Tracking the Expression of Annoyance in Call Centers

Machine learning researchers have dealt with the identification of emo- tional cues from speech since it is research domain showing a large number of po- tential applications. Many acoustic parameters have been analyzed when searching for cues to identify emotional categories. Then classical classifiers and also out- standing computational approaches have been developed. Experiments have been carried out mainly over induced emotions, even if recently research is shifting to work over spontaneous emotions. In such a framework, it is worth mentioning that the expression of spontaneous emotions depends on cultural factors, on the particu- lar individual and also on the specific situation. In this work, we were interested in the emotional shifts during conversation. In particular we were aimed to track the annoyance shifts appearing in phone conversations to complaint services. To this end we analyzed a set of audio files showing different ways to express annoyance. The call center operators found disappointment, impotence or anger as expression of annoyance. However, our experiments showed that variations of parameters derived from intensity combined with some spectral information and suprasegmental fea- tures are very robust for each speaker and annoyance rate. The work also discussed the annotation problem arising when dealing with human labelling of subjective events. In this work we proposed an extended rating scale in order to include anno- tators disagreements. Our frame classification results validated the chosen annota- tion procedure. Experimental results also showed that shifts in customer annoyance rates could be potentially tracked during phone callsSpanish Mineco under grant TIN2014- 54288-C4-4-R H2020 EU under Empathic RIA action number 769872

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

<p>Abstract</p> <p>Background</p> <p>The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published <it>q</it>-Norm MKL algorithm.</p> <p>Results</p> <p>We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities<it> ab initio</it> along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.</p> <p>Conclusions</p> <p>We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.</p

Beyond outputs: pathways to symmetrical evaluations of university sustainable development partnerships

As the United Nations Decade of Education for Sustainable Development (2005–2014) draws to a close, it is timely to review ways in which the sustainable development initiatives of higher education institutions have been, and can be, evaluated. In their efforts to document and assess collaborative sustainable development program outcomes and impacts, universities in the North and South are challenged by similar conundrums that confront development agencies. This article explores pathways to symmetrical evaluations of transnationally partnered research, curricula, and public-outreach initiatives specifically devoted to sustainable development. Drawing on extensive literature and informed by international development experience, the authors present a novel framework for evaluating transnational higher education partnerships devoted to sustainable development that addresses design, management, capacity building, and institutional outreach. The framework is applied by assessing several full-term African higher education evaluation case studies with a view toward identifying key limitations and suggesting useful future symmetrical evaluation pathways. University participants in transnational sustainable development initiatives, and their supporting donors, would be well-served by utilizing an inclusive evaluation framework that is infused with principles of symmetry

The gastrointestinal nematode Trichostrongylus colubriformis down-regulates immune gene expression in migratory cells in afferent lymph

Background: Gastrointestinal nematode (GIN) infections are the predominant cause of economic losses in sheep. Infections are controlled almost exclusively by the use of anthelmintics which has lead to the selection of drug resistant nematode strains. An alternative control approach would be the induction of protective immunity to these parasites. This study exploits an ovine microarray biased towards immune genes, an artificially induced immunity model and the use of pseudo-afferent lymphatic cannulation to sample immune cells draining from the intestine, to investigate possible mechanisms involved in the development of immunity.\ud \ud Results: During the development of immunity to, and a subsequent challenge infection with Trichostrongylus colubriformis, the transcript levels of 2603 genes of cells trafficking in afferent intestinal lymph were significantly modulated (P < 0.05). Of these, 188 genes were modulated more than 1.3-fold and involved in immune function. Overall, there was a clear trend for down-regulation of many genes involved in immune functions including antigen presentation, caveolar-mediated endocytosis and protein ubiquitination. The transcript levels of TNF receptor associated factor 5 (TRAF5), hemopexin (HPX), cysteine dioxygenase (CDO1), the major histocompatability complex Class II protein (HLA-DMA), interleukin-18 binding protein (IL-18BP), ephrin A1 (EFNA1) and selenoprotein S (SELS) were modulated to the greatest degree.\ud \ud Conclusions: This report describes gene expression profiles of afferent lymph cells in sheep developing immunity to nematode infection. Results presented show a global down-regulation of the expression of immune genes which may be reflective of the natural temporal response to nematode infections in livestock

Coronin-1A Links Cytoskeleton Dynamics to TCRαβ-Induced Cell Signaling

Actin polymerization plays a critical role in activated T lymphocytes both in regulating T cell receptor (TCR)-induced immunological synapse (IS) formation and signaling. Using gene targeting, we demonstrate that the hematopoietic specific, actin- and Arp2/3 complex-binding protein coronin-1A contributes to both processes. Coronin-1A-deficient mice specifically showed alterations in terminal development and the survival of αβT cells, together with defects in cell activation and cytokine production following TCR triggering. The mutant T cells further displayed excessive accumulation yet reduced dynamics of F-actin and the WASP-Arp2/3 machinery at the IS, correlating with extended cell-cell contact. Cell signaling was also affected with the basal activation of the stress kinases sAPK/JNK1/2; and deficits in TCR-induced Ca2+ influx and phosphorylation and degradation of the inhibitor of NF-κB (IκB). Coronin-1A therefore links cytoskeleton plasticity with the functioning of discrete TCR signaling components. This function may be required to adjust TCR responses to selecting ligands accounting in part for the homeostasis defect that impacts αβT cells in coronin-1A deficient mice, with the exclusion of other lympho/hematopoietic lineages

Abstract

The approach of learning of multiple ”related ” tasks simultaneously has proven quite successful in practice; however, theoretical justification for this success has remained elusive. The starting point of previous work on multiple task learning has been that the tasks to be learnt jointly are somehow ”algorithmically related”, in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar. We take a logical step backwards and offer a data generating mechanism through which our notion of task-relatedness is defined. We provide a formal framework for task relatedness that captures a certain sub-domain of the wide scope of issues in which one may apply a multiple task learning approach. Our notion of similarity between tasks is relevant to a variety of real life multi-task learning scenarios and allows the formal derivation of strong generalization bounds (bounds that are strictly stronger than the previously known bounds for both the learning-to-learn and the multi-tasklearning scenarios). We provide general conditions under which our bounds guarantee smaller sample size per task than the known bounds for the single task learning approach.