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It is simple to query a relational database because all columns of the tables are known and the language SQL is easily applicable. In NoSQL, there usually is no fixed schema and no query language. In this article, we present NotaQL, a data-transformation language for wide-column stores. NotaQL is easy to use and powerful. Many MapReduce algorithms like filtering, grouping, aggregation and even breadth-first-search, PageRank and other graph and text algorithms can be expressed in two or three short lines of code.
Analysen auf NoSQL-Datenbanken sind oft langdauernd und die Ergebnisse fur den Benutzer haufig schwer verst ̈andlich. Wir prasentieren eine Möglichkeit, Datenmengen aus Wide-Column Stores mittels der Transformationssprache NotaQL zu transformieren sowie zu aggregieren und die Ergebnisse in Form von Diagrammen dem Benutzer darzustellen. Dabei kommen Sampling-Techniken zum Einsatz, um die Berechnung auf Kosten der Genauigkeit zu beschleunigen. Das von uns verwendete iterative Samplingverfahren sorgt fur eine kontinuierliche Verbesserung der Berechnungsgenauigkeit und bietet zudem Möglichkeiten zur Genauigkeitsabschätzung, die in Form von Konfidenzintervallen in den Diagrammen dargestellt werden kann.
Wir pr asentieren SQL Island, ein neuartiges browserbasiertes Lernspiel,welches auf dem Konzept der Text-Adventures basiert. Nach einem Flugzeugabsturzlandet die Spielfigur auf einer Insel. Man redet mit Bewohnern, sammelt Gegenst ̈andeund k ̈apft gegen B ̈osewichte. Die Besonderheit bei diesem Spiel ist jedoch, dass derSpieler seine Figur lediglich mittels SQL-Befehlen steuert. Alle n ̈otigen Befehle wer-den zun ̈achst pr ̈asentiert, sodass keine Vorerfahrung notwendig ist. Nach etwa einerStunde Spielzeit beherrscht der Spieler SELECT-, UPDATE- und DELETE-Anfragensowie Gruppierungen, Aggregationsfunktionen und Joins. Das Spiel kann online aufhttp://www.sql-island.de kostenlos und ohne Registrierung gespielt werden.
Robots should appropriately give reasons for their actions
when these actions affect a human’s action or goal space. Communicating reasons may help the human understand the robot’s intents and may initiate joint action, i. e., accepting the robot’s goals and cooperating on the robot’s actions. However, to be efficient, the communication of reasons should be limited to the necessary rather than to completeness, conforming to the Gricean Maxim of Quantity. Furthermore, what is necessary only becomes apparent as the situation evolves and hence, for seamless interaction, ongoing utterances must be adapted as they happen. We present a system that flexibly gives reasons in a reduced setting in which the robot needs to intrude a human’s personal space in order to reach its goal.
We propose to use a model of personal space to initiate communication while passing a human thereby acknowledging that humans are not just a special kind of obstacle to be avoided but potential interaction partners. As a simple form of interaction, our system communicates an apology while closely passing a human. To this end, we present a software architecture that integrates a social-spaces knowledge base and a component for incremental speech production. Incrementality ensures that the robot’s utterance can be adapted to fit the developing situation in a natural way. Observer ratings show that personal-space intrusion is perceived as both natural and polite if the robot has the capability to utter and adapt an apology in an incremental way whereas it is perceived as unfriendly if the robot intrudes personal space without saying anything. Moreover, the robot is perceived as less natural if it does not adapt.
Incremental speech synthesis aims at delivering the synthetic voice while the sentence is still being typed. One of the main challenges is the online estimation of the target prosody from a partial knowledge of the sentence's syntactic structure. In the context of HMM-based speech synthesis, this typically results in missing segmental and suprasegmental features, which describe the linguistic context of each phoneme. This study describes a voice training procedure which integrates explicitly a potential uncertainty on some contextual features. The proposed technique is compared to a baseline approach (previously published), which consists in substituting a missing contextual feature by a default value calculated on the training set. Both techniques were implemented in a HMM-based Text-To-Speech system for French, and compared using objective and perceptual measurements. Experimental results show that the proposed strategy outperforms the baseline technique for this language.
A method, system, and computer-usable non-transitory storage device for dynamic voice codec adaptation are disclosed. The voice codec adapts in real time to devote more bits to audio quality when it is most needed, and fewer bits to less important parts of utterances are disclosed. Dialog knowledge is utilized for compression opportunities to adjust the bitrate moment-by-moment, based on the inferred value of each frame. Frame importance and appropriate transmission fidelity is predicted based on prosodic features and models of dialog dynamics. This technique provides the same communications quality with less spectrum needs, fewer antennas, and less battery drain.
We demonstrate nonlinear coupling in a discrete optical system. This is achieved in waveguide arrays with quadratic nonlinearity, where the symmetries of the nonlinearly interacting waveguide modes are used to suppress the usually dominating nonlinear effects within individual waveguides. We derive a mathematical model to describe the nonlinear coupling in such waveguide arrays and show experimentally the profound effects of this nonlinear coupling mechanism on second-harmonic generation.
We propose and experimentally demonstrate an all-optically tunable biphoton quantum light source using a nonlinear directional coupler. The source can generate high-fidelity N00N states, completely split states, and states with variable degrees of entanglement.
Fluctuations of hi-hat timing and dynamics in a virtuoso drum track of a popular music recording
(2015)
Long-range correlated temporal fluctuations in the beats of musical rhythms are an inevitable consequence of human action. According to recent studies, such fluctuations also lead to a favored listening experience. The scaling laws of amplitude variations in rhythms, however, are widely unknown. Here we use highly sensitive onset detection and time series analysis to study the amplitude and temporal fluctuations of Jeff Porcaro's one-handed hi-hat pattern in "I Keep Forgettin'"-one of the most renowned 16th note patterns in modern drumming. We show that fluctuations of hi-hat amplitudes and interbeat intervals (times between hits) have clear long-range correlations and short-range anticorrelations separated by a characteristic time scale. In addition, we detect subtle features in Porcaro's drumming such as small drifts in the 16th note pulse and non-trivial periodic two-bar patterns in both hi-hat amplitudes and intervals. Through this investigation we introduce a step towards statistical studies of the 20th and 21st century music recordings in the framework of complex systems. Our analysis has direct applications to the development of drum machines and to drumming pedagogy.
With the availability of cost effective embedded Linux solutions and the increasing complexity of embedded devices because of growing calculation power and communication demand, Linux is getting increasingly interesting as an operating system for the design of embedded control solutions. This is the case for almost all technical applications in electrical engineering like energy distribution systems, high level communication, signal processing or industrial automation. In the engineering master courses at the OTH Regensburg, a lecture is offered introducing students to Linux with a strong focus on embedded applications. This paper describes the concept of the lecture including the laboratory set up and gives some examples of embedded Linux projects performed by students.
This article provides a unifying Bayesian view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By identifying and converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules. We thus summarize the various approaches as approximations or modifications of the same Bayesian decoding rule leading to a unified view on known derivations as well as to new formulations for certain approaches.
The vehicular ad hoc network (VANET) technology based on the approved IEEE 802.11p standard and the appendant inter-vehicle communication (IVC) has the potential to dramatically change the way transportation systems work. The fundamental idea is to change the individual behavior of each vehicle by exchanging information among traffic participants to realize a cooperative and more efficient ransportation system. Certainly, the evaluation of such systems is a comprehensive and challenging task in a real world test bed, therefore, simulation frameworks are a key tool to analyze IVC. Several models are needed to emulate the real behavior of a VANET in all aspects as much realistically as necessary. The intention of this survey is to provide a comprehensive overview of publications concerning IVC simulations of the year 2013 and to see how IVC simulation has changed since 2009. Based on this analysis, we will answer the following questions: What simulation techniques are applied to IVC? Which aspects of IVS have been evaluated? What has changed within five years of IVC simulations? We also take a closer look at commonly used software tools and discuss their functionality and drawbacks. Finally, we present open questions concerning IVC simulations.
Intelligent driver assistance systems and new infotainment innovations cause a rapidly growing demand of computing power. To satisfy that demand, the quantity of electronic control units in cars has increased dramatically. OEMs tackle that trend by consolidating software on powerful multicore hardware platforms. However, current software solutions are mostly static and designed to run on limited platforms. As promising operating system for automotive, Linux comes into consideration, which seems to scale better than already existing solutions. To ease the migration process of older software parts and guarantee freedom from interference according to ISO26262 between single software partitions, embedded hypervisors can achieve that requirements. Up to now, automotive systems are not developed to run within virtualized environments. Within this paper, we present an approach to map communication channels of virtual automotive ECUs and connect them with their already existing CAN interfaces. For our analysis, we use the Xen hypervisor. The focus for interaction between virtual machines is to use SocketCAN and given paravirtualized Ethernet drivers. Our goal is a non-intrusive software integration methodology. We keep the source code within software partitions as unmodified as possible. To benchmark our studies, we evaluate our implementation on the Intel i7 and the.