Nowcasting Thunderstorms for Munich Airport

The successful demonstration and assessment of the DLR thunderstorm nowcasting algorithms at Munich Airport during two campaigns in the summers of 2010 and 2011 are described. The algorithms Cb-TRAM and Rad-TRAM, that detect, monitor, and forecast up to one hour (nowcast) thunderstorm cells from satellite and radar data, run in real time and provided new thunderstorm products for users at the airport. The products were presented on displays the users were already familiar with as well as on webpages designed by DLR. On the webpages, also additional information like measurements with DLR’s polarimetric radar and model forecasts was shown. Moreover, thunderstorm warnings were is-sued and sent via email to the users whenever a thunderstorm was detected in the terminal manoeu-vring area of the airport of Munich. The nowcasting skills of Rad-TRAM and Cb-TRAM are encouraging, especially for lead times up to 30 minutes, and the user feedback on the DLR thunderstorm products was very positive. The Rad-TRAM and Cb-TRAM products provide a good overview on the situation and its future development, and the thunderstorm warnings were very helpful for the collaborative decision making at the airport. However, some suggestions for improvements were made like the demand for nowcasts beyond one hour. This will be considered within the integrated weather forecast system, WxFUSION, which has been further developed during the campaigns

EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR , the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets

Fault Injection for Embedded Microprocessor-based Systems

Microprocessor-based embedded systems are increasingly used to control safety-critical systems (e.g., air and railway traffic control, nuclear plant control, aircraft and car control). In this case, fault tolerance mechanisms are introduced at the hardware and software level. Debugging and verifying the correct design and implementation of these mechanisms ask for effective environments, and Fault Injection represents a viable solution for their implementation. In this paper we present a Fault Injection environment, named FlexFI, suitable to assess the correctness of the design and implementation of the hardware and software mechanisms existing in embedded microprocessor-based systems, and to compute the fault coverage they provide. The paper describes and analyzes different solutions for implementing the most critical modules, which differ in terms of cost, speed, and intrusiveness in the original system behavio

The Best Answers? Think Twice: Online Detection of Commercial Campaigns in the CQA Forums

In an emerging trend, more and more Internet users search for information from Community Question and Answer (CQA) websites, as interactive communication in such websites provides users with a rare feeling of trust. More often than not, end users look for instant help when they browse the CQA websites for the best answers. Hence, it is imperative that they should be warned of any potential commercial campaigns hidden behind the answers. However, existing research focuses more on the quality of answers and does not meet the above need. In this paper, we develop a system that automatically analyzes the hidden patterns of commercial spam and raises alarms instantaneously to end users whenever a potential commercial campaign is detected. Our detection method integrates semantic analysis and posters' track records and utilizes the special features of CQA websites largely different from those in other types of forums such as microblogs or news reports. Our system is adaptive and accommodates new evidence uncovered by the detection algorithms over time. Validated with real-world trace data from a popular Chinese CQA website over a period of three months, our system shows great potential towards adaptive online detection of CQA spams.Comment: 9 pages, 10 figure

Using evaluation techniques and performance claims to demonstrate public relations impact: An Australian perspective

Public relations professionals use many methods to demonstrate their contribution to organizational goals, yet it is unclear how their attitudes towards evaluation and the reporting of success matches real outcomes. Ten years after the International Public Relations Association produced an evaluation gold paper, this study combines research on Australian practitioners’ evaluation practices and attitudes, and data from industry awards to identify how practitioners demonstrate their accountability. Data suggest that despite the attention paid to evaluation by the academy and industry, practitioners still focus on measuring outputs, not outcomes to demonstrate performance and continue to rely heavily on media-based evaluation methods

Determination of pesticides in the respirable fraction of airborne particulate matter by high-performance liquid chromatography–tandem mass spectrometry

Potential harmful effects of pesticides include risks to human health of workers involved in the wet spray application in cultivated areas. Inhalation exposure depends on several factors including pesticide concentrations in the respirable fraction of airborne particulate matter (PM4). To ensure a high level of protection, the use of tractors with cabins provides protection against dust, aerosols, and vapors. Since tractors not providing maximum protection are still in use, PM4 was sampled during spreading operations in agricultural fields inside and outside tractor cabins. Sample preparation technique based on accelerated solvent extraction and solid-phase extraction cleanup was optimized before analysis of nine pesticides in PM4. Meptyldinocap, deltamethrin, myclobutanil, fluopyram, methoxyfenozide, dimethomorph, fluopicolide, cyflufenamid, and metrafenone were simultaneously determined by high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (HPLC–ESI–MS–MS). The results demonstrated the efficacy of the tractor cabs used in the sampling sites. © 2017 Taylor & Francis

Applying a unified public relations evaluation model in a European context

The evaluation of public relations programmes has been a topic of keen interest to the public relations community for many years. A number of three level/stage models have been proposed to describe and explain the evaluation process. They have not been successful in terms of practitioner acceptance and have been criticised for being unrealistic and lacking feedback mechanisms. More recently the short-term and continuing models have been developed in response to these criticisms. This paper suggests a Unified Evaluation model which uses an established analysis of the communication/persuasion process as a framework to integrate and unify existing models that describe the public relations evaluation process. The proposed testing of this model in a European, transnational context is then outlined

A Time for Action: A New Vision of Participatory Democracy

For over eighty years, the League of Women Voters has been a voice for women and men of all backgrounds, rising above partisan disputes to help citizens fully and intelligently exercise their rights -- and their responsibilities -- as participants in the American experiment. The League has earned a reputation for integrity and fairness, and generations have relied upon League resources to help them make the kind of informed decisions that keep policymakers responsive and truly give weight and meaning to the hallowed phrase "consent of the governed."The League has cultivated expertise on electoral behavior and public policy at national, state, and local levels, and has been a leader in identifying and researching political trends. In recent years, one of the most distressing trends has been the ongoing decline of civic participation, in the voting booth and beyond. If one measure of the health of democracy is the rate at which citizens participate in elections, the fitness of the American body politic has been spiraling downward ever since voter turnout peaked in 1960.Recognizing the need for new insights and strategies to attack this problem, the Chicago chapter of the League convened a Task Force of recognized experts and leaders from the community to spearhead an examination of the factors at play. Concerned organizations of many stripes have studied the situation over the years, but there has been no authoritative summary of what we know and what we yet need to learn that can be turned into real steps toward a solution. Why are people dropping out of the political process....and what can be done to draw them back? What creative strategies hold the most promise for capturing Americans' attention, raising their awareness, and inspiring them to participate?The Task Force's findings are often disturbing, yet they also give cause for optimism. Americans may be keeping to themselves in growing numbers, but they do not do so solely from apathy or indifference; and want only to be invited to share their views, to be assured that government will pay attention, to be shown how and why they can make a difference. Young people especially have felt shut out of the process, despite knowing as well as anyone what matters to them and their communities. It's time they were invited back in. In this deeply polarized political moment, it is vitally important that we remind all Americans that civic engagement isn't merely about the often arcane and alienating world of politics -- it's a way to share in something bigger than ourselves, to express our devotion to our country and our community, to assure that (in Abraham Lincoln's timeless phrase) "government of the people, by the people, for the people, shall not perish from the earth."Here in the state that was home to the author of those words, in the city where he was nominated for the presidency, we can take the first steps toward reinvigorating the vision he expressed. It is the hope of the League and the Task Force that this report will point the way toward those steps

Optimal advertising campaign generation for multiple brands using MOGA

The paper proposes a new modified multiobjective genetic algorithm (MOGA) for the problem of optimal television (TV) advertising campaign generation for multiple brands. This NP-hard combinatorial optimization problem with numerous constraints is one of the key issues for an advertising agency when producing the optimal TV mediaplan. The classical approach to the solution of this problem is the greedy heuristic, which relies on the strength of the preceding commercial breaks when selecting the next break to add to the campaign. While the greedy heuristic is capable of generating only a group of solutions that are closely related in the objective space, the proposed modified MOGA produces a Pareto-optimal set of chromosomes that: 1) outperform the greedy heuristic and 2) let the mediaplanner choose from a variety of uniformly distributed tradeoff solutions. To achieve these results, the special problem-specific solution encoding, genetic operators, and original local optimization routine were developed for the algorithm. These techniques allow the algorithm to manipulate with only feasible individuals, thus, significantly improving its performance that is complicated by the problem constraints. The efficiency of the developed optimization method is verified using the real data sets from the Canadian advertising industry

Clustering Memes in Social Media

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'13), 201