Selling to strategic consumers: on the benefits of consumers’ valuation uncertainty and abundant inventory

in Journal of Revenue and Pricing Management (2018), 17(3), 146-165

A comparison between a person and a robot in the attention, imitation, and repetitive and stereotypical behaviors of children with autism spectrum disorder.

Scientific Conference (2018)

The aim of the present study was to assess the usefulness of QT, a socially assistive robot, in interventions with children with autism spectrum disorder (ASD) by assessing children’s attention, imitation ... [more ▼]

The aim of the present study was to assess the usefulness of QT, a socially assistive robot, in interventions with children with autism spectrum disorder (ASD) by assessing children’s attention, imitation, and presence of repetitive and stereotyped behaviors. Fifteen children diagnosed with ASD, aged from 4 to 14 years participated in two short interactions, one with a person and one with QT robot. Statistical analyses revealed that children directed more attention towards the robot than to the person, imitated the robot as much as the person, and engaged in fewer repetitive or stereotyped behaviors with the robot than with the person. These results support previous research demonstrating the usefulness of robots in interventions with children with ASD and provide new evidence to the usefulness of robots in reducing repetitive and stereotyped behaviors in children with ASD, which can affect children’s learning. [less ▲]

A Search-based Approach for Accurate Identification of Log Message Formats

in Proceedings of the 26th IEEE/ACM International Conference on Program Comprehension (ICPC ’18) (2018)

Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the ... [more ▼]

Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the actual events recorded in the log. Each event is denoted by a log message, which is composed of a fixed part-called (event) template-that is the same for all occurrences of the same event type, and a variable part, which may vary with each event occurrence. The formats of log messages, in complex and evolving systems, have numerous variations, are typically not entirely known, and change on a frequent basis; therefore, they need to be identified automatically. The log message format identification problem deals with the identification of the different templates used in the messages of a log. Any solution to this problem has to generate templates that meet two main goals: generating templates that are not too general, so as to distinguish different events, but also not too specific, so as not to consider different occurrences of the same event as following different templates; however, these goals are conflicting. In this paper, we present the MoLFI approach, which recasts the log message identification problem as a multi-objective problem. MoLFI uses an evolutionary approach to solve this problem, by tailoring the NSGA-II algorithm to search the space of solutions for a Pareto optimal set of message templates. We have implemented MoLFI in a tool, which we have evaluated on six real-world datasets, containing log files with a number of entries ranging from 2K to 300K. The experiments results show that MoLFI extracts by far the highest number of correct log message templates, significantly outperforming two state-of-the-art approaches on all datasets. [less ▲]

Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search

in Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018) (2018)

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ... [more ▼]

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ways. A challenge is to detect and manage feature interactions, in particular, those that violate system requirements, hence leading to failures. In this paper, we propose a technique to detect feature interaction failures by casting our approach into a search-based test generation problem. We define a set of hybrid test objectives (distance functions) that combine traditional coverage-based heuristics with new heuristics specifically aimed at revealing feature interaction failures. We develop a new search-based test generation algorithm, called FITEST, that is guided by our hybrid test objectives. FITEST extends recently proposed many-objective evolutionary algorithms to reduce the time required to compute fitness values. We evaluate our approach using two versions of an industrial self-driving system. Our results show that our hybrid test objectives are able to identify more than twice as many feature interaction failures as two baseline test objectives used in the software testing literature (i.e., coverage-based and failure-based test objectives). Further, the feedback from domain experts indicates that the detected feature interaction failures represent real faults in their systems that were not previously identified based on analysis of the system features and their requirements. [less ▲]

Automated Generation of Constraints from Use Case Specifications to Support System Testing

in Proceedings of the 11th IEEE Conference on Software Testing, Validation and Verification (2018)

System testing plays a crucial role in safety-critical domains, e.g., automotive, where system test cases are used to demonstrate the compliance of software with its functional and safety requirements ... [more ▼]

System testing plays a crucial role in safety-critical domains, e.g., automotive, where system test cases are used to demonstrate the compliance of software with its functional and safety requirements. Unfortunately, since requirements are typically written in natural language, significant engineering effort is required to derive test cases from requirements. In such a context, automated support for generating system test cases from requirements specifications written in natural language would be highly beneficial. Unfortunately, existing approaches have limited applicability. For example, some of them require that software engineers provide formal specifications that capture some of the software behavior described using natural language. The effort needed to define such specifications is usually a significant deterrent for software developers. This paper proposes an approach, OCLgen, which largely automates the generation of the additional formal specifications required by an existing test generation approach named UMTG. More specifically, OCLgen relies on semantic analysis techniques to automatically derive the pre- and post-conditions of the activities described in use case specifications. The generated conditions are used by UMTG to identify the test inputs that cover all the use case scenarios described in use case specifications. In practice, the proposed approach enables the automated generation of test cases from use case specifications while avoiding most of the additional modeling effort required by UMTG. Results from an industrial case study show that the approach can automatically and correctly generate more than 75% of the pre- and post-conditions characterizing the activities described in use case specifications. [less ▲]

Positions on Emancipation. Architecture between Aesthetics and Politics

Book published by Lars Müller Publishers (2018)

Who owns the land? Highlights in the history of land reform

in Archplus (2018), (231),

Hans-Jochen Vogel's Project to Reform Ownership of Urban Land. A protocol in 20 points

in Arch + (2018), 231

Cache-aided Simultaneous Wireless Information and Power Transfer (SWIPT) with Relay Selection

in IEEE Journal on Selected Areas In Communications (2018)

In this paper, we investigate the performance of cache-assisted simultaneous wireless information and power transfer (SWIPT) cooperative systems, in which one source communicates with one destination via ... [more ▼]

In this paper, we investigate the performance of cache-assisted simultaneous wireless information and power transfer (SWIPT) cooperative systems, in which one source communicates with one destination via the aid of multiple relays. In order to prolong the relays’ serving time, the relays are assumed to be equipped with a cache memory and energy harvesting (EH) capability. Based on the time-splitting mechanism, we analyze the effect of caching on the system performance in terms of the serving throughput and the stored energy at the relay. In particular, two optimization problems are formulated to maximize the relay-destination throughput and the energy stored at the relay subject to some quality-of-service (QoS) constraints, respectively. By using the KKT conditions and with the help of the Lambert function, closed-form solutions are obtained for the two formulated problems. In order to further improve the performance, a relay selection policy is introduced to select the best relay based on either the maximum throughput between the relays’ and destination link or maximum stored energy at the relay, for conveying information to the destination. Numerical results reveal significant benefits of incorporating caching capabilities to SWIPT systems, in terms of improved serving time, throughput and energy harvesting performance at the relays. [less ▲]

Live Intracellular Biorthogonal Imaging by Surface Enhanced Raman Spectroscopy using Alkyne-Silver Nanoparticles Clusters

in Scientific Reports (2018), 8

Live intracellular imaging is a valuable tool in modern diagnostics and pharmacology. Surface Enhanced Raman Spectroscopy (SERS) stands out as a non-destructive and multiplexed technique, but ... [more ▼]

Live intracellular imaging is a valuable tool in modern diagnostics and pharmacology. Surface Enhanced Raman Spectroscopy (SERS) stands out as a non-destructive and multiplexed technique, but intracellular SERS imaging still suffers from interfering background from endogenous components. Here we show the assembly of small colloidal SERS probes with Raman signal in the cell-silent window of 1800–2900 cm−1 for biorthogonal intracellular SERS imaging of dopamine that was undistinguishable from the endogenous cell background. By linking colloidal silver nanoparticles with alkyne-dopamine adducts, clusters are formed by 2–6 nanoparticles spaced by tight interparticle gaps that exhibited high electric field enhancement and strong SERS signals of alkyne and dopamines. Due to the cell-silent signals of the alkyne, intracellular in-vitro Raman imaging shows that the dopamines on the internalized clusters remain distinguishable across the cytoplasm with good spatial resolution. Our method can be a general-purpose method for real-time imaging of biomolecules, such as proteins, peptides, DNA and drugs. [less ▲]