Sensitive-by-distance: quasi-health data in the algorithmic era

This article offers a new perspective on the boundaries between health and non-health data in the age of ‘Quantified-Self’ apps: the ‘data-sensitiveness-by-computational-distance’ approach-or, more simply, the ‘sensitive-by-distance’ approach. This approach takes into account two variables: the intrinsic sensitiveness (a static variable) of personal data and the computational distance (a dynamic variable) between some kinds of personal data and pure health (or sensitive) data, which depends upon computational capacity. From an objective perspective, computational capacity depends on the level of development of data retrieval technologies at a certain moment, the availability of ‘accessory data’, and the applicable legal restraints on processing data. From a subjective perspective, computational capacity depends on the specific data mining efforts (or the ability to invest in them) taken by a given data controller: economic resources, human resources, and the use of accessory data. A direct consequence of the expansion of augmented humanity in collecting and inferring personal data is the increasing loss of health data processing ‘legibility’ for data subjects. In order to address this issue, we propose exploiting the existing legal tools in the General Data Protection Regulation to empower data subjects (the right to data access, the right to know the logic involved in automated decision-making, data portability, etc.).     

Ontology-based mobile augmented reality in cultural heritage sites: information modeling and user study

Augmented reality (AR) has received much attention in the cultural heritage domain as an interactive medium for requesting and accessing information regarding heritage sites. In this study, we developed a mobile AR system based on Semantic Web technology to provide contextual information about cultural heritage sites. Most location-based AR systems are designed to present simple information about a point of interest (POI), but the proposed system offers information related to various aspects of cultural heritage, both tangible and intangible, linked to the POI. This is achieved via an information modeling framework where a cultural heritage ontology is used to aggregate heterogeneous data and semantically connect them with each other. We extracted cultural heritage data from five web databases and modeled contextual information for a target heritage site (Injeongjeon Hall and its vicinity in Changdeokgung Palace in South Korea) using the selected ontology. We then implemented a mobile AR application and conducted a user study to assess the learning and engagement impacts of the proposed system. We found that the application provides an agreeable user experience in terms of its affective, cognitive, and operative features. The results of our analysis showed that specific usage patterns were significant with regard to learning outcomes. Finally, we explored how the study’s key findings can provide practical design guidance for system designers to enhance mobile AR information systems for heritage sites, and to show system designers how to support particular usage patterns in order to accommodate specific user experiences better.

     

Fragile base-class problem,problem?

The fragile base-class problem (FBCP) has been described in the literature as a consequence of “misusing” inheritance and composition in object-oriented programming when (re)using frameworks. Many research works have focused on preventing the FBCP by proposing alternative mechanisms for reuse, but, to the best of our knowledge, there is no previous research work studying the prevalence and impact of the FBCP in real-world software systems. The goal of our work is thus twofold: (1) assess, in different systems, the prevalence of micro-architectures, called FBCS, that could lead to two aspects of the FBCP, (2) investigate the relation between the detected occurrences and the quality of the systems in terms of change and fault proneness, and (3) assess whether there exist bugs in these systems that are related to the FBCP. We therefore perform a quantitative and a qualitative study. Quantitatively, we analyse multiple versions of seven different open-source systems that use 58 different frameworks, resulting in 301 configurations. We detect in these systems 112,263 FBCS occurrences and we analyse whether classes playing the role of sub-classes in FBCS occurrences are more change and–or fault prone than other classes. Results show that classes participating in the analysed FBCS are neither more likely to change nor more likely to have faults. Qualitatively, we conduct a survey to confirm/infirm that some bugs are related to the FBCP. The survey involves 41 participants that analyse a total of 104 bugs of three open-source systems. Results indicate that none of the analysed bugs is related to the FBCP. Thus, despite large, rigorous quantitative and qualitative studies, we must conclude that the two aspects of the FBCP that we analyse may not be as problematic in terms of change and fault-proneness as previously thought in the literature. We propose reasons why the FBCP may not be so prevalent in the analysed systems and in other systems in general.     

Two-space variability compensation technique for speaker verification in short length and reverberant environments

The performance of state-of-the-art speaker verification in uncontrolled environment is affected by different variabilities. Short duration variability is very common in these scenarios and causes the speaker verification performance to decrease quickly while the duration of verification utterances decreases. Linear discriminant analysis (LDA) is the most common session variability compensation algorithm, nevertheless it presents some shortcomings when trained with insufficient data. In this paper we introduce two methods for session variability compensation to deal with short-length utterances on i-vector space. The first method proposes to incorporate the short duration variability information in the within-class variance estimation process. The second proposes to compensate the session and short duration variabilities in two different spaces with LDA algorithms (2S-LDA). First, we analyzed the behavior of the within and between class scatters in the first proposed method. Then, both proposed methods are evaluated on telephone session from NIST SRE-08 for different duration of the evaluation utterances: full (average 2.5 min), 20, 15, 10 and 5 s. The 2S-LDA method obtains good results on different short-length utterances conditions in the evaluations, with a EER relative average improvement of 1.58%, compared to the best baseline (WCCN[LDA]). Finally, we applied the 2S-LDA method in speaker verification under reverberant environment, using different reverberant conditions from Reverb challenge 2013, obtaining an improvement of 8.96 and 23% under matched and mismatched reverberant conditions, respectively.     

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.     

Achieving traceability in large scale continuous integration and delivery deployment,usage and validation of the eiffel framework

The importance of traceability in software development has long been recognized, not only for reasons of legality and certification, but also to enable the development itself. At the same time, organizations are known to struggle to live up to traceability requirements, and there is an identified lack of studies on traceability practices in the industry, not least in the area of tooling and infrastructure. This paper presents, investigates and discusses Eiffel, an industry developed solution designed to provide real time traceability in continuous integration and delivery. The traceability needs of industry professionals are also investigated through interviews, providing context to that solution. It is then validated through further interviews, a comparison with previous traceability methods and a review of literature. It is found to address the identified traceability needs and found in some cases to reduce traceability data acquisition times from days to minutes, while at the same time alternatives offering comparable functionality are lacking. In this work, traceability is shown not only to be an important concern to engineers, but also regarded as a prerequisite to successful large scale continuous integration and delivery. At the same time, promising developments in technical infrastructure are documented and clear differences in traceability mindset between separate industry projects is revealed.     

Top-<Emphasis Type="Italic">k</Emphasis> temporal keyword search over social media data

Social media services have already become main sources for monitoring emerging topics and sensing real-life events. A social media platform manages social stream consisting of a huge volume of timestamped user generated data, including original data and repost data. However, previous research on keyword search over social media data mainly emphasizes on the recency of information. In this paper, we first propose a problem of top-k most significant temporal keyword query to enable more complex query analysis. It returns top-k most popular social items that contain the keywords in the given query time window. Then, we design a temporal inverted index with two-tiers posting list to index social time series and a segment store to compute the exact social significance of social items. Next, we implement a basic query algorithm based on our proposed index structure and give a detailed performance analysis on the query algorithm. From the analysis result, we further refine our query algorithm with a piecewise maximum approximation (PMA) sketch. Finally, extensive empirical studies on a real-life microblog dataset demonstrate the combination of two-tiers posting list and PMA sketch achieves remarkable performance improvement under different query settings.     

Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.