Miracle-dz/UniFAR-FTUs huggingface dataset:
Service-based Processes: Design for business and technology

The concepts of processes and services can be used to structure both businesses and software systems. From a business perspective, the use of processes promises efficient management of organizations. From a software perspective, executable process descriptions provide a way to structure software systems according to the business process the systems should support. Furthermore, the concept of software services allows systems to be partitioned in a modular fashion, thereby enabling large-scale system integration on a technical level. By combining process descriptions with software services in service-based processes, it is feasible to address both business and more technical software needs. Aligning the business and software perspectives of processes and software services is, however, challenging, since implemented executable processes and software services need to cater for operational and strategic business needs as well as existing legacy software systems. In this thesis, several instruments are presented that aim at aiding the design and evolution of service-based processes. The aligning of operational business and software perspectives on process descriptions is addressed by the introduction of business and technical process model levels. These levels can be aligned by the use of a set of model transformations and associated transformation rules. Furthermore, the aligning is aided by introducing means for achieving flexibility on both the business and technical model levels. The development of service-based processes from a strategic business point of view is aided by the introduction of novel models and methods for goal-based design of software services. Taken together, the instruments can be used for the design and evolution of service-based processes.
International Workshop on Performance Evaluation of Tracking and Surveillance ( PETS ’ 2002 )

In earlier work we introduced a generic, modular tracker architecture that combines the advantages of several simple and rapidly performing tracking algorithms. The adaptive choice of critical system parameters such as processing regions and resolution results in robustness to varying frame rates and computational constraints. In this paper, we describe the embedding of our tracker into a distributed infrastructure for visual surveillance applications via an event-based mechanism. The tracker generates applicationindependent events on the basis of generic incidents and target interactions detected in the video stream. These events can then be received and interpreted by applicationspecific clients. We report experimental results on the shopwindow datasets of PETS 2002.
An intelligent real time 3D vision system for robotic welding tasks

MARWIN is a top-level robot control system that has been designed for automatic robot welding tasks. It extracts welding parameters and calculates robot trajectories directly from CAD models which are then verified by real-time 3D scanning and registration. MARWIN's 3D computer vision provides a user-centred robot environment in which a task is specified by the user by simply confirming and/or adjusting suggested parameters and welding sequences. The focus of this paper is on describing a mathematical formulation for fast 3D reconstruction using structured light together with the mechanical design and testing of the 3D vision system and show how such technologies can be exploited in robot welding tasks.
Dimension Reduction and Visualization for Single-copy Alignments via Generalized PCA.

Principal components analysis (PCA) has been one of the most widely used exploration tools in genomic data analysis since its introduction in 1978 (Menozzi et al. 1978). PCA allows similarities between individuals to be efﬁciently calculated and visualized, optimally in two dimensions. While PCA is well suited to analyses concerned with autosomal DNA, no analogue for PCA exists for the analysis and visualization of non-autosomal DNA. In this paper we introduce a statistically valid method for the analysis of single-copy sequence data. We then show that tests for relationships between genetic information and qualitative and quantitative characteristics can be implemented in a rigorous statistical framework. We motivate the use of our method with examples from empirical data.
An innovative use of orthophotos – possibilities to assess plant productivity from colour infrared aerial orthophotos

Studies of ecological processes should focus on a relevant spatial scale, as crude spatial resolution will fail to detect small scale variation which is of potentially critical importance. Remote sensing methods based on multispectral satellite images are used to assess primary productivity and aerial photos to map vegetation structure. Both methods are based on the principle that photosynthetically active vegetation has a characteristic spectral signature. Yet they are applied differently due to technical differences. Satellite images are suitable for calculations of vegetation indices, for example Normalized Difference Vegetation Index (NDVI). Colour infrared aerial photography was developed for visual interpretation and never regarded for calculation of indices since the spectrum recorded and post processing differ from satellite images. With digital cameras and improved techniques for generating colour infrared orthophotos, the implications of these differences are uncertain and should be explored. We tested if plant productivity can be assessed using colour infrared aerial orthophotos (0.5 m resolution) by applying the standard NDVI equation. With 112 vegetation samples as ground truth, we evaluated an index that we denote rel‐NDVIortho in two areas of the Fennoscandian mountain tundra. We compared the results with conventional SPOT5 satellite‐based NDVI (10 m resolution). rel‐NDVIortho was related to plant productivity (Northern area: P = <0.001, R2 = 0.73; Southern area: P = <0.001, R2 = 0.39), performed similar to SPOT5 satellite NDVI (Northern area: P = <0.001, R2 = 0.76; Southern area: P = <0.001, R2 = 0.40) and the two methods were highly correlated (cor = 0.95 and cor = 0.84). Despite different plant composition, the results were consistent between areas. Our results suggest that vegetation indices based on colour infrared aerial orthophotos can be a valuable tool in the remote sensing toolbox, offering a high‐spatial resolution proxy for plant productivity with less signal degradation due to atmospheric interference and clouds, compared to satellite images. Further research should aim to investigate if the method is applicable to other ecosystems.
The Journal of Systems and Software

During the whole life-cycle of software-intensive systems in safety-critical domains, system models must consistently co-evolve with quality evaluation models like fault trees. However, performing these co-evolution steps is a cumbersome and often manual task. To understand this problem in detail, we have analyzed the evolution and mined common changes of architecture and fault tree models for a set of evolution scenarios of a part of a factory automation system called Pick and Place Unit. On the other hand, we designed a set of intra-and inter-model transformation rules which fully cover the evolution scenarios of the case study and which offer the potential to semi-automate the co-evolution process. In particular, we validated these rules with respect to completeness and evaluated them by a comparison to typical visual editor operations. Our results show a signiﬁcant reduction of the amount of required user interactions in order to realize the co-evolution.
Structural Equation Modeling

Structural equation modeling (SEM) grows out of and serves purposes similar to multiple regression, but in a more powerful way which takes into account the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents each measured by multiple indicators, and one or more latent dependents also each with multiple indicators. SEM may be used as a more powerful alternative to multiple regression, path analysis, factor analysis, time series analysis, and analysis of covariance. That is, these procedures may be seen as special cases of SEM, or, to put it another way, SEM is an extension of the general linear model (GLM) of which multiple regression is a part.
TPC server fault tolerance using connection migration to a backup server

This paper describes the design, implementation, and performance evaluation of ST-TCP (Server fault-Tolerant TCP), which is an extension of TCP to tolerate TCP server failures. This is done by using an active backup server that keeps track of the state of the TCP connection and takes over the TCP connection whenever the primary fails. This migration of the TCP connection to the backup is completely transparent to the client. Because no changes are required on the client machine, any TCP client can access a ST-TCP server. The performance overhead of ST-TCP over standard TCP is minimal, and during normal operation its behavior is the same as that of a regular TCP. In addition, ST-TCP provides a fast and seamless failover whenever the primary server fails. This is verified by a prototype implementation of ST-TCP in the Linux operating system, and experiments with a number of simulated applications which have different communication characteristics.
Eecient Similarity Search in Sequence Databases

We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the rst few frequencies are strong. Another important observation is Parseval's theorem, which speci es that the Fourier transform preserves the Euclidean distance in the time or frequency domain. Having thus mapped sequences to a lower-dimensionality space by using only the rst few Fourier coe cients, we use R -trees to index the sequences and e ciently answer similarity queries. We provide experimental results which show that our method is superior to search based on sequential scanning. Our experiments show that a few coe cients (1-3) are adequate to provide good performance. The performance gain of our method increases with the number and length of sequences.
Purdue e-Pubs Purdue e-Pubs

: It is shown that the Lyndon decomposition of a word of n symbols can be computed by an n~processors CReW PRAM in O(log n) time. Extensions of the basic algorithm convey, within the same time and processors bOWlds, efficient parallel solutions to problems such as finding the lexicographically minimum or maximum suffix for aU prefixes of the input string, and finding the lexicographically least rotation of all prefixes of the input
First Tertiary molossid (Microchiroptera: Molossidae) from Australia: its phylogenetic and biogeographic implications

Petramops creaseri n.gen., n.sp. is described from Middle Miocene freshwater limestones on Riversleigh Station in northwestern Queensland. One of 25 new bat species identified among fossil remains recovered from the Riversleigh deposits, it is Australia's first Tertiary molossid. Its affinities appear to lie outside the modern Australian molossid radiation. It seems likely that bats of the Petramops lineage were proficient long-distance fliers which colonized Australia before the Miocene. Subsequent or coincident colonizations of Australia by molossids would have involved species of Nyclinomus, Chaerephon and Mormopterus. A Chiroptera, Molossidae, Riversleigh, Miocene, Petramops creasei, biogeography.
Nucleotide sequence composition adjacent to intronic splice sites improves splicing efficiency via its effect on pre-mRNA local folding in fungi

RNA splicing is the central process of intron removal in eukaryotes known to regulate various cellular functions such as growth, development, and response to external signals. The canonical sequences indicating the splicing sites needed for intronic boundary recognition are well known. However, the roles and evolution of the local folding of intronic and exonic sequence features adjacent to splice sites has yet to be thoroughly studied. Here, focusing on four fungi (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Aspergillus nidulans, and Candida albicans), we performed for the first time a comprehensive high-resolution study aimed at characterizing the encoding of intronic splicing efficiency in pre-mRNA transcripts and its effect on intron evolution. Our analysis supports the conjecture that pre-mRNA local folding strength at intronic boundaries is under selective pressure, as it significantly affects splicing efficiency. Specifically, we show that in the immediate region of 12–30 nucleotides (nt) surrounding the intronic donor site there is a preference for weak pre-mRNA folding; similarly, in the region of 15–33 nt surrounding the acceptor and branch sites there is a preference for weak pre-mRNA folding. We also show that in most cases there is a preference for strong pre-mRNA folding further away from intronic splice sites. In addition, we demonstrate that these signals are not associated with gene-specific functions, and they correlate with splicing efficiency measurements (r = 0.77, P = 2.98 × 10−21) and with expression levels of the corresponding genes (P = 1.24 × 10−19). We suggest that pre-mRNA folding strength in the above-mentioned regions has a direct effect on splicing efficiency by improving the recognition of intronic boundaries. These new discoveries are contributory steps toward a broader understanding of splicing regulation and intronic/transcript evolution.
Probabilistic Dynamic Hard Real-Time Scheduling in HPC

Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes, enabling novel application such as flexible manufacturing of highly customized products. Real-time control of these processes, however, has not yet realized its full potential in using the data collected to drive further development. We believe that modern virtualization techniques, specifically application containers, present a unique opportunity to decouple control functionality from associated plants and fully realize the potential for highly distributed and transferable industrial processes even with real-time constraints arising from time-critical sub-processes. In this paper, we explore the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or (edge) cloud computing platforms using off-the-shelf technology. We present a specifically developed orchestration tool that can manage the execution of containerized applications on shared resources without compromising hard real-time execution determinism.
Collaborative Context Based Reasoning

A perspective towards modeling teamwork is presented. This perspective extends the Context Based Reasoning (CxBR) paradigm. The new paradigm is coined Collaborative Context Based Reasoning (CCxBR). CxBR corollaries are specified. Joint Intention Theory and Belief-Desire-Intention models are reviewed. CCxBR utilizes Joint Intention Theory as the justification for the theorems that are presented in this paper.
Scaling Up to the Real

Editor’s Summary: This chapter provides an overview of model-based adaptive critic designs, including background, general algorithms, implementations, and comparisons. The authors begin by introducing the background mathematics involved in model-reference adaptive critic designs. Various ADP designs such as Heuristic Dynamic Programming (HDP), Dual HDP (DHP), Globalized DHP (GDHP), and Action-Dependent (AD) designs are examined from both a mathematical and implementation standpoint and put into perspective. Pseudocode is provided for many aspects of the algorithms. The chapter concludes with applications and examples. For another overview perspective that focuses more on implementation issues read Chapter ? : Guidance in the Use of Adaptive Critics for Control. Chapter contains a comparison of DHP with back-propagation through time, building a common framework for comparing these methods.
State Abstractions

A common aspect of methods surveyed in the previous lectures is that the size of dataset (or sample size) required to yield learning guarantees is polynomial in the size of the state space. When the size of the state space is very large, as will be the case in many challenging problems, the agent needs to generalize what is learned about one state to other states using prior knowledge. One of the easiest-to-deploy generalization schemes is state abstraction (sometimes also called state aggregation/compression). A state abstraction is a mapping φ that maps the original (or primitive/raw) state space S to some finite abstract state space; for brevity we use φ(S) to denote the codomain of the mapping. Intuitively, if s and s are mapped to the same element, that is φ(s) = φ(s), they are treated as the same state. Given a problem with state space S and an abstraction φ, a typical usage of φ is to convert every state s in the dataset D into φ(s), and run any tabular algorithm over D with the understanding that the state space is φ(S). For example, if we collect a dataset that consists of tuples (s, a, r, s′), we can view each tuple now as (φ(s), a, r, φ(s′)), and build a certainty-equivalence model over state space φ(S). This is always doable, although there might not be a well-defined MDP with state space φ(S) that is the groundtruth process for the converted dataset. An obvious benefit of using state abstraction is the increase of effective sample size. Suppose we collected a dataset with n samples per (s, a) pair (protocol (a)), and an abstraction φ maps s and s to the same abstract state x. Then, after applying the abstraction, we get 2n samples for the stateaction pairs (x, a). In certainty-equivalence, we essentially double the sample size for estimating the transition and reward functions for a state-action pair and can enjoy lower estimation errors. This advantage, of course, comes with a caveat, otherwise we could simply map every s ∈ S to the same abstract state and maximize the number of samples per state. The caveat is that if we aggregate states that are very different from each other, then we do not converge to the optimal policy even in the limit of infinite data; in other words, we may incur high approximation errors. The trade-off between approximation error and estimation error is a constant theme of statistical machine learning [1]. Intuitively, the approximation error is high when we aggregate states that are very different from each other. The question is, how should we define an (approximate) equivalence notion among states? Whether they share the same optimal action? Whether they share the same Q values? Whether they yield the same rewards and next-state distributions? It turns out that, these criteria define a hierarchy of different state abstractions, from lenient to strict. Lenient notions of abstractions yield more generalization benefits, but may not work well with certain algorithms; strict notions of abstractions preserve many properties of the original MDP and hence work with a wider range of algorithms, but the generalization benefits are relatively limited.
Capacity and Delay Analysis for Ad Hoc Networks

Capacity and delay are tightly related entities in ad hoc and sensor networks. In the past few years, there has been considerable amount of work describing how these entities behave given a set of assumptions. It was shown that the improvement of one is obtained at the expense of the other; that is, there is a trade-off between them. On the other hand, there are other important parameters such as mobility and physical layer properties (e.g., the use of directional antennas) that can change the way these two entities are associated to each other. Furthermore, diversity routing strategies like multi-copy packet forwarding has proven to enhance delay without changing the capacity order of such networks. On this chapter, we present a summary of main results on the trade-off between capacity and delay for ad hoc and sensor networks. First, we review the basic concepts employed throughout the literature, followed by the fundamental results for capacity and delay in ad hoc networks. Then, we show how mobility can be exchanged with capacity and delay, as well as how the physical layer features change such trade-off. Finally, we describe how a multi-copy packet relaying strategy provides an exponential delay reduction in mobile ad hoc networks (MANETs) compared with the traditional single-copy relaying scheme without changing the capacity order of such networks.
WhoLoDancE: Whole-body Interaction Learning for Dance Education

Dance resides among the most ancestral forms of art, representing a major asset of the human intangible cultural heritage playing, at the same time, a primary role in contemporary artistic creation. WhoLoDancE, a Research and Innovation Action funded under the European Union’s Horizon 2020 research and innovation programme, aimed at the double goal of preserving its inheritance and integrating digital technologies into contemporary dance learning, teaching and choreography through the digitalisation of dance movements with motion capture techniques, the creation of a large motion repository including movements from ballet, contemporary, flamenco and traditional Greek folk dances and the implementation of breakthrough applications ranging from movement quality annotation and segmentation, similarity search, movement blending, multimodal and virtual reality-based experiences for self-reflection and experimentation. In this paper, we present the prototype tools and state-of-the-art results of the project development in its conclusive phase, highlighting the added value this interdisciplinary approach could possibly bring to dance learning and practice, the main technical, practical and cultural challenges encountered in this path and open issues to be addressed in the months to come, providing hints on future research directions.
AxleDB: A Novel Programmable Query Processing Platform on FPGA

With the rise of Big Data, providing high-performance query processing capabilities through the acceleration of the database analytic has gained signiﬁcant attention. Leveraging Field Programmable Gate Array (FPGA) technology, this approach can lead to clear beneﬁts. In this work, we present the design and implementation of AxleDB: An FPGA-based platform that enables fast query processing for database systems by melding novel database-speciﬁc accelerators with commercial-off-the-shelf (COTS) storage using modern interfaces, in a novel, uniﬁed, and a programmable environment. AxleDB can perform a large subset of SQL queries through its set of instructions that can map compute-intensive database operations, such as ﬁlter, arithmetic, aggregate, group by, table join, or sort, on to the specialized high-throughput accelerators. To minimize the amount of SSD I/O operations required, AxleDB also supports hardware MinMax indexing for databases. We evaluated AxleDB with ﬁve decision support queries from the TPC-H benchmark suite and achieved a speedup from 1.8X to 34.2X and energy efﬁciency from 2.8X to 62.1X, in comparison to the state-of-the-art DBMS, i.e., PostgreSQL and MonetDB.