross-Pitaevskii equation (GPE) with an angular momentum rotation term, scale it to obtain a four-parameter model, reduce it to a 2D GPE in the limiting regime of strong anisotropic conflnement and present its semiclassical scaling and geometrical optics. We discuss the existence/nonexistence problem for ground states (depending on the angular velocity) and flnd that symmetric and central vortex states are independent of the angular rotational momentum. We perform numerical experiments computing these states using a continuous normal- ized gradient ∞ow (CNGF) method with a backward Euler flnite difierence (BEFD) discretization. Ground, symmetric and central vortex states, as well as their energy conflgurations, are reported in 2D and 3D for a rotating BEC. Through our numerical study, we flnd various conflgurations with several vortices in both 2D and 3D structures, energy asymptotics in some limiting regimes and ratios between energies of difierent states in a strong replusive interaction regime. Finally we report the critical angular velocity at which the ground state loses symmetry, numerical veriflcation of dimension reduction from 3D to 2D, errors for the Thomas-Fermi approximation, and spourous numerical ground states when the rotation speed is larger than the minimal trapping frequency in the xy plane.
MovementFinder: Visual analytics of origin-destination patterns from geo-tagged social media

Geo-tagged social media data can be viewed as sampling of people's trajectories in daily life. It consists of people's movements and embeds the semantics of movements. However, it is challenging to reveal patterns from the sparse and irregular sampling data. We proposed an interactive multi-filter visualization approach to analyze the spatial-temporal movement pattern in people's daily life. People's trajectories are visualized on the map with multiple functional layers. With our visual analytics tools, users are able to drill down to details, with the awareness of the origin-destination flow patterns of spatial, temporal, and semantic meaning.
Three-layer Spatial Sparse Coding for Image Classification

In this paper, we propose a three-layer spatial sparse coding (TSSC) for image classification, aiming at three objectives: naturally recognizing image categories without learning phase, naturally involving spatial configurations of images, and naturally counteracting the intra-class variances. The method begins by representing the test images in a spatial pyramid as the to-be-recovered signals, and taking all sampled image patches at multiple scales from the labeled images as the bases. Then, three sets of coefficients are involved into the cardinal sparse coding to get the TSSC, one to penalize spatial inconsistencies of the pyramid cells and the corresponding selected bases, one to guarantee the sparsity of selected images, and the other to guarantee the sparsity of selected categories. Finally, the test images are classified according to a simple image-to-category similarity defined on the coding coefficients. In experiments, we test our method on two publicly available datasets and achieve significantly more accurate results than the conventional sparse coding with only a modest increase in computational complexity.
Work Family Programs and the Employment Relationship

This research was supported by the Spencer Foundation. I am grateful to Rose Batt for superb research assistance and to Lotte Bailyn, Steve Barley, Peter Cappelli, David Knoke, and Maureen Scully for comments. Using data from a representative sample of American private-sector establishments, this paper explains variation across firms in the implementation of work/family programs by examining how these are related to the employment strategy of organizations. The central hypothesis is that firms seeking to implement so-called high-performance or high-commitment work systems, incorporating employee involvement and quality programs, are more likely to adopt work/family programs as part of an effort to build up the level of workforce commitment to the enterprise. This hypothesis is tested, controlling for two other broad hypothesized effects: (1) that adoption of work/family programs is linked to the demand for them arising either from workforce problems such as absenteeism and turnover or from pressure from the labor force; and (2) that adoption is linked to whether employers already have in place elements of well-developed internal labor markets such as job ladders and human resource departments. Results show considerable support for the link between work/family programs and the use of high-commitment work systems. '