Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement-based TCP parameter tuning in cloud data centers

TCP congestion control has been a native part of all modern Operating System implementations where parameters are initialized assuming an underlying high Bandwidth Delay Product (BDP) environment. However, the significantly lower BDP in Data Centre (DC) networks makes such conservative transport-layer parameters together with deep-buffered switches and bursty traffic a factor of performance degradation, eventually leading to throughput incast collapse. In this paper, we propose a Software Defined Networking (SDN) approach to tune TCP initial window and retransmission timers for newly created flows based on a network-wide view created by aggregating known characteristics and temporal measurements at a central controller. Through simulation, we show the detrimental effect static TCP parameters have on mice flows and demonstrate the benefits of network-aware per-flow tuning. We show that the average latency under bursty traffic can be improved by a factor of eight, and that flow start and completion times can be improved by a factor of two and five, respectively.
Online prime labeling and generation of synopsis for XML query estimation

XML query optimizers use selectivity estimators to choose the best possible query execution plan. However, one the main components of most estimators is the generation of a synopsis of the XML database. In this paper, we present a novel approach for online construction of an XML synopsis based on prime labeling and counting. The resulting synopsis will be used to estimate different path and twig queries with parent-child or ancestor-descendant relationships. Besides the efficiency of the proposed approach in terms of estimation accuracy and low storage requirements, it can process streams of XML documents as they are received. We experimentally compared the proposed algorithm with one of the state-of-the-art algorithms, namely the Sampling, and it showed drastic improvement in the estimation results. For example, when allocating the same memory budget for both algorithms, the overall average error rate of our algorithm was 0.54% whereas it was 55.3% for the Sampling algorithm.
A model-driven approach for developing business applications: experience, lessons learnt and a way forward

We discuss our experience in using model-driven techniques to build large business applications on a variety of architectures and technology platforms. Our foray into model-driven techniques began 12 years ago when our organization decided to develop a banking product that was to be capable of being delivered on multiple technology platforms and capable of easily keeping pace with technological advances. We began by developing a set of modeling notations to specify different architectural layers of the application and a set of code generators that transform these models into an implementation. Separating business functionality from technological concerns and model-based code generation resulted in significant productivity and quality gains. We then discovered that process aspect needed to be addressed as well so as to extend these gains to other phases of development lifecycle. Modeling of workspaces and a role-based process enabled a large team to effectively coordinate development effort leading to significant reduction of integration effort. Encouraged by these benefits, many large development projects readily adopted the model-driven approach despite an initial investment in learning how to create the models. However, even though the benefits of automated generation of architectural code were quite obvious, small and medium sized projects found this upfront expense too large. We developed a code-centric approach that delivers these benefits without incurring the expense of having to explicitly create the models. Ability to easily move to the code-centric approach provided additional flexibility to the model-centric approach. Looking forward, we feel the focus of model-driven techniques needs to expand to address other aspects such as integration, maintenance, testing etc. We also feel there is a need to look at interpretive model-driven techniques in order to address the growing need of rapid customizability
Lesbian and Gay Immigrants' Post-Migration Experiences: An Integrative Literature Review

ABSTRACT This integrative literature review synthesizes recent empirical knowledge about lesbian and gay immigrants' post-migration experiences. Twenty-four studies with various designs are included. The literature shows that those immigrants encounter some opportunities but also many challenges on economical, social and identity levels, thus soliciting coping strategies. Results are synthesized under five topics: the delicate situation in which they find themselves for revealing their lesbian or gay sexual orientation; the racist and homophobic discrimination they can experience; their renegotiation of socioeconomic status; the identity and affiliation challenges they have to overcome; and the hardships that can impact their health.
What zombies can't do: A social cognitive neuroscience approach to the irreducibility of reflective consciousness

Could an individual act and speak just like other individuals without having any internal conscious experience? Belief in the possibility of so-called philosophical zombies serves as a litmus test for whether someone believes in some form of mind-body dualism or materialism. This chapter focuses on a related hypothesis that is emerging within psychology referred to as the psychological zombie hypothesis. This hypothesis suggests that our behaviors and judgements are produced by an 'inner-zombie' whose mental work does not depend on conscious awareness, and that those mental operations which are typically accompanied by conscious awareness do not rely on awareness to generate the operations and their outputs. This hypothesis suggests that mental operations which are typically accompanied by conscious awareness can be produced in the absence of conscious awareness, thus demonstrating the superfluousness of awareness.
Precise Spatial Relationships between Mossy Fibers and Climbing Fibers in Rat Cerebellar Cortical Zones

Classically, mossy fiber and climbing fiber terminals are regarded as having very different spatial distributions in the cerebellar cortex. However, previous anatomical studies have not studied these two major cerebellar inputs with sufficient resolution to confirm this assumption. Here, we examine the detailed pattern of collateralization of both types of cerebellar afferent using small injections of the bidirectional tracer cholera toxin b subunit into the posterior cerebellum. The cortical and zonal location of these injections was characterized by mapping climbing fiber field potentials, the distribution of retrogradely labeled olivary neurons, and the intrinsic zebrin pattern of Purkinje cells. Labeled climbing fiber collaterals were distributed as longitudinal strips and were always accompanied by clusters of labeled mossy fiber rosettes in the subjacent granular layer. Two- and three-dimensional reconstructions and quantitative analysis showed that mossy fibers also collateralized to other stripe-like regions usually below Purkinje cells with the same zebrin-positive or zebrin-negative characteristics as that of the injection site and associated climbing fiber collaterals. The distribution of retrogradely labeled neurons in two major sources of mossy fibers, the lateral reticular and basilar pontine nuclei, revealed interlobular and some interzonal differences. These data indicate that nonadjacent cerebellar zones, sharing the same climbing fiber input and zebrin identity, also share a common mossy fiber input. Other cerebellar cortical regions that receive collaterals from the same mossy fibers usually also have the same zebrin signature. Together with the distribution of neurons in precerebellar centers, the findings suggest a revision of the modular hypothesis for information processing in the cerebellar cortex.
Metric measurement from street view sequences with simple operator assistance and phase correlation based frame selection

This paper presents a metric measurement approach from sequences of images captured from a moving spherical camera without the need of additional equipment, such as laser scanners or motion detection units. The user assists the algorithms with simple inputs to facilitate the measurement process. The operator initially selects a keyframe that contains the object of interest that is to be measured. Next, a suitable pair is selected for this keyframe, automatically, using a novel phase correlation based approach proposed in this paper. Then, correspondence matching between these two images is performed using scale-invariant feature transform (SIFT) and these features are refined using RANdom SAmple Consensus (RANSAC) and information obtained from the phase correlation stage. As a last step conversion form the image domain to the 3D domain is performed. The user selects two corresponding point pairs in both frames, corresponding to the edges of the distance that is to be measured, and the metric distance between these two points is obtained. During this process, the height information of the camera with respect to the ground is used as basic reference to obtain metric results. Experimental results show that the proposed methods can provide metric measurements with up to 10% error.
Geometric approach of quasi-linear kernel composition for support vector machine

This paper proposes a geometric way to construct a quasi-linear kernel by which a quasi-linear support vector machine (SVM) is performed. A quasi-linear SVM is a SVM with quasi-linear kernel, in which the nonlinear separation boundary is approximated by using multi-local linear boundaries with interpolation. However, the local linearity extraction for the composition of quasi-linear kernel is still an open problem. In this paper, according to the geometric theory, a method based on piecewise linear classifier is proposed to extract the local linearity in a more precise and efficient way. We firstly construct a function set including multiple linear functions and each of those functions reflects one part of linearity of the whole nonlinear separation boundary. Then the obtained local linearity is added as prior information into the composition of quasi-linear kernel. Experimental results on synthetic data sets and real world data sets show that our proposed method is effective to improve classification performances.
Task Rescheduling using Relational Reinforcement Learning

Generating and representing knowledge about heuristics for repair-based scheduling is a key issue in any rescheduling strategy to deal with unforeseen events and disturbances. Resorting to a feature-based propositional representation of schedule states is very inefficient and generalization to unseen states is highly unreliable whereas knowledge transfer to similar scheduling domains is difficult. In contrast, first-order relational representations enable the exploitation of the existence of domain objects and relations over these objects, and enable the use of quantification over objectives (goals), action effects and properties of states. In this work, a novel approach which formalizes the re-scheduling problem as a Relational Markov Decision Process integrating first-order (deictic) representations of (abstract) schedule states is presented. Task rescheduling is solved using a relational reinforcement learning algorithm implemented in a real-time prototype system which makes room for an interactive scheduling strategy that successfully handle different repair goals and disruption scenarios. An industrial case study vividly shows how relational abstractions provide compact repair policies with minor computational efforts.
Theory and Applications

Decomposition and Discretization Methods In this chapter we briefly introduce the underlying ideas of the decomposition analysis and discretization methods to obtain the abstract operator equations, which are used and studied for our time decomposition methods. 2.1 Decomposition In this section we discuss the decomposition of the evolution equations to obtain separate underlying equations. The simpler equation parts are discretized to obtain an abstract operator equation formulation. Based on this formulation we apply the decomposition methods, see Chapters 3 and 4 for detailed explanations. In the decomposition of the evolution equations, we deal with two main problems: • Decomposition of the evolution equation • Decomposition methods to solve the decoupled evolution equation The decomposition can be performed before the discretization, in which case the physical behaviors of the operators are used to decide the type of the splitting operators. Further, the decomposition can be done after the space and time discretization of the partial differential equation, such that the spectral analysis or stability conditions can be used to decide whether operators are initiated for the splitting process. We focus on the decomposition methods, which are applied after the space discretization, and we obtain a system of ordinary differential equations. On this level, the consistency and stability analysis can be treated on an abstract level as a Cauchy problem in a more abstract level. In the next subsection, we consider the decomposition of the evolution equations and present two methods: the direct method called the physical decomposition method and the more indirect method called the mathematical decomposition method.
Psychological Contract Violation in IT Megaprojects in the Context of Public Private Partnerships: The German TollCollect Case

Prior research in the context of public private partnerships has mainly focused on organizational and risk management aspects. Because there is a growing number of IT megaprojects in the context of public private partnerships, recent work concentrates on the explanation of the relationship between public and private partners and the success factors for these relations. However, we still have a lack of understanding on the causes and consequences for IT megaproject failure in the context of public private partnerships. In this exploratory, qualitative single-case study from the German TollCollect IT megaproject we draw on findings from the psychological contract theory to explain psychological contract violation as a threat to IT megaproject success. Our research model suggests that the management of common understanding and expectations, moderated by political and public pressure of a public-private environment, affects the stability of the psychological contract and therefore the success of an IT megaproject in the context of public private partnerships.
Automatic commercial aircraft-collision avoidance in free flight: the three-dimensional problem

In this paper, optimal resolution of air-traffic (AT) conflicts were considered. Aircraft are assumed to cruise within a free altitude layer and are modeled in three dimensions with variable velocity and proximity bounds. Aircraft cannot get closer to each other than a predefined safety distance. The problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace were considered. For such systems of multiple aircraft, the total flight time by avoiding all possible conflicts were minimized. This paper proposes a formulation of the conflict avoidance problem as a mixed-integer nonlinear-programming problem. In the author's case, only velocity changes are admissible as maneuvers. Nevertheless, in subsequent work, simultaneous velocity and heading angle changes will be checked too. Simulation results for realistic aircraft conflict scenarios are provided.
Effectiveness of a Play Therapy Intervention on Children's Externalizing and Off-Task Behaviors

Children's externalizing and off-task behaviors are deemed a major concern for elementary teachers and interfere with the classroom learning environment (Abidin & Robinson, 2002; Shillingford-Butler & Theodore, 2013). Children with these behaviors are at risk of falling behind academically and experiencing ongoing behavioral problems throughout their development. This single-case research design investigates the effectiveness of Adlerian play therapy on the behaviors of two elementary-aged children who have externalizing and off-task classroom behaviors. Results for this intervention suggest Adlerian play therapy may show promise as an effective intervention for school counselors and school-based counselors.
Domestic Violence and Poverty

Purpose: Domestic violence poses a major challenge to social workers. Low-income families are significantly more likely to have to contend with domestic violence, as poverty can act as a fuelling factor in this type of conflict. The objective of this study was to explore and describe the experiences of low-income abused women. Method: A qualitative research approach was followed to explore and describe the phenomenon of low-income abused women. Purposive and snowball sampling were used to obtain 20 participants. Data were collected by means of semi-structured interviews. The data were categorized into four themes. Results: Domestic violence and poverty are issues that some women might experience. Low-income abused women are especially more vulnerable to domestic violence and poverty limits choices and resources. Conclusion: Domestic violence and poverty should be seen in a serious light. Social workers should recognise the complexity and correlation between domestic violence and poverty.
Literature Analysis on Malware Detection

Usage of Android smartphones is more as compared to another smartphones due to its Open Source Operating System. Due to its Open OS, Android enables us to install third party applications. However, Security is one of the main concerns in Android. Security threats of malicious applications are rapidly increasing due to the nature of the third party applications where only developers can assign required permissions. Applications are installed on the basis of all or nothing basis. For this reason, attackers can inject into a normal application with inappropriately acquired permissions. In this paper, we have described the android architecture, various types of malware and literature analysis for security considerations in android smartphones, including the various general approaches and techniques for detection of various malwares.
Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines.
Hardware Design and System Calibration for Electrical Capacitance Tomography System

The mathematic model of the oil-water two-phase distribution information is established based on double integral infinitesimal principle, an ECT measurement system of 8-electrode oil-water two-phase flow is set up that is comprised of a measurement circuit with stray-capacitance-resistance and a 80C32 SCM, both of which control the measurement among the pole plates and the capture system. The image reconstruction for oil-water two-phase cross-sectional image and the calculating of oil-water two-phase water content can be accomplished by an adaptive threshold image reconstruction algorithm, and the precision of reconstruction is about 1%, which can meet the need of identification of flow regime for oil-water two-phase flow.
Characterization of perovskite solar cells: Towards a reliable measurement protocol

Lead halide perovskite solar cells have shown a tremendous rise in power conversion efficiency with reported record efficiencies of over 20% making this material very promising as a low cost alternative to conventional inorganic solar cells. However, due to a differently severe “hysteretic” behaviour during current density-voltage measurements, which strongly depends on scan rate, device and measurement history, preparation method, device architecture, etc., commonly used solar cell measurements do not give reliable or even reproducible results. For the aspect of commercialization and the possibility to compare results of different devices among different laboratories, it is necessary to establish a measurement protocol which gives reproducible results. Therefore, we compare device characteristics derived from standard current density-voltage measurements with stabilized values obtained from an adaptive tracking of the maximum power point and the open circuit voltage as well as characteristics extracted from time resolved current density-voltage measurements. Our results provide insight into the challenges of a correct determination of device performance and propose a measurement protocol for a reliable characterisation which is easy to implement and has been tested on varying perovskite solar cells fabricated in different laboratories.