17,886 research outputs found
Quality-aware model-driven service engineering
Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects
ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box
character of services
Zipf's law, 1/f noise, and fractal hierarchy
Fractals, 1/f noise, Zipf's law, and the occurrence of large catastrophic
events are typical ubiquitous general empirical observations across the
individual sciences which cannot be understood within the set of references
developed within the specific scientific domains. All these observations are
associated with scaling laws and have caused a broad research interest in the
scientific circle. However, the inherent relationships between these scaling
phenomena are still pending questions remaining to be researched. In this
paper, theoretical derivation and mathematical experiments are employed to
reveal the analogy between fractal patterns, 1/f noise, and the Zipf
distribution. First, the multifractal process follows the generalized Zipf's
law empirically. Second, a 1/f spectrum is identical in mathematical form to
Zipf's law. Third, both 1/f spectra and Zipf's law can be converted into a
self-similar hierarchy. Fourth, fractals, 1/f spectra, Zipf's law, and the
occurrence of large catastrophic events can be described with similar
exponential laws and power laws. The self-similar hierarchy is a more general
framework or structure which can be used to encompass or unify different
scaling phenomena and rules in both physical and social systems such as cities,
rivers, earthquakes, fractals, 1/f noise, and rank-size distributions. The
mathematical laws on the hierarchical structure can provide us with a holistic
perspective of looking at complexity such as self-organized criticality (SOC).Comment: 20 pages, 9 figures, 3 table
Complex Actions for Event Processing
Automatic reactions triggered by complex events have been
deployed with great success in particular domains, among
others, in algorithmic trading, the automatic reaction to realtime
analysis of marked data. However, to date, reactions
in complex event processing systems are often still limited
to mere modifications of internal databases or are realized
by means similar to remote procedure calls.
In this paper, we argue that expressive complex actions
with support for composite work
ows and integration of
so called external actions are desirable for a wide range
of real-world applications among other emergency management.
This article investigates the particularities of external
actions needed in emergency management, which are initiated
inside the event processing system but which are actually
executed by external actuators, and discuss the implications
of these particularities on composite actions. Based
on these observations, we propose versatile complex actions
with temporal dependencies and a seamless integration of
complex events and external actions. This article also investigates
how the proposed integrated approach towards
complex events and complex actions can be evaluated based
on simple reactive rules. Finally, it is shown how complex actions
can be deployed for a complex event processing system
devoted to emergency management
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Increasing evidence suggests that a growing amount of social media content is
generated by autonomous entities known as social bots. In this work we present
a framework to detect such entities on Twitter. We leverage more than a
thousand features extracted from public data and meta-data about users:
friends, tweet content and sentiment, network patterns, and activity time
series. We benchmark the classification framework by using a publicly available
dataset of Twitter bots. This training data is enriched by a manually annotated
collection of active Twitter users that include both humans and bots of varying
sophistication. Our models yield high accuracy and agreement with each other
and can detect bots of different nature. Our estimates suggest that between 9%
and 15% of active Twitter accounts are bots. Characterizing ties among
accounts, we observe that simple bots tend to interact with bots that exhibit
more human-like behaviors. Analysis of content flows reveals retweet and
mention strategies adopted by bots to interact with different target groups.
Using clustering analysis, we characterize several subclasses of accounts,
including spammers, self promoters, and accounts that post content from
connected applications.Comment: Accepted paper for ICWSM'17, 10 pages, 8 figures, 1 tabl
Building complex events: the case of Sicilian Doubly Inflected Construction
We examine the Doubly Inflected Construction of Sicilian (DIC; Cardinaletti and Giusti 2001, 2003, Cruschina 2013), in which a motion verb V1 from a restricted set is followed by an event verb V2 and both verbs are inflected for the same person and tense features. The interpretation of DIC involves a complex event which behaves as a single, integrated event by linguistic tests. Based on data drawn from different sources, we argue that DIC is an asymmetrical serial verb construction (Aikhenvald 2006). We propose an analysis of DIC in which V1 and V2 enter the semantic composition as lexical verbs, with V1 contributing a motion event and projecting a theme and a goal argument which are identified, respectively, with an agent and a location argument projected by V2. A morphosyntactic mechanism of feature-spread requires that the person and tense features be realized both on V1 and on V2, while, semantically, these features are interpreted only once, in a position from which they take scope over the complex predicate resulting from the combination of V1 and V2. The semantic analysis is based on an operation of event concatenation, defined over spatio-temporally contiguous events which share specific participants, and is implemented in a neo-Davidsonian framework (Parsons 1990)
Characterization and Classification of Collaborative Tools
Traditionally, collaboration has been a means for organizations to do their work. However, the context in which they do this work is changing, especially in regards to where the work is done, how the work is organized, who does the work, and with this the characteristics of collaboration. Software development is no exception; it is itself a collaborative effort that is likewise affected by these changes. In the context of both open source software development projects and communities and organizations that develop corporate products, more and more developers need to communicate and liaise with colleagues in geographically distant places about the software product they are conceiving, designing, building, testing, debugging, deploying and maintaining. Thus, work teams face sizeable collaborative challenges, for which they have need of tools that they can use to communicate and coordinate their Work efficiently
On the mechanism of response latencies in auditory nerve fibers
Despite the structural differences of the middle and inner ears, the latency pattern in auditory nerve fibers to an identical sound has been found similar across numerous species. Studies have shown the similarity in remarkable species with distinct cochleae or even without a basilar membrane. This stimulus-, neuron-, and species- independent similarity of latency cannot be simply explained by the concept of cochlear traveling waves that is generally accepted as the main cause of the neural latency pattern.
An original concept of Fourier pattern is defined, intended to characterize a feature of temporal processing—specifically phase encoding—that is not readily apparent in more conventional analyses. The pattern is created by marking the first amplitude maximum for each sinusoid component of the stimulus, to encode phase information. The hypothesis is that the hearing organ serves as a running analyzer whose output reflects synchronization of auditory neural activity consistent with the Fourier pattern.
A combined research of experimental, correlational and meta-analysis approaches is used to test the hypothesis. Manipulations included phase encoding and stimuli to test their effects on the predicted latency pattern. Animal studies in the literature using the same stimulus were then compared to determine the degree of relationship.
The results show that each marking accounts for a large percentage of a corresponding peak latency in the peristimulus-time histogram. For each of the stimuli considered, the latency predicted by the Fourier pattern is highly correlated with the observed latency in the auditory nerve fiber of representative species.
The results suggest that the hearing organ analyzes not only amplitude spectrum but also phase information in Fourier analysis, to distribute the specific spikes among auditory nerve fibers and within a single unit.
This phase-encoding mechanism in Fourier analysis is proposed to be the common mechanism that, in the face of species differences in peripheral auditory hardware, accounts for the considerable similarities across species in their latency-by-frequency functions, in turn assuring optimal phase encoding across species. Also, the mechanism has the potential to improve phase encoding of cochlear implants
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
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