Elevated Temperature and CO2 Concentrations Affect Carbon Flux in Two Boreal Conifers

Elevated temperatures and CO2 will alter carbon flux in two dominant boreal tree species Picea mariana (black spruce) and Larix laricina (tamarack). Trees were grown in three temperature treatments (ambient, ambient +4 °C, and ambient +8 °C) at either 400 ppm or 750 ppm CO2, to simulate climate conditions between now and the year 2100. Spruce acclimated to increasing temperature detractively; warming scenarios reduced spruce net carbon gain. Tamarack maintained comparable levels of net photosynthesis (Anet) across warming treatments and both species acclimated respiration (Rdark) with increasing growth temperature. Elevated CO2-grown spruce suppressed Anet whereas tamarack was insensitive. Decreasing leaf N with warming explained reduced Anet and Rdark for both species; however, tamarack mitigated this by increasing stomatal conductance. Moderate warming benefited tamarack growth but hindered spruce; extreme warming hindered growth in both species. Reduced CO2 uptake in these species with predicted warming may contribute to increased atmospheric CO2 accumulation

A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks

The emergence of pre-trained language models (PLMs) has shown great success in many Natural Language Processing (NLP) tasks including text classification. Due to the minimal to no feature engineering required when using these models, PLMs are becoming the de facto choice for any NLP task. However, for domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a pre-trained model for a specific task has shown to provide a performance improvement. In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domain-specific words, against a simple SVM linear classifier with TFIDF vectorized text. The experimental results on the four datasets show that using PLMs, even fine-tuned, do not provide significant gain over the linear SVM classifier. Hence, we recommend that for text classification tasks, traditional SVM along with careful feature engineering can pro-vide a cheaper and superior performance than PLMs

Attention is Not Always What You Need: Towards Efficient Classification of Domain-Specific Text

For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of accurate classification of classes at the higher level in the hierarchies is crucial to avoid errors propagating to the lower levels. In the business world, an efficient and explainable ML model is preferred over an expensive black-box model, especially if the performance increase is marginal. A current trend in the Natural Language Processing (NLP) community is towards employing huge pre-trained language models (PLMs) or what is known as self-attention models (e.g., BERT) for almost any kind of NLP task (e.g., question-answering, sentiment analysis, text classification). Despite the widespread use of PLMs and the impressive performance in a broad range of NLP tasks, there is a lack of a clear and well-justified need to as why these models are being employed for domain-specific text classification (TC) tasks, given the monosemic nature of specialized words (i.e., jargon) found in domain-specific text which renders the purpose of contextualized embeddings (e.g., PLMs) futile. In this paper, we compare the accuracies of some state-of-the-art (SOTA) models reported in the literature against a Linear SVM classifier and TFIDF vectorization model on three TC datasets. Results show a comparable performance for the LinearSVM. The findings of this study show that for domain-specific TC tasks, a linear model can provide a comparable, cheap, reproducible, and interpretable alternative to attention-based models

Differentiating Web Service Offerings

The advent of Service Oriented Architecture (SOA) paradigm and increasing use of Web Services (WS) implies that the future will see a large number of services transferred between providers and consumers, using many applications or agents working on behalf of humans. Discovering and using the services is the easy part. Negotiating and selecting the best services from amongst the plethora of similar ones, depending on their cost and quality, is the challenging issue. However, existing WS-I standards neither cater to provision of Service Level Agreements (SLAs), nor their exchange between parties. These standards are confined merely to WS description (WSDL). Once WS are discovered and selected, SLAs are merely used to monitor service compliance. We propose a novel method that allows service-providers to dynamically generate the SLAs, and then transfer them to clients for selection amongst competitive service providers. The clients use Application to Application (A2A) communication to choose the best service provider at run time, and then bind to it to available services. Our method complies with all WS-I standards, and hence does not require any modifications to the UDDI or WSDL. Instead of using the SLA as just a contractual document for compliance monitoring of the service, we also use it as a means of service selection. We demonstrate and validate our method using a prototype developed in laboratory settings, which uses multiple ‘Weather Service Providers’ to obtain various indicators for weather forecasting

Welcome from the Workshop Chairs

Over the last decade, the Information Technology industry has become ever more interested in evaluating Requirements Engineering (RE) approaches, techniques and tools and comparing their usefulness, effectiveness and utility in specific practical contexts. The increasing interest in empirical evaluation resulted in a growing number of industry-university collaborations in the RE community, that have been instrumental to generating empirical data through experiments, surveys, case studies, and action research studies. As empirical studies are recognized as indispensable and valuable ways to assess the actual benefits and cost of applying the RE methods and tools proposed in the RE community, the conversation on adopting systematic research methods and evaluation practices intensified. The overall objective of the EmpiRE workshop series at the annual RE conference is to increase the cross-fertilization of Empirical Software Engineering (ESE) methods and RE by actively encouraging the exchange of knowledge and ideas between the communities of ESE and RE. Since its launch at RE’11 in Trento, the EmpiRE workshop has been serving as the platform promoting the use of new evaluation techniques from ESE in the area of RE as well as the discussion on new domains and problems in RE where involving ESE will make a great difference. Some outcomes of the past editions of EmpiRE include the identification of open research problems and the possible solutions to these problems regarding: (i) the aspects of RE approaches that can be evaluated; (ii) the factors, criteria, and metrics that are appropriate for empirical evaluation purposes; (iii) the replication of empirical RE studies; (iv) the role of the users’ perspectives in empirical RE

Welcome from the Workshop Chairs

Welcome to the fifth International Workshop on Empirical Requirements Engineering (EmpiRE 2015) at RE’15!\ud In the past few years, some important developments in the Information Technology Services marketplace as well as in the software industry in particular fueled the debate on the evaluation of Requirements Engineering (RE) approaches, techniques and tools and the comparison of their usefulness, effectiveness and utility in specific practical contexts. Examples of such market trends include, among many others, the increased interest in collaborative and just-in-time application of RE techniques and the use of software analytics techniques for mining requirements repositories.Also, existing RE technology is more and more being applied in the context of new areas, such as Internet of Things, software ecosystems, green and Cloud computing, to name a few. This increased interest in empirical evaluation is precipitating a growing number of industry-university collaborations in the RE community, which, in turn, is instrumental in generating empirical data through case studies, action research studies, experiments, and surveys. As empirical studies are recognized as invaluable for assessing the actual benefits and cost of applying the RE methods and tools proposed in the RE community, the conversation on adopting these and on evaluation practices intensifies even further

Agent-based simulation of open source evolution

We present an agent-based simulation model developed to study how size, complexity and effort relate to each other in the development of open source software (OSS). In the model, many developer agents generate, extend, and re-factor code modules independently and in parallel. This accords with empirical observations of OSS development. To our knowledge, this is the first model of OSS evolution that includes the complexity of software modules as a limiting factor in productivity, the fitness of the software to its requirements, and the motivation of developers. Validation of the model was done by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled the observed data, except for system size: three of the OSS systems showed alternating patterns of super-linear and sub-linear growth, while the simulations produced only super-linear growth. However, the fidelity of the model for the other measures suggests that developer motivation and the limiting effect of complexity on productivity have a significant effect on the development of OSS systems and should be considered in any model of OSS development

Contrasting acclimation responses to elevated CO2 and warming between an evergreen and a deciduous boreal conifer

Rising atmospheric carbon dioxide (CO2) concentrations may warm northern latitudes up to 8°C by the end of the century. Boreal forests play a large role in the global carbon cycle, and the responses of northern trees to climate change will thus impact the trajectory of future CO2 increases. We grew two North American boreal tree species at a range of future climate conditions to assess how growth and carbon fluxes were altered by high CO2 and warming. Black spruce (Picea mariana, an evergreen conifer) and tamarack (Larix laricina, a deciduous conifer) were grown under ambient (407 ppm) or elevated CO2 (750 ppm) and either ambient temperatures, a 4°C warming, or an 8°C warming. In both species, the thermal optimum of net photosynthesis (ToptA) increased and maximum photosynthetic rates declined in warm-grown seedlings, but the strength of these changes varied between species. Photosynthetic capacity (maximum rates of Rubisco carboxylation, Vcmax, and of electron transport, Jmax) was reduced in warmgrown seedlings, correlating with reductions in leaf N and chlorophyll concentrations. Warming increased the activation energy for Vcmax and Jmax (EaV and EaJ, respectively) and the thermal optimum for Jmax. In both species, the ToptA was positively correlated with both EaV and EaJ, but negatively correlated with the ratio of Jmax/Vcmax. Respiration acclimated to elevated temperatures, but there were no treatment effects on the Q10 of respiration (the increase in respiration for a 10°C increase in leaf temperature). A warming of 4°C increased biomass in tamarack, while warming reduced biomass in spruce. We show that climate change is likely to negatively affect photosynthesis and growth in black spruce more than in tamarack, and that parameters used to model photosynthesis in dynamic global vegetation models (EaV and EaJ) show no response to elevated CO2.Ontario Early Researcher Award; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Australian National Universit