Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering complex adaptations in highly heterogeneous distributed systems

Distributed systems now encounter extreme heterogeneity in the form of diverse devices, network types etc., and also need to dynamically adapt to changing environmental conditions. Self-adaptive middleware is ideally situated to address these challenges. However, developing such software is a complex task. In this paper, we present the Gridkit self approach to the engineering of reflective middleware; this embraces state of the art software engineering practices, and flexible dynamic adaptation mechanisms to better support system developers. Domain specific frameworks are modeled and developed to enhance configurability and reconfigurability. We evaluate this approach using case studies in the domains of service discovery and network overlays. These demonstrate the benefits of the approach in terms of aiding and simplifying the process of creating self-configuring and self-adaptive software.
Law's Fragile State: Colonial, Authoritarian and Humanitarian Legacies in Sudan . Mark Fathi Massoud. Cambridge, Cambridge University Press, 2013. 297. Hardback £60, ISBN 9781107026070

States marred by conflicts and serious human rights violations are frequently associated with lawlessness. Sudan is no exception. In challenging this view, Massoud provides an important and original contribution to extant literature. His groundbreaking monograph is both an in-depth empirical study of how colonial states, authoritarian regimes and humanitarian actors use law, and a history of the law in Sudan. As such, it addresses an important gap in socio-legal studies on Sudan. Its use of sources is impressive, including invaluable personal interviews with a range of actors (175 in total), personal observations of law in action, and archival research in the United Kingdom, Egypt and Sudan. The book also provides unique insights into recent developments concerning the judiciary in Sudan, such as court infrastructure, gathered by the author “based on data collected at and certified by Sudan Judiciary Department of Statistics”. 1 This reliance on first hand sources generates important, contextual information. However, this approach does to some extent come at the expense of references to the wealth of materials that have recently become available on the law in Sudan. This includes reports by the United Nations and African Union (particularly the AU High-Level Panel on Darfur) resulting from their engagement in Sudan, publications by civil society initiatives (such as the Project for Criminal Law Reform in Sudan)2 and books on law reform.3 Setting out a broad understanding of law and taking an approach that “analyse[s] the legal order as a whole” (chapter 1), Massoud discusses pertinent developments in Sudan’s legal history with reference to colonial, authoritarian and humanitarian approaches. While the book is comprehensive in scope from 1898 onwards, it does not examine the use of law under the Mahdiyya (1885–98) which merits further consideration as a prototype for the authoritarian use of law in Sudan, particularly of Shari’a (Islamic law). Customary law is also excluded because of “access limitations” and the understandable focus on state laws. However, its inclusion would have added an important dimension. This is in relation to both the intricate relationship between formal and customary laws, and the part that the co-optation and manipulation of customary law have played in Sudan’s legal politics.4 It is
Insecure Identities. The Approval of a Biometric ID Card in Mexico

Former Mexican President, Felipe Calderon, signed a decree to implement a compulsory ID with biometric traits for all the Mexican population. At first sight it seems like a good policy because Mexico has suffered a lot of problems with identification, but if we analyze the policy we find it to be full of problems and contradictions, particularly because of the corrupt nature of Mexican politics.
Smooth Energy Auto-Estimation for Graph Cuts Algorithm

Graph cuts is one of the best algorithms for disparity estimation. In its energy function, the smooth energy that is necessary for achieving the disparity map with good smoothness and good discontinuity varies from images to images. Based on image texture information, this paper presents a novel approach to estimate smooth energy automatically and precisely. To train the parameters of representation, an algorithm is proposed to auto-estimate the precise smooth energy coefficient,given the stereo images and the ground-truth disparity map. The experimental results show that, with the estimated smooth energy, the disparity map with less gross error could be achieved. And the disparity map obtained by hierarchical algorithm with estimated smooth energy for each level is much better than the one with same smooth energy for each level.
Integrating Language and Ontology Engineering

Creating new modeling environments has become a relatively low-cost investment thanks to meta modeling environments and language workbenches that can automatically synthesize environments from lan- guage specifications. However, the currently existing tools are focused on language syntax and execution/simulation rather than providing means to reason with semantic properties from the real world. It appears that reasoning opportunities as they arise in ontology research (e.g., based on description logics) are currently not exploited in the field of language en- gineering. In this vision paper, we explore the integration of the hitherto rather isolated areas of language engineering and ontology engineering in order to exploit the potential of using reasoning for models expressed in user-defined languages.
Quantitative Analysis of the Relative Transcript Levels of ABC Transporter Atr Genes in Aspergillus nidulans by Real-Time Reverse Transcription-PCR Assay

ABSTRACT The development of assays for quantitative analysis of the relative transcript levels of ABC transporter genes by real-time reverse transcription-PCR (RT-PCR) might provide important information about multidrug resistance in filamentous fungi. Here, we evaluate the potential of real-time RT-PCR to quantify the relative transcript levels of ABC transporter Atr genes from Aspergillus nidulans. The AtrA to AtrD genes showed different and higher levels in the presence of structurally unrelated drugs, such as camptothecin, imazalil, itraconazole, hygromycin, and 4-nitroquinoline oxide. We also verified the relative transcript levels of the Atr genes in the A. nidulans imazalil-resistant mutants. These genes displayed a very complex pattern in different ima genetic backgrounds. The imaB mutant has higher basal transcript levels of AtrB and -D than those of the wild-type strain. The levels of these two genes are comparable when the imaB mutant is grown in the presence and absence of imazalil. The imaC, -D, and -H mutants have higher basal levels of AtrA than that of the wild type. The same behavior is observed for the relative transcript levels of AtrB in the imaG mutant background.
1 5 Ju l 2 01 3 Some Improvements to Total Degree Tests ∗

A low-degree test is a collection of simple, local rules for checking the proximity of an arbitrary function to a lowdegree polynomial. Each rule depends on the function’s values at a small number of places. If a function satisfies many rules then it is close to a low-degree polynomial. Low-degree tests play an important role in the development of probabilistically checkable proofs. In this paper we present two improvements to the efficiency of low-degree tests. Our first improvement concerns the smallest field size over which a low-degree test can work. We show how to test that a function is a degree d polynomial over prime fields of size only d+ 2. Our second improvement shows a better efficiency of the low-degree test of [14] than previously known. We show concrete applications of this improvement via the notion of “locally checkable codes”. This improvement translates into better tradeoffs on the size versus probe complexity of probabilistically checkable proofs than previously known .
Facing the Dreaded: Does Mindfulness Facilitate Coping with Distressing Experiences? A Moderator Analysis

Increasing evidence shows that mindfulness is positively related to mental health; however, the nature of this relationship is not fully understood. The current study used structural equation modeling to investigate the hypothesis that mindfulness moderates the association between the occurrence of unavoidable distressing experiences (UDE) and mental health. Participants from a community sample (N = 376) completed the Freiburg Mindfulness Inventory, the Positive and Negative Affect Scale, the Brief Symptom Inventory, the Inventory of Approach and Avoidance Motivation, and the Incongruence Scale. Results indicated that mindfulness moderated the association between unavoidable distressing events and psychopathological symptoms/negative affect. Thus, mindfulness may contribute to enhance the ability to cope with UDE and thus mitigate the detrimental effects of these experiences on mental health.
Sensor-Based Identification of Opportune Moments for Triggering Notifications

Today's smartphones will issue a notification immediately after an event occurs, repeating unanswered notifications in fixed time intervals. The disadvantage of this issue-and-repeat strategy is that notifications can appear in inconvenient situations and thus are perceived as annoying and interrupting. The authors study the mobile context as inferred through a phone's sensors for both answered and ignored notifications. They conducted a large-scale, longitudinal study via the Google Play store and observed 6,581 notifications from 79 different users over 76 days. Their derived model can predict opportune moments to issue notifications with approximately 77 percent accuracy. Their findings could lead to intelligent strategies to issue unobtrusive notifications on today's smartphones at no extra cost. This article is part of a special issue on managing attention.
Freedom of Speech: Rights and Liberties under the Law

An innovative narrative approach combines history, politics, and legal doctrine to explore the origin and evolution of Americans' constitutional right to free speech. In a field dominated by jargon-filled texts and march-of-progress treatments, this book presents an insightful introduction to freedom of speech, skillfully blending legal analysis with accounts of how staunchly contested historical, political, and cultural issues often influenced legal reasoning. The volume traces the origins of the freedom in English law and its development through the founding of the United States, and examines how the unique struggles of 19th century Americans over such issues as political parties, slavery, women's rights, and economic inequality transformed this traditional English right into a distinctively American one. The book outlines the ways in which the U.S. Supreme Court became the prime interpreter of the meaning of free speech and introduces readers to current court rulings on the First Amendment. It also speculates about the political and legal developments likely to emerge in the new century. * A-Z entries survey key individuals, laws, events, judicial decisions, statutes, institutions, organizations, and concepts * Four narrative chapters examine constitutional history, evolution of ideas in this area, contemporary concerns and controversies, and prospects for the near future based on today's challenges to the status quo
CROSS-SCALE NUMERICAL SIMULATIONS USING DISCRETE PARTICLE MODELS

Abstract We propose a concept for a homogenous computational model in carrying out cross-scale numerical experiments on liquids. The model employs the particle paradigm and comprises three types of simulation techniques: molecular dynamics (MD), dissipative particle dynamics (DPD) and smoothed particle hydrodynamics (SPH). With respect to the definition of the collision operator, this model may work in different hierarchical spatial and time scales as: MD in the atomistic scale, DPD in the mesoscale and SPH in the macroscale. The optimal computational efficiency of the three types of cross-scale experiments are estimated in dependence on: the system size N-where N is the number of particles-and the number of processors P employed for computer simulation. For the three-hierarchical-stage, as embodied in the MD-DPD-SPH model, the efficiency is proportional to N 8/7 but its dependence on P is different for each of the three types of cross-scale experiments. The problem of matching the different scales is dis.
Emotion Conversion Based on Prosodic Unit Selection

Voice conversion has been traditionally focused on spectrum. Current systems lack a solid prosody conversion method suitable for different speaking styles. Recently, the unit selection technique has been applied to transform emotional intonation contours. This paper goes one step beyond: it explores strategies for training and configuring the selection cost function in an emotion conversion application. The proposed system, which uses accent groups as basic intonation units and performs conversion also on phoneme durations and intensity, is evaluated by means of a carefully designed subjective test involving the big six emotions. Although the expressiveness of the converted sentences is still far from that of natural emotional speech, satisfactory results are obtained when different configurations are used for different emotions.
Information Maps: A Practical Approach to Position Dependent Parameterization

In this contribution a practical approach to determine and store position dependent parameters is presented. These parameters can be obtained, among others, using experimental results or expert knowledge and are stored in 'Information Maps'. Each Information Map can be interpreted as a kind of static grid map and the framework allows to link different maps hierarchically. The Information Maps can be local or global, with static and dynamic information in it. One application of Information Maps is the representation of position dependent characteristics of a sensor. Thus, for instance, it is feasible to store arbitrary attributes of a sensor's preprocessing in an Information Map and utilize them by simply taking the map value at the current position. This procedure is much more efficient than using the attributes of the sensor itself. Some examples where and how Information Maps can be used are presented in this publication. The Information Map is meant to be a simple and practical approach to the problem of position dependent parameterization in all kind of algorithms when the analytical description is not possible or can not be implemented efficiently.
Effect of viscoelastic postseismic relaxation on estimates of interseismic crustal strain accumulation at Yucca Mountain, Nevada

We estimate the long‐term crustal strain rate at Yucca Mountain (YM), Nevada from GPS velocities taking into account viscoelastic relaxation following recent earthquakes to remove bias associated with transient deformation. The YM data reveal postseismic relaxation in time series non‐linearity and geographic variation of the transient signal. From the data we estimate best‐fitting lower crust and upper mantle viscosities of 1019.5 Pa s and 1018.5 Pa s, respectively. Once the relaxation model predictions are subtracted from the data, the long‐term shear strain accumulation rate is between 16.3 and 25.1 nanostrains/year (ns/yr) to 99% confidence, a range much larger than the formal uncertainties from GPS measurement. We conclude that 1) a Maxwell viscoelastic model cannot explain all the deformation observed at YM, 2) uncertainty in viscosities dominates uncertainty in YM strain rates, and 3) the effects of large, recent earthquakes must be accounted for in seismic hazard studies using GPS.
Domain-oriented Multilevel Methods

For the discretization of elliptic linear PDE's, instead of the usual nodal basis, we use a generating system that contains the nodal basis functions of the nest and all coarser levels. The Galerkin approach now results in an enlarged semideenite linear system to be solved. Traditional iterative methods for that system turn out to be equivalent to modern multilevel methods for the ne grid system. Beside level-oriented iterative methods that lead to multilevel algorithms, other orderings of the unknowns of the enlarged system can be considered as well. A domain-wise block Gauss-Seidel iteration for the enlarged system results in a certain domain decomposition method with convergence rates independent of the mesh width of the ne grid. Furthermore, this approach directly leads to a O(1)-preconditioner for the Schur complement that arises in conventional domain decomposition methods. 1. The Generating System Consider a partial diierential equation with linear, symmetric and elliptic operator Lu = f in with Dirichlet boundary conditions and associated weak formulation a(u; v) = f(v); 8v 2 V: For the discretization on some grid k with uniform mesh width h k = 2 ? k usually a basis B k = f (k) i ; i = 1; ::; n k g with nodal basis functions (k) i is used, that span the corresponding space V k = spanf (k) Here, n k denotes the number of interior grid points and thus the dimension of V k .
Continuous sign language recognition from tracking and shape features using Fuzzy Inference Engine

Fuzzy classifying continuous sign language videos with simple backgrounds with tracking and shape combined features is the focus of this work. Tracking and capturing hand position vectors is the artwork of horn schunck optical flow algorithm. Active contours extract shape features from sign frames in the video sequence. The two most dominant features of sign language are combined to build sign features. This feature matrix is the training vector for Fuzzy Inference Engine (FIS). The classifier is tested with 50 signs in a video sequence. Ten different signers created 50 signs. Different instances of FIS are tested with different combination of feature vectors. The results are compared with our previous work using no tracking and with discrete sign language database. A word matching score (WMS) gauges the performance of the classifiers. A 92.5% average matching score is reported in this work. A through comparisons for FIS gesture classifier between Discrete Cosine Transform features, Elliptical Fourier descriptor features and the proposed hybrid features for continuous sign language videos show a 40% jump in word matching score.
The Term Structure of Interest Rates and the Macroeconomy: Learning About Economic Dynamics from a FAVAR

Expectations about macroeconomic developments are important determinants of long term interest rates. In this paper, I compare two different assumptions on how agents may form their expectations about the economy and yields in a pseudo real time exercise. Based on the no-arbitrage factor-augmented vector autoregression model developed by Moench (2008), I apply a purely econometric learning scheme as proposed by Laubach, Tetlow, and Williams (2007) in the estimation and compare the results to those of an estimation without discounting. In- and out-of-sample performance indicates that the agents are more inclined to form their expectations according to the learning approach.
PROJECT Deliverable D 3 . 4 “ Design and Implementation of Technical Objective 2 ( Path availability estimation

This deliverable is part of WP3 (Cognitive network and system experimentation). The feasibility, benefits and applicability of introducing a cognitive engine in the ECODE architecture are experimented using a number of use cases covering different problem areas identified as Internet architectural and design challenges. In particular we address techniques for path availability estimation, for improving network recovery and resiliency, and profile-based accountability. The following three use cases are studied in-depth: • Path availability: the goal is to design and experiment techniques, embedded in our so-called IDIPS server, to rank internet paths based on their characteristics, such as delays, and available throughput. To this end, on-line machine learning techniques are used to estimate future path characteristics based on past measurements , in order to reduce the future measurement load. Machine learning techniques are also used to Improve Internet Coordinate Systems to better estimate delays between nodes with scalable active delay measurements. • Network recovery and resiliency: the goal is to reduce the recovery time and increase the recoverability, when routes are updated in a network. To this end, we design and experiment machine learning techniques to minimize packet loss during rerouting and infer SRLGs (Shared Risk Link Groups, i.e. sets of links that can fail all together) in networks, and anticipate their occurrence very early. • Profile-based accountability: the goal is to infer the demand subscribers are requesting from the network, so that the network resources can be fairly allocated and accountability properly imposed respecting the contract subscribers have with their operator. To this end, we design and experiment machine learning techniques to cluster customer profiles based on their network resource demands, and to classify customers according to the learned profiles. Based on this experience, we describe how we currently view the mapping of these use cases onto the anticipated ECODE architecture designed earlier. In particular we show how the functionalities of all the use cases are split among the Routing Engine (RE), Forwarding Engine (FE) and Machine Learning Engine (MLE) of the architecture, and which message exchanges are necessary among these components. From a research perspective, the main results of this deliverable can be summarized as follows: • Measuring a path performance according to one or several metrics, such as delay or bandwidth, is becoming more and more popular for applications. However, constantly probing the network is not suitable. To make measurements more scal-able, the notion of clustering has emerged. …
A fair scalable inter-domain TCP marker for multiple domain DiffServ networks

The differentiated services (DiffServ) is proposed to provide packet level service differentiations in a scalable manner. To provide an end-to-end service differentiation to users having a connection over multiple domains, as well as a flow marker, an intermediate marker is necessary at the edge routers, and it should not be operated at a flow level due to a scalability problem. Due to this operation requirement, the intermediate marker has a fairness problem among the transmission control protocol (TCP) flows since TCP flows have intrinsically unfair throughputs due to the TCP's congestion control algorithm. Moreover, it is very difficult to resolve this problem without individual flow state information such as round trip time (RTT) and sending rate of each flow. In this paper, to resolve this TCP fairness problem of an intermediate marker, we propose a fair scalable marker (FSM) as an intermediate marker which works with a source flow three color marker (sf-TCM) operating as a host source marker. The proposed fair scalable marker improves the fairness among the TCP flows with different RTTs without per-flow management. Through the simulations, we show that the FSM can improve TCP fairness as well as link utilization in multiple domain DiffServ networks.