Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category

Research on Ship Classification using Faster Region Convolutional Neural Network for Port Security

Huvudsyftet med studien var att se i vilken grad det går att finna samarbeten genom material- och/eller energiutbyten mellan närliggande anläggningar inom skogsindustrin i Sverige. Genom att göra en inventering av vilka anläggningar som finns inom skogsindustrin och sedan kontakta dessa, sammanställdes en lista över de olika anläggningarna och deras olika samarbeten. Inventeringen gjordes med hjälp av olika branschorganisationer samt sökmotorer på Internet. Utöver detta besöktes också fyra intressanta fall för att ge en inblick i hur dessa samarbeten kan se ut. Studien visar på att den här typen av samarbeten existerar inom skogsindustrin och att drygt en tredjedel av de studerade anläggningarna har någon form av samarbeten rörande dessa frågor. Detta pekar på att man inom skogsindustrin är långt framme när det gäller resursutnyttjande och att möjligheten att minimera sin energi- och materialanvändning hela tiden är en relevant fråga. Det finns med stor sannolikhet ännu fler sådana samarbeten som inte framkommit vid undersökningen och en intressant aspekt är att vid de besök som gjordes upptäcktes samarbeten som inte uppmärksammats vid tidigare kontakter. Av de 152 tillfrågade anläggningarna i inventeringen erhölls svar från 117 stycken vilket tyder på att det finns ett stort intresse för dessa frågor inom skogsindustrin. Flera av de anläggningar som inte hade några samarbeten kring dessa frågor svarade också att de hela tiden undersöker möjligheten till att inleda sådana. Många av samarbetena rörande dessa frågor kretsar kring leveranser av el och ånga samt spån och flis men en del andra intressanta samarbeten har också framkommit. Exempelvis används slam från bioreningsdammar till bränsle, jordförbättringsmedel och som täckmaterial vid deponier. Sammanfattningsvis tyder detta på att skogsindustrin ligger långt framme gällande dessa frågor men att det fortfarande finns mer att göra om energi- och materialanvändningen och därigenom den negativa miljöpåverkan ska minimeras.The aim and objective with this study was to investigate to what extent co-operation through material and energy exchange between adjacent industries among the forest industry in Sweden could be found. First, an inventory of the industries in the forest industry was conducted. Secondly, each company was contacted with questions concerning this issue. Complementary field studies of four specific cases were conducted in order to give an insight to how these co-operations may function in reality. The result of this study illustrates that co-operations among the industries exist in the forest industry sector as more than a third of the investigated industries has some kind of co-operation regarding material and energy exchange with adjacent industries. A total number of 152 industries were identified during the inventory phase and 117 of those industries participated in the study with their own answers. This high participation rate enhances the impression that these are important questions to the forest industry sector. Numerous of the co-operations mentioned revolve around electricity, steam, and by products from sawmills, like woodchips and sawdust. Nevertheless, a few other interesting co-operations have also been revealed during the study, for example; sludge from some of the pulp mills are used as fuel, soil fertilizer and as covering material at landfills. An interesting point is that co-operations, which not were discovered during the earlier correspondence with the industries, in fact were revealed during the field studies. Therefore, the probability that there are more existing co-operations between adjacent industries than the findings in the study reveals, are high. To sum up, this shows that the forest industry is well in advance regarding co-operation through material and energy exchange between adjacent industries. However, there is still a lot to be done if the negative effect on the environment from the forest industry should be minimised

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition