This volume contains those research papers presented at the Second International Conference on Tests and Proofs (TAP 2008) that were not included in the main conference proceedings. TAP was the second conference devoted to the convergence of proofs and tests. It combines ideas from both areas for the advancement of software quality. To prove the correctness of a program is to demonstrate, through impeccable mathematical techniques, that it has no bugs; to test a program is to run it with the expectation of discovering bugs. On the surface, the two techniques seem contradictory: if you have proved your program, it is fruitless to comb it for bugs; and if you are testing it, that is surely a sign that you have given up on any hope of proving its correctness. Accordingly, proofs and tests have, since the onset of software engineering research, been pursued by distinct communities using rather different techniques and tools. And yet the development of both approaches leads to the discovery of common issues and to the realization that each may need the other. The emergence of model checking has been one of the first signs that contradiction may yield to complementarity, but in the past few years an increasing number of research efforts have encountered the need for combining proofs and tests, dropping earlier dogmatic views of their incompatibility and taking instead the best of what each of these software engineering domains has to offer. The first TAP conference (held at ETH Zurich in February 2007) was an attempt to provide a forum for the cross-fertilization of ideas and approaches from the testing and proving communities. For the 2008 edition we found the Monash University Prato Centre near Florence to be an ideal place providing a stimulating environment. We wish to sincerely thank all the authors who submitted their work for consideration. And we would like to thank the Program Committee members as well as additional referees for their great effort and professional work in the review and selection process. Their names are listed on the following pages. In addition to the contributed papers, the program included three excellent keynote talks. We are grateful to Michael Hennell (LDRA Ltd., Cheshire, UK), Orna Kupferman (Hebrew University, Israel), and Elaine Weyuker (AT&T Labs Inc., USA) for accepting the invitation to address the conference. Two very interesting tutorials were part of TAP 2008: "Parameterized Unit Testing with Pex" (J. de Halleux, N. Tillmann) and "Integrating Verification and Testing of Object-Oriented Software" (C. Engel, C. Gladisch, V. Klebanov, and P. Rümmer). We would like to express our thanks to the tutorial presenters for their contribution. It was a team effort that made the conference so successful. We are grateful to the Conference Chair and the Steering Committee members for their support. And we particularly thank Christoph Gladisch, Beate Körner, and Philipp Rümmer for their hard work and help in making the conference a success. In addition, we gratefully acknowledge the generous support of Microsoft Research Redmond, who financed an invited speaker.

Five personality traits commonly known as the “Big Five” have been widely acknowledged as universal. But most available psychological instruments are not necessarily transferable to other cultures. They are referred to as “W.E.I.R.D.” (western, educated, industrial, rich, democratic) and lack the combined emic-etic approach that is necessary for a transcultural perspective. This intercontinental congress brings experts from Kenya and Germany together – thinking out of the box and collecting ideas for a scientific based partnership of East Africa and Europe. Main topics are psychological constructs that prove relevant for Human Resources Management. The Five-Factor Model, core self-evaluations, coping processes and acculturation as well as globalization effects and gender issues are discussed.

Abstracts

The Proceedings of the 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding include publications (extended abstracts), that cover but are not limited to the following topics: - Mathematical Theory of Pattern Recognition, Image and Speech Processing, Analysis, Recognition and Understanding. - Cognitive Technologies, Information Technologies, Automated Systems and Software for Pattern Recognition, Image, Speech and Signal Processing, Analysis and Understanding - Databases, Knowledge Bases, and Linguistic Tools - Special-Purpose Architectures, Software and Hardware Tools - Vision and Sensor Data Interpretation for Robotics - Industrial, Medical, Multimedia and Other Applications - Algorithms, Software, Automated Systems and Information Technologies in Bioinformatics and Medical Informatics. The workshop took place from December 1st-5th, 2014, at the University of Koblenz-Landau in Koblenz, Germany.

A fundamental understanding of attachment of engineered nanoparticles to environmentalrnsurfaces is essential for the prediction of nanoparticle fate and transport in the environment. The present work investigates the attachment of non-coated silver nanoparticles and citraterncoated silver nanoparticles to different model surfaces and environmental surfaces in thernpresence and absence of humic acid. Batch sorption experiments were used for this investigation. The objective of this thesis was to investigate how silver nanoparticles interactrnwith surfaces having different chemical functional groups. The effect of presence of HA, on the particle-surface interactions was also investigated. In the absence of humic acid, nanoparticle-surface interactions or attachment was influencedrnby the chemical nature of the interacting surfaces. On the other hand, in the presence ofrnhumic acid, nanoparticle-surface attachment was influenced by the specific surface area of the sorbent surfaces. The sorption of non-coated silver nanoparticles and citrate coatedrnnanoparticles to all the surfaces was nonlinear and best described by Langmuir isotherm, indicating monolayer sorption of nanoparticles on to the surfaces. This can be explained as due to the blocking effect generated by the particle-particle repulsion. In the presence of humic acid, sorption of nanoparticles to the surfaces was linear. When the humic acid was present in the interacting medium, both the nanoparticles and surfaces were getting coated with humic acid and this masks the chemical functionalities of the surfaces. This leads to the change in particle-surface interactions, in the presence of humic acid. For the silver nanoparticle sorption from an unstable suspension, the sorption isotherms did not follow any classical sorption models, suggesting interplay between aggregation and sorption. Citrate coated silver nanoparticles and humic acid coated silver nanoparticles showed arndepression in sorption compared to the sorption of non-coated silver nanoparticles. In therncase of citrate coated silver nanoparticles the decrease in sorption can be explained by thernmore negative zeta potential of citrate coated nanoparticles compared to non-coated ones. For humic acid coated nanoparticles the sorption depression can be due to the steric hindrance caused by the free humic acid molecules which may coat the sorbent surface or due to the competition for sorption sites between the nanoparticle and free humic acid molecules present in the suspension. Thus nanoparticle surface chemistry is an important factor that determines the attachment of nanoparticles towards surfaces and it makes the characterization of nanoparticle surface an essential step in the study of their fate in the environment. Another aim of this study was to introduce the potential of chemical force microscopy for nanoparticle surface characterization. With the use of this technique, it was possible to distinguish between bare silver nanoparticles, citrate coated silver nanoparticles, and humic acid coated silver nanoparticles. This was possible by measuring the adhesion forces between the nanoparticles and five different AFM probes having different chemical functionalization.

Most social media platforms allow users to freely express their opinions, feelings, and beliefs. However, in recent years the growing propagation of hate speech, offensive language, racism and sexism on the social media outlets have drawn attention from individuals, companies, and researchers. Today, sexism both online and offline with different forms, including blatant, covert, and subtle lan- guage, is a common phenomenon in society. A notable amount of work has been done over identifying sexist content and computationally detecting sexism which exists online. Although previous efforts have mostly used peoples’ activities on social media platforms such as Twitter as a public and helpful source for collecting data, they neglect the fact that the method of gathering sexist tweets could be biased towards the initial search terms. Moreover, some forms of sexism could be missed since some tweets which contain offensive language could be misclassified as hate speech. Further, in existing hate speech corpora, sexist tweets mostly express hostile sexism, and to some degree, the other forms of sexism which also appear online was disregarded. Besides, the creation of labeled datasets with manual exertion, relying on users to report offensive comments with a tremendous effort by human annotators is not only a costly and time-consuming process, but it also raises the risk of involving discrimination under biased judgment. This thesis generates a novel sexist and non-sexist dataset which is constructed via "UnSexistifyIt", an online web-based game that incentivizes the players to make minimal modifications to a sexist statement with the goal of turning it into a non-sexist statement and convincing other players that the modified statement is non-sexist. The game applies the methodology of "Game With A Purpose" to generate data as a side-effect of playing the game and also employs the gamification and crowdsourcing techniques to enhance non-game contexts. When voluntary participants play the game, they help to produce non-sexist statements which can reduce the cost of generating new corpus. This work explores how diverse individual beliefs concerning sexism are. Further, the result of this work highlights the impact of various linguistic features and content attributes regarding sexist language detection. Finally, this thesis could help to expand our understanding regarding the syntactic and semantic structure of sexist and non-sexist content and also provides insights to build a probabilistic classifier for single sentences into sexist or non-sexist classes and lastly find a potential ground truth for such a classifier.

Knowledge-based authentication methods are vulnerable to Shoulder surfing phenomenon. The widespread usage of these methods and not addressing the limitations it has could result in the user’s information to be compromised. User authentication method ought to be effortless to use and efficient, nevertheless secure. The problem that we face concerning the security of PIN (Personal Identification Number) or password entry is shoulder surfing, in which a direct or indirect malicious observer could identify the user sensitive information. To tackle this issue we present TouchGaze which combines gaze signals and touch capabilities, as an input method for entering user’s credentials. Gaze signals will be primarily used to enhance targeting and touch for selecting. In this work, we have designed three different PIN entry method which they all have similar interfaces. For the evaluation, these methods were compared based on efficiency, accuracy, and usability. The results uncovered that despite the fact that gaze-based methods require extra time for the user to get familiar with yet it is considered more secure. In regards to efficiency, it has the similar error margin to the traditional PIN entry methods.

Human action recognition from a video has received growing attention in computer vision and has made significant progress in recent years. Action recognition is described as a requirement to decide which human actions appear in videos. The difficulties involved in distinguishing human actions are due to the high complexity of human behaviors as well as appearance variation, motion pattern variation, occlusions, etc. Many applications use human action recognition on captured video from cameras, resulting in video surveillance systems, health monitoring, human-computer interaction, and robotics. Action recognition based on RGB-D data has increasingly drawn more attention to it in recent years. RGB-D data contain color (Red, Green, and Blue (RGB)) and depth data that represent the distance from the sensor to every pixel in the object (object point). The main problem that this thesis deals with is how to automate the classification of specific human activities/actions through RGB-D data. The classification process of these activities utilizes a spatial and temporal structure of actions. Therefore, the goal of this work is to develop algorithms that can distinguish these activities by recognizing low-level and high-level activities of interest from one another. These algorithms are developed by introducing new features and methods using RGB-D data to enhance the detection and recognition of human activities. In this thesis, the most popular state-of-the-art techniques are reviewed, presented, and evaluated. From the literature review, these techniques are categorized into hand-crafted features and deep learning-based approaches. The proposed new action recognition framework is based on these two categories that are approved in this work by embedding novel methods for human action recognition. These methods are based on features extracted from RGB-D data that are evaluated using machine learning techniques. The presented work of this thesis improves human action recognition in two distinct parts. The first part focuses on improving current successful hand-crafted approaches. It contributes into two significant areas of state-of-the-art: Execute the existing feature detectors, and classify the human action in the 3D spatio-temporal domains by testing a new combination of different feature representations. The contributions of this part are tested based on machine learning techniques that include unsupervised and supervised learning to evaluate this suitability for the task of human action recognition. A k-means clustering represents the unsupervised learning technique, while the supervised learning technique is represented by: Support Vector Machine, Random Forest, K-Nearest Neighbor, Naive Bayes, and Artificial Neural Networks classifiers. The second part focuses on studying the current deep-learning-based approach and how to use it with RGB-D data for the human action recognition task. As the first step of each contribution, an input video is analyzed as a sequence of frames. Then, pre-processing steps are applied to the video frames, like filtering and smoothing methods to remove the noisy data from each frame. Afterward, different motion detection and feature representation methods are used to extract features presented in each frame. The extracted features are represented by local features, global features, and feature combination besides deep learning methods, e.g., Convolutional Neural Networks. The feature combination achieves an excellent accuracy performance that outperforms other methods on the same RGB-D datasets. All the results from the proposed methods in this thesis are evaluated based on publicly available datasets, which illustrate that using spatiotemporal features can improve the recognition accuracy. The competitive experimental results are achieved overall. In particular, the proposed methods can be better applied to the test set compared to the state-of-the-art methods using the RGB-D datasets.

Recent estimates have confirmed that inland waters emit a considerable amount of CH4 and CO2 to the atmosphere at the regional and global scale. But these estimates are based on extrapolated measured data and lack of data from inland waters in arid and semi-arid regions and carbon sources from wastewater treatment plants (WWTPs) as well insufficient resolution of the spatiotemporal variability of these emissions. Through this study, we analyzed monthly hydrological, meteorological and water quality data from three irrigation and drinking water reservoirs in the lower Jordan River basin and estimated the atmospheric emission rates of CO2. We investigated the effect of WWTPs on surrounding aquatic systems in term of CH4 and CO2 emission by presenting seasonally resolved data for dissolved concentrations of both gases in the effluents and in the receiving streams at nine WWTPs in Germany. We investigated spatiotemporal variability of CH4 and CO2 emission from aquatic ecosystems by using of simple low-cost tools for measuring CO2 flux and bubble release rate from freshwater systems. Our estimates showed that reservoirs in semi-arid regions are oversaturated with CO2 and acted as net sources to the atmosphere. The magnitude of observed fluxes at the three water reservoirs in Jordan is comparable to those from tropical reservoirs (3.3 g CO2 m-2 d-1). The CO2 emission rate from these reservoirs is linked to changes of water surface area, which is the result of water management practices. WWTPs have been shown to discharge a considerable amount of CH4 (30.9±40.7 kg yr-1) and CO2 (0.06±0.05 Gg yr-1) to their surrounding streams, and emission rates of CH4 and CO2 from these streams are significantly enhanced by effluents of WWTPs up to 1.2 and 8.6 times, respectively. Our results showed that both diffusive flux and bubble release rate varied in time and space, and both of emission pathways should be included and variability should be resolved adequately in further sampling and measuring strategies. We conclude that future emission measurements and estimates from inland waters may consider water management practices, carbon sources from WWTPs as well spatial and temporal variability of emission.

In the present study the flora and vegetation of Kakamega Forest, an East African rainforest in Western Kenya, was investigated. Kakamega Forest is highly degraded and fragmented and is an ideal model to study the anthropogenic influence on the forest inventory. The main focus was to analyse the influence of human impact on the vascular plant species composition. During five field phases in the years 2001 to 2004 a total of 19 study sites scattered over the whole forest including all fragments were investigated regarding forest structure, species composition and plant communities. The different forest sites were analysed by three different methods, phytosociological relevés, line-transect and with the variable-area transect method. The forest survey revealed about 400 taxa of vascular plant species, among them 112 trees, 62 shrubs, 58 climbers and 114 herbs. Several species are restricted to this forest in Kenya, but only one endemic species, the herb Commelina albiflora, could be discovered. About 15 species were recorded as new for Kenya and probably at least one species is new to science. Kakamega Forest is a unique mixture of Guineo-Congolian and Afromontane floral elements. About one half of the vascular plant species has its origin in the lowland forests of the Congo basin and one third originates from Afromontane habitats. The present study represents the first description of plant communities of Kakamega Forest. An analysis of different forest sites and plantations resulted in 17 different vegetation units. For the mature forest sites eleven plant communities were described. The young succession stage consists of two plant communities. Since the disturbance history and the age of the different plant communities could be estimated, their chronology was also described. An exception are the study sites within the plantations and afforested sites. The four defined vegetation units were not described as plant communities, because they are highly affected by man and do not belong to the natural succession of Kakamega Forest. Nevertheless, the regeneration potential of such forests was investigated. Due to the different succession stages the changing species composition along a disturbance gradient could be analysed. Most of Kakamega Forest consists of middle-aged secondary forest often surrounded by very young secondary forest. A true primary rainforest could not be found due the massive influence by over-exploitation. In all parts of the forest the anthropogenic influence could be observed. The forest develops towards a climax stage, but a 2 Abstract comparison with former surveys shows that the regeneration is much slower than expected. Human impact has to be avoided to allow the forest to develop into a primary-like rainforest. But several climax tree species might be missing anyway, because after the broad logging activities in the past there are not enough seed trees remaining. Species richness was highest in disturbed forest sites. A mixture of pioneer, climax and bushland species could be recorded there. Therefore, a high species richness is not a suitable indicator for forest quality. The proportion of climax species typical for Kakamega Forest would be a better measure. Compared to the main forest block the forest fragments do not lack in diversity as expected due to fragmentation processes. Instead, the only near primary forest could be recorded in Kisere, a northern fragment. The high amount of climax species and the more or less undisturbed forest structure is a result of the strict protection by the Kenya Wildlife Service and due to low logging activities. Differences in species composition between the studied forest sites are either a result of the different logging history or management regime rather than due to different edaphic or climatic conditions.