An artist's rendering of Japan's new National Stadium, which will become the main venue for the 2020 Summer Olympics in Tokyo. THE Olympic Games have long been used to showcase some of the world's newest technologies. From electronic stopwatches in Stockholm in 1912, to live television broadcasts in Berlin in 1936, to instant video replay at Salt Lake City in 2002 -- host cities have looked to stay at the cutting edge. But in four years, those innovations are going to look as ancient as the Games themselves as the most tech-savvy of nations -- Japan -- prepares to dazzle visitors and audiences across the globe with the most futuristic Olympics of them all. "The Olympic Games is a sports festival, but also it's a chance to show the innovation of scientific technologies," Tokyo's organising committee CEO Toshiro Muto said. "We have the potential to make this Olympic Games wonderful (and one) that the people of the world are going to admire."

Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the Automated Planning community in recent years. Hybrid domain models capture a more accurate representation of real world problems that involve continuous processes than is possible using discrete systems. However, solving problems represented as PDDL+ domains is very challenging due to the construction of complex system dynamics, including non-linear processes and events. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-linear system dynamics governing the continuous evolution of states. DiNo is based on the discretise-and-validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic, which is introduced in this paper. Although several planners have been developed to work with subsets of PDDL+ features, or restricted forms of processes, DiNo is currently the only heuristic planner capable of handling non-linear system dynamics combined with the full PDDL+ feature set.

As the rapid development of e-commerce, reputation model has been proposed to help customers make effective purchase decisions. However, most of reputation models focus only on the overall ratings of products without considering reviews which provided by customers. We believe that textual reviews provided by buyers can express their real opinions more honestly. As so, in this paper, based on word2vector model, we propose a Textual Reputation Model (TRM) to obtain useful information from reviews, and evaluate the trustworthiness of objective product. Experimental results on real data demonstrate the effectiveness of our approach in capturing reputation information from reviews.

This paper offers a perspective on Symbiotic Cognitive Systems that draws on Distributed Cognition. It argues that representations are the medium of cognition, and that the external representations that are one of the foci of Distributed Cognition are critical to supporting symbiosis. The paper analyzes an instance of a symbiotic cognitive system in which hundreds of human participants – with the support of a digital system – collectively optimize a program. It discusses the roles external representations play in symbiosis, and suggest that the design of external representations that are accessible and legible to both human and digital agents is a critical part of symbiotic cognitive systems.

Modeling occupant behavior in smart buildings to reduce energy usage in a more accurate fashion has garnered much recent attention in the literature. Predicting occupant comfort in buildings is a related and challenging problem. In some smart buildings, such as NASA AMES Sustainability Base, there are discrepancies between occupants' actual thermal discomfort and sensors based upon a weighted average of wet bulb, dry bulb, and mean radiant temperature intended to characterize thermal comfort. In this paper we attempt to find other contributing factors to occupant discomfort. For our experiment we use a dataset from a Building Automation System (BAS) in NASA Sustainability Base. We choose one conference room for our experiment and empirically establish the thermal discomfort level for the room's temperature sensor. We use various causality metrics and causal graphs to isolate candidate causes of the target room temperature. And we compare these feature sets according to their predictive capability of future instances of discomfort. Moreover, we establish a trade off between computational and statistical performance of adverse event prediction.

Automatic evaluation of text for its innovative quality has been necessitated by the growing trend to organize open innovation contests by different organizations. Such online/offline contests are known to fuel major business benefits to many industries. However, open contests result in a huge number of documents of which only a few may contain potentially interesting and relevant ideas. Usually these entries are manually reviewed and scored by multiple experts. But manual evaluation process not only require a lot of time and effort but are also prone to erroneous judgments due to inter-annotator disagreements. To counter this issue, in this paper, we have proposed a new approach towards detecting novelty or innovativeness of textual ideas from a given collection of ideas. The proposed approach uses information theoretic measures and term relevance to domain to compute document level innovativeness score. We have evaluated the performance of the proposed approach with a real world collection of innovative ideas which were manually scored by experts. We have compared the performance of our proposed model with some of the commonly used baseline approaches that rely on distributional semantics and geometric distances. The result shows that the proposed method outperform the existing baseline models.

Robust intelligent systems may leverage knowledge about the world to cope with a variety of contexts.While automatic knowledge extraction algorithms have been successfully used to build knowledge bases in English,little progress has been made in extracting non-alphabetic languages, e.g. Chinese.This paper identifies the key challenge in instance and pattern extraction for Chinese and presents the Coupled Chinese Pattern Learner that utilizes part-of-speech tagging and language-dependent grammar rules for generalized matching in the Chinese never-ending language learner framework for large-scale knowledge extraction from online documents.Experiments showed that the proposed system is scalable and achieves a precision of 79.9% in learning categories after a small number of iterations.

In this paper we describe the Unified Cybersecurity Ontology (UCO) that is intended to support information integration and cyber situational awareness in cybersecurity systems. The ontology incorporates and integratesheterogeneous data and knowledge schemas from different cybersecurity systems and most commonly usedcybersecurity standards for information sharing and exchange. The UCO ontology has also been mapped to anumber of existing cybersecurity ontologies as well asconcepts in the Linked Open Data cloud (Berners-Lee,Bizer, and Heath 2009). Similar to DBpedia (Auer etal. 2007) which serves as the core for general knowledge in Linked Open Data cloud, we envision UCO toserve as the core for cybersecurity domain, which wouldevolve and grow with the passage of time with additional cybersecurity data sets as they become available.We also present a prototype system and concrete usecases supported by the UCO ontology. To the best of ourknowledge, this is the first cybersecurity ontology thathas been mapped to general world ontologies to support broader and diverse security use cases. We comparethe resulting ontology with previous efforts, discuss itsstrengths and limitations, and describe potential futurework directions.

In intensive care units (ICU), electrocardiogram (ECG) waveforms show diverse variationsunder different patients' physical conditions.In general, physicians can diagnose patients efficientlyby detecting any disorder of heart rate or rhythm and any change in the morphological pattern of ECG data,which contain underlying semantics.To help physicians better analyze ECG data in a fairly short time,it is essential to develop a novel method for mining semantics from ECG patterns.This paper is the very first time to characterize ECG patterns by using Prefix Scalable Pattern Tree (PSP-Tree).Comparing with similar currently existing methods, PSP-Tree can mine significant semantics,such as scalability, temporality and hierarchy over ECG patterns.We conduct extensive experiments on real ECG data set which are obtained from PhysioBank Community and Beijing No.3 People Hospital.The experiment results show that our method performs more feasibly and effectively than other related work.

We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and context-counting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs.