Miracle-dz/UniFAR-FTUs huggingface dataset:
Colored graph transformation rules for model-driven engineering of multi-target systems

Multi-target systems are interactive systems that are aimed at covering multiple contexts of use: by enabling users to carry out their tasks thanks to different input and output interaction modalities, these systems should support multiple computing platforms, multiple users, and multiple environments. This paper introduces a model-driven engineering method for developing user interfaces for such multi-target systems. It relies on a transformational approach that applies graph transformation rules on a graph representation of the models in order to support model-to-model transformation. In order to factor out parts that are common in transformation rules used for similar contexts of use, the notion of colored graph transformation is introduced. Each model element is assigned to a specific color depending on the desired target, here the interaction modality involved. Colored transformation rules based on these colored model elements can be therefore specified and applied in order to produce various user interfaces with different modalities of interaction, depending on the context of use. For this purpose, operations over colored transformation rules are defined: a monocolored transformation rule produces a monomodal user interface for a single context of use (single-target system) while multicolored rules embed capabilities for producing a multimodal user interface for multiple contexts of use (multi-target system). The benefits of using multicolored transformation rules over monocolored ones are obtained in terms of number of rules to specify and to apply, in terms of performance of applying these rules and degree of scalability when a new rule corresponding to a new interaction modality should be introduced.
The Dynamics of Government : a positive analysis ∗

How does the size of the transfer system evolve in the short and in the long run? We model income redistribution as determined by voting among individuals of different types and income realizations. Taxation is distortionary because it discourages effort to accumulate human capital. Voters are fully rational, realizing that transfers have implications also for future economic decisions and taxation outcomes. In our economy, our politically driven redistribution provides insurance, and we investigate to what extent the democratic process provides it appropriately. A general finding is that redistribution tends to be too persistent relative to what would have been chosen by a utilitarian planner under commitment. The difference is larger, the lower is the political influence of young agents, the lower is the altruistic concern for future generations, and the lower is risk-aversion. Furthermore, there tends to be too much redistribution in the political equilibrium. Finally, we find that the political mechanism is important: settings with smooth preference aggregation–we analyze probabilistic voting here–produce less persistence and do not admit multiple expectational equilibria, which occur under majority-voting aggregation. ∗We thank Costas Azariadis, Alex Cuikerman, and Joseph Zeira for comments. †John Hassler: IIES and CEPR, IZA and CESIfo; e-mail: John.Hassler@iies.su. se. ‡Per Krusell: University of Rochester, IIES, and CEPR; e-mail: pekr@troi.cc. rochester.edu. §Kjetil Storesletten: IIES and CEPR; e-mail: Kjetil.Storesletten@iies.su. se. ¶Fabrizio Zilibotti: University College London and CEPR; e-mail: f.zilibotti@ucl.ac. uk.
Methods for bounding end-to-end delays on an AFDX network

Architectures of avionics networks, such as that of the Airbus A380, currently know important evolutions. This is principally due to the increase in the complexity of the embedded systems, in term of rise in number of integrated functions and their connectivity. The evolution of switched Ethernet technologies allows their implementation as an avionics architecture (AFDX: avionics full duplex switched Ethernet). The problem is then to prove that no frame is lost by the network (no switch queue will overflow) and to evaluate the end-to-end transfer delay through the network. The objective of this paper is to present and shortly compare three methods for the evaluation of end-to-end delays: network calculus, queuing networks simulation and model checking
Randomly driven fuzzy key extraction of unclonable images

In this paper, we develop an adjustable Fuzzy Extractor using the Physical Unclonable Functions (PUF) obtained by a common laser engraving method to sign physical objects. In particular, a string (or helper data) is generated by XORing a binary reduction of the PUF observation with the encoding of a randomly generated key, or identifier. Since the binary reduction (or hash) relies on keeping the sign of few random projections of the observation, a measure concentration property bounds, with a controlled accuracy, the distance between two different hashes in function of this of the original images. The error correcting code used to encode the identifier stabilizes therefore both the observation noise and the hashing distortion. In a verification stage, reobserving the PUF with the helper data in hand allows one to authenticate the object if the identifier can be exactly recovered. We conclude this work by calibrating and challenging the system on a database of laser-written PUFs, balancing helper data size, that is, hashing dimensions, and system security.
Research Statement My Approach

Today, large-scale data-intensive computing is more ubiquitous than ever. After decades of academic research, distributed system design and massive-scale data management are real concerns faced by everyday practitioners. Data-intensive services today now include billion-user, geo-replicated applications such as Facebook and Google. Coupled with the increasing interconnectivity of computing systems, from retail inventories to biosensors, and the availability of elastic cloud computing, these services have placed increased demands on today’s data management solutions. Requirements for low latency, “always on,” and scalable operation have led to a substantial restructuring of commodity database systems, resulting in a host of emerging research challenges. My research focuses on the core problems in these large-scale distributed data management systems. In my dissertation research, I have investigated the design of coordination-free, asynchronous distributed database systems and algorithms. Specifically, when is it possible to achieve the benefits of coordination-free execution— including available, low latency, and scalable operation—and when is coordination required? Answering this question enables coordination avoidance: the use of as little coordination (i.e., blocking synchronization between concurrent operations [3]) as possible. When this coordination avoidance is possible, how can we realize its benefits, and what are the performance implications? Practically, coordination avoidance is a useful tool in designing systems and algorithms that circumvent the overheads found in traditional data management systems. For example, we can often avoid wide-area network delays that are lower-bounded by the speed of light and avoid the use of potentially expensive consensus and atomic commitment protocols, leading to regular order-of-magnitude performance gains. I have investigated coordination avoidance in a number of systems contexts. As an example, prior research in data management has left a wide gap between easy-to-use but expensive mechanisms like serializable transactions and less understood but cheap and increasingly adopted approaches such as eventual consistency. One of my goals has been to understand when we can preserve the latency, scalability, and availability benefits of these weaker models (due to their lack of coordination) while providing database functionality and semantics traditionally associated with more coordination-intensive designs (e.g., transactional isolation [2], integrity constraints [3], indexes and views [7]). I have also studied the use of coordination avoidance in large-scale statistical and “Big Data” analytics in asynchronous distributed model training [10] and in building low-latency data product prediction pipelines [18]. Overall, this research demonstrates both the power and the limitations of coordination-free data management systems.
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer

Methods of deep machine learning enable to to reuse low-level representations efficiently for generating more abstract high-level representations. Originally, deep learning has been applied passively (e.g., for classification purposes). Recently, it has been extended to estimate the value of actions for autonomous agents within the framework of reinforcement learning (RL). Explicit models of the environment can be learned to augment such a value function. Although "flat" connectionist methods have already been used for model-based RL, up to now, only model-free variants of RL have been equipped with methods from deep learning. We propose a variant of deep model-based RL that enables an agent to learn arbitrarily abstract hierarchical representations of its environment. In this paper, we present research on how such hierarchical representations can be grounded in sensorimotor interaction between an agent and its environment.
Synthesis and In Vitro Evaluation of N-(Bromobut-3-en-2-yl)-7-methoxy-1,2,3,4-tetrahydroacridin-9-amine as a Cholinesterase Inhibitor with Regard to Alzheimer's Disease Treatment

A new tacrine based cholinesterase inhibitor, N-(bromobut-3-en-2-yl)-7-methoxy-1,2,3,4-tetrahydroacridin-9-amine (1), was designed and synthesized to interact with specific regions of human acetylcholinesterase and human butyrylcholinesterase. Its inhibitory ability towards cholinesterases was determined and compared to tacrine (THA) and 9-amino-7-methoxy-1,2,3,4-tetrahydroacridine (7-MEOTA). The assessment of IC50 values revealed 1 as a weak inhibitor of both tested enzymes.
The unnaturalness of natural burials: dispossessing the dispossessed

ABSTRACT The rise of natural burials has not been without controversy. Traditionalist funeralists and a number of mourners struggle to reconcile new immaterial, anti-symbolic practices with those of old. Drawing from an extensive ethnographic study of German cemeteries of both traditional and natural denomination, and by employing a spatial theory approach, we consider the impact that the rise of natural burials has had on all parties in the funeral industry. In particular, we find that those who initially profess a keenness to mark the death of a loved one according to the new conventions of natural burials frequently become disillusioned with their choice. They are unwilling to fully embrace novel mourning practices which eradicate the material symbols that memorialise the deceased. In effect, natural burials dispossess the already dispossessed.
Young Children’s Theory of Mind: Home Literacy Environment, Technology Usage, and Preschool Education

Theory of mind (ToM) skills involve young children’s mentalizing ability to be aware of their own selves and other individuals’ thoughts, beliefs, desires, and intentions (mental states). The social cognition skills are essential for processing complex social relations and overcoming interpersonal difficulties in communication. Previous studies shed light on the relationship of parenting and demographics to children’s ToM skills, but do not examine the associations with both home environment and preschool education experiences. The goal of the present study is to investigate children’s preschool education experience, home literacy environment (HLE), and technology usage in relation to their ToM skills. Participants were 203 preschoolers and their parents. The study data were collected using a home literacy environment questionnaire (HLEQ), theory of mind scale, and a demographic information form. The findings revealed that (a) children’s ToM scores are not differentiated regarding gender, excluding the diverse belief tasks, b) children’s ToM performances were differentiated in favor of children who have internet access at home, c) HLE, child age, daily TV watching, household income, maternal education, preschool experience, and shared book reading explained 46% of the total variance of preschoolers’ ToM scores.
Incorporating Concerns for Diversity and Social Justice in Admissions Decisions.

An increasingly important element in the mission of the teacher education program at New Jersey's Montclair State University is preparing candidates for educating students to become engaged citizens who will work actively toward improving social and political democracy and be comfortable teaching diverse students. It is expected that effective teachers will understand the moral dimensions of teaching, be committed to enculturating students into a social and political democracy, think critically, and possess pedagogical skills and content knowledge to provide their students with access to knowledge. The school's admissions program was reconceptualized to seek and select students with predispositions congruent with program beliefs. The first step was creating the Portrait of a Teacher, which presents characteristics of teachers the school hopes to develop. Next, an admission evaluation scale was designed using criteria related directly to ideals depicted in the Portrait. The school believes that rather than being subjective and lowering standards, it is questioning current standards and admissions processes. The paper describes admission into the school's music education program to illustrate the interview process, using a composite profile of students who have been interviewed in recent years. It explains that this admissions process is a selecting-in versus screening-out process. (Contains 11 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
Local Decorrelation For Improved Detection

Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topology of the data. Given highly correlated data, decision trees with oblique (multiple feature) splits can be effective. Use of oblique splits, however, comes at considerable computational expense. Inspired by recent work on discriminative decorrelation of HOG features, we instead propose an efficient feature transform that removes correlations in local neighborhoods. The result is an overcomplete but locally decorrelated representation ideally suited for use with orthogonal decision trees. In fact, orthogonal trees with our locally decorrelated features outperform oblique trees trained over the original features at a fraction of the computational cost. The overall improvement in accuracy is dramatic: on the Caltech Pedestrian Dataset, we reduce false positives nearly tenfold over the previous state-of-the-art.
An analysis and comparison for popular video encryption algorithms

The security of video in the communication field become the major concern, especially after the rapid development of multimedia technology (internet and mobile devices). Since the using of multimedia data transmission become more and more due to the wide internet using all around the world, the video protection techniques, is becoming necessary to keep that information not accessible by irrelevant public or malicious attackers. The researchers have designed different type of encryption algorithms to secure the multimedia data, that algorithms have their strength and weakness points. In this paper, we will focus on introducing and comparison between the three popular encryption algorithms, DES, RSA and AES, as well to choose which encryption algorithm can be used to exchange video safely, and maintain the balancing between the security and computational time.
Dynamic regulation of index implementation for flash memory storages

Hard disk is gradually replaced by flash memory for its mechanical constraints. And with increase of capacity of flash memory, a large-scale database is able to run it. But the frequent changes of index structure can degrade the performance of flash memory and reduction of service life. Most previous works considered the characteristics of flash memory and improved the performance to some extent. In this paper, we first analyze two representative mechanisms which are suitable to write-oriented and read-oriented workload, respectively. And we pointed out the drawbacks of them. Then we propose a dynamic regulation algorithm for utilizing the superiority of both two mechanisms. With simulation, we can see that the proposed methodology could significantly enhance the efficiency of using index on flash memory storages.
Eurographics/ Ieee-vgtc Symposium on Visualization 2010 Supporting Exploratory Analysis with the Select & Slice Table

In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select & Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select & Slice table during exploratory analysis of multivariate data.
$q$-Difference raising operators for Macdonald polynomials and the integrality of transition coefficients

We study certain $q$-difference raising operators for Macdonald polynomials (of type $A_{n-1}$) which are originated from the $q$-difference-reflection operators introduced in our previous paper. These operators can be regarded as a $q$-difference version of the raising operators for Jack polynomials introduced by L.Lapointe and L.Vinet. As an application of our $q$-difference raising operators we give an elementary proof of the integrality of the double Kostka coefficients which had been conjectured I.G. Macdonald. We also determine their quasi-classical limits, which give rise to (differental) raising operators for Jack polynomials.
The flow of narrative in the mind unmoored: An account of narrative processing

ABSTRACT Verbal narratives provide incomplete information and can be very long, yet readers and hearers often effortlessly fill in the gaps and make connections across long stretches of text, sometimes even finding this immersive. How is this done? In the last few decades, event-indexing situation modeling and complementary accounts of narrative emotion have suggested answers. Despite this progress, comparisons between real-life perception and narrative experience might underplay the way narrative processing modifies our world model, as well as the role of the emotions that do not relate to characters. I reframe narrative experience in predictive processing and neural networks, capturing continuity between fiction, perception, and states like dreaming and imagination, enabled by the flexible instantiation of concepts. In this framework, narrative experience is more clearly revealed as a creative experience that can share some of the phenomenology of dreams.
Preliminary Version Do Online Social Networks Increase Welfare ?

We consider a setting where agents select among competing products of unknown quality by costly sequential search. The agents are active on an online social network, and late movers benefit from possibly being able to see early movers’ purchase decisions. In this setting we consider the impact of advertising by firms, and the incentives of the underlying social network platform. We consider display advertising, which is standard firm to consumer advertising, and social advertising, in which agents who purchased that firm’s product are highlighted to their friends. We show that in equilibrium, the heterogeneous firms spend the same amount on advertising. Social advertising is more lucrative than standard banner advertising. However, both forms of advertising have no effect on consumer welfare, and are instead a transfer from the firms to the social network. A social network motivated by advertising revenues may limit the amount of information agents see about actions by other agents, since this will increase advertising revenue. This reduces consumer welfare relative to the first best, since early movers’ purchases are informative about relative quality.
AN OBJECT-ORIENTED APPROACH FOR HIERARCHICAL STATE MACHINES

Finite State Machines have been widely used as a tool for developing video games, especially as pertains to solving problems related to AI, input handling, and game progression. In this paper, we introduce a practical technique that can highly improve the productivity of development of Finite State Machines in video games. This technique consists of adapting concepts of OOP for use with Hierarchical Finite State Machines in an entirely visual system. An open source implementation of the technique that can be used as middleware in most games is also provided
Automatic Terminology Intelligibility Estimation for Readership-oriented Technical Writing

This paper describes automatic terminology intelligibility estimation for readership-oriented technical writing. We assume that the term frequency weighted by the types of documents can be an indicator of the term intelligibility for a certain readership. From this standpoint, we analyzed the relationship between the following: average intelligibility levels of 46 technical terms that were rated by about 120 laymen; numbers of documents that an Internet search
Budget de?cits and redistributive politics

This paper proposes a new view of the forces in the political process that cause governments to accumulate debt. The analysis builds on a model of redistributive politics that, contrary to median voter models, does not restrict the set of policies that politicians can propose. I show that deficits occur even in an environment where voters (and periods) are homogeneous. This is an environment where previous political theories of debt would predict budget balance. In the model deficits are a way for candidates to better target promises to voters and are therefore used as tools of redistributive politics. The main contribution of the analysis is to show that the same forces that push candidates to redistribute resources across voters to pursue political advantage are forces that generate budget deficits.
Image Retrieve Based on Primary Clusters

In this paper, a new image retrieval method, which uses the two primary features of image cluster to retrieve image, i.e., the average color value and the number of pixels for each cluster, is proposed. In comparison with popular methods, since this method uses both color feature and structure feature, it is good to solve such a sort of image retrieval questions for two images that they are similar but not identical color. Experimental results show that this method not only enhances the flexibility of system retrieval, but also achieves prominent effects for image retrieval.