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Strategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual search and has limited scope. This paper proposes a text-mining framework, ACRank, that automatically extracts alliances from news articles. ACRank aims to provide human analysts with a higher coverage of strategic alliances compared to existing databases, yet maintain a reasonable extraction precision. It has the potential to discover alliances involving less well-known companies, a situation often neglected by commercial databases.

The proposed framework is a systematic process of alliance extraction and validation using natural language processing techniques and alliance domain knowledge. The process integrates news article search, entity extraction, and syntactic and semantic linguistic parsing techniques. In particular, Alliance Discovery Template (ADT) identifies a number of linguistic templates expanded from expert domain knowledge and extract potential alliances at sentence-level. Alliance Confidence Ranking (ACRank)further validates each unique alliance based on multiple features at document-level. The framework is designed to deal with extremely skewed, noisy data from news articles.

In evaluating the performance of ACRank on a gold standard data set of IBM alliances (2006–2008) showed that: Sentence-level ADT-based extraction achieved 78.1% recall and 44.7% precision and eliminated over 99% of the noise in news articles. ACRank further improved precision to 97% with the top20% of extracted alliance instances. Further comparison with Thomson Reuters SDC database showed that SDC covered less than 20% of total alliances, while ACRank covered 67%. When applying ACRank to Dow 30 company news articles, ACRank is estimated to achieve a recall between 0.48 and 0.95, and only 15% of the alliances appeared in SDC.

The research framework proposed in this paper indicates a promising direction of building a comprehensive alliance database using automatic approaches. It adds value to academic studies and business analyses that require in-depth knowledge of strategic alliances. It also encourages other innovative studies that use text mining and data analytics to study business relations.

Rather than an attempt to define or clarify resilience in terms of its characteristics, or its correspondence to reality, this paper is a series of reflections that leads to the notion of resilience as a way of being in the world. What is presented is not intended to be conclusive in any way. The paper aims to trigger thoughtful reflections that will lead to further conversations about the entailments and ethical concerns implicit in the concept of resilience.

The paper comprises reflections based on a selection of published ideas, as well as on personal engagement in the domains of ecology and the biology of cognition. In postulating that resilience may have a central, ineffable quality, the phrase “soul of” has been used as an approach to considering what such a quality may be. This formulation leads to a consideration about the nature of our relationships with the other such that the notion of resilience has become meaningful.

Language parses or chunks named ideas in a way that reveals some regularities and obscures others. The word resilience is one such chunk that historically has changed its meaning. Furthermore in the present the word refers to variable aspects or elements depending on the context of its usage. In the ecological context, resilience entails an ambiguous balance between persistence and change, and indeed this ambiguity may be necessary to accommodate the complexities. In all cases the author has considered, we are only concerned with resilience where there is an ethical dimension and this, inherently, must be multidimensional given the complexities of the systems being regarded. The author claims that the soul of resilience arises through an ethically oriented reflexive awareness of our dynamic flow in a relational embeddedness.

The practical implications of this paper are elusive, yet meaningful, as the paper attempts to open space for operating in an ethical and useful manner with ambiguous concepts and for avoiding the hubris of certainty. The paper is an explication of one of the ways of thinking inspired by second order science.

As the reflections in this paper are largely those original to the author, the ideas are original. Whether they have value depends on what ideas, attitudes, orientations and further reflections are triggered and whether these in turn implicitly or explicitly alter actions toward more effective care of our world. If reflection on resilience increases resilience, then this paper will have value.

This study aims to explore, illuminate and hence evoke further reflections on the implications of creating and conserving distinctions that inherently act as simplifications and limit appropriate action.

The approach used was reflective regarding the chosen concept of designing and learning from the perspective of a constitutive epistemology. These were investigated as circularities and as distinctions in language. The variety of intended meanings and hence implicit entailments was examined from the perspective of implicit domains.

A tendency to focus on the results of designing and learning rather than the processes was attributed to several factors including cultural relevance, tangibility, durability and observability. Further, it was found that result and process are arbitrary distinctions in a circular system. It was noted that lack of awareness of multiple domains encourages reification, and that distinctions inherently obscure what happens in the non-articulated aspects of living. However, expertise embraces an ability to attend to such “betweens”. This applies to expertise in the assessment of learning and designing.

The most obvious value of the findings is for the field of education. The insights gained indicate that the path of individualized learning with an emphasis on attention to the processes, inclusive of those that are not distinguished and named but can, with reflective experience, be sensed and acted on, has deep epistemological roots. A further implication is that educators require expertise to effectively work with learners, and that effective assessment depends on recurrent conversational interactions between the educator and learner.

To propose a categorization of the different conflation procedures at the two basic approaches, non‐linguistic and linguistic techniques, and to justify the application of normalization methods within the framework of linguistic techniques.

Presents a range of term conflation methods, that can be used in information retrieval. The uniterm and multiterm variants can be considered equivalent units for the purposes of automatic indexing. Stemming algorithms, segmentation rules, association measures and clustering techniques are well evaluated non‐linguistic methods, and experiments with these techniques show a wide variety of results. Alternatively, the lemmatisation and the use of syntactic pattern‐matching, through equivalence relations represented in finite‐state transducers (FST), are emerging methods for the recognition and standardization of terms.

The survey attempts to point out the positive and negative effects of the linguistic approach and its potential as a term conflation method.

Outlines the importance of FSTs for the normalization of term variants.

Many mobile devices today are equipped with diversified sensors that enable the acquisition of rich user context (e.g. GPS location, phone activity) for application utilization. With the growing usage of mobile devices in daily life, the problem of conveniently and promptly searching a piece of content that a user has viewed on his/her device before becomes more and more crucial. This paper aims to propose a context‐based query processing framework called UCQP that supports unstructured queries for content search in a user's access history.

Beyond the keywords related to the content properties, a context query in the framework is specified with freeform phrases that describe high‐level mobile contexts of the user at a previous time when the user viewed the searched content.

Experimental results on a prototype system of the framework illustrate its good accuracy and small response time.

To tolerate the incompleteness and inaccuracy in user query texts caused by fading human memory, the authors develop several semantic query parsers that are tailored for different types of contexts using natural language processing and information retrieval techniques. The authors further propose a similarity model to rank the multiple result contents of a query by comparing context entities specified in the query and historical context values associated with each result.

The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion.

The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains.

The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques.

The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks.

The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

The purpose of this research is to develop a new slicing scheme for the emerging cooperative three-dimensional (3D) printing platform that has multiple mobile 3D printers working together on one print job.

Because the traditional lay-based slicing scheme does not work for cooperative 3D printing, a chunk-based slicing scheme is proposed to split the print job into chunks so that different mobile printers can print different chunks simultaneously without interfering with each other.

A chunk-based slicer is developed for two mobile 3D printers to work together cooperatively. A simulator environment is developed to validate the developed slicer, which shows the chunk-based slicer working effectively, and demonstrates the promise of cooperative 3D printing.

For simplicity, this research only considered the case of two mobile 3D printers working together. Future research is needed for a slicing and scheduling scheme that can work with thousands of mobile 3D printers.

The research findings in this work demonstrate a new approach to 3D printing. By enabling multiple mobile 3D printers working together, the printing speed can be significantly increased and the printing capability (for multiple materials and multiple components) can be greatly enhanced.

The chunk-based slicing algorithm is critical to the success of cooperative 3D printing, which may enable an autonomous factory equipped with a swarm of autonomous mobile 3D printers and mobile robots for autonomous manufacturing and assembly.

This work presents a new approach to 3D printing. Instead of printing layer by layer, each mobile 3D printer will print one chunk at a time, which provides the much-needed scalability for 3D printing to print large-sized object and increase the printing speed. The chunk-based approach keeps the 3D printing local and avoids the large temperature gradient and associated internal stress as the size of the print increases.

Experimental findings not suiting the guided search model (GSM) of Cave and Wolfe (1990) and Wolfe (1994) and the modified guided search model (mGSM) of Efron and Yund (1996) are reviewed. Assumptions which explain these discrepancies are added. Thus two possible alternative models are obtained. According to both models the preattentional parallel processing stage of the GSM is related to the right hemisphere, while the attentional stage is related to the left hemisphere. Experiments determining whether one of these models is correct are suggested.

Aiming at the problems of weak generalization of robot imitation learning methods and higher accuracy requirements of low-level detectors, this study aims to propose an imitation learning method based on structural grammar.

The paper proposes a hybrid training model based on artificial immune algorithm and the Baum–Welch algorithm to extract the action information of the demonstration activity to form the {action-object} sequence and extract the symbol description of the scene to form the symbol primitives sequence. Then, probabilistic context-free grammar is used to characterize and manipulate these sequences to form a grammar space. Minimum description length criteria are used to evaluate the quality of the grammar in the grammar space, and the improved beam search algorithm is used to find the optimal grammar.

It is found that the obtained general structure can parse the symbol primitive sequence containing noise and obtain the correct sequence, thereby guiding the robot to perform more complex and higher-order demonstration tasks.

Using this strategy, the robot completes the fourth-order Hanoi tower task has been verified.

An imitation learning method for robots based on structural grammar is first proposed. The experimental results show that the method has strong generalization ability and good anti-interference performance.

Current segmentation systems almost invariably focus on linear segmentation and can only divide text into linear sequences of segments. This suits cohesive text such as news feed but not coherent texts such as documents of a digital library which have hierarchical structures. To overcome the focus on linear segmentation in document segmentation and to realize the purpose of hierarchical segmentation for a digital library’s structured resources, this paper aimed to propose a new multi-granularity hierarchical topic-based segmentation system (MHTSS) to decide section breaks.

MHTSS adopts up-down segmentation strategy to divide a structured, digital library document into a document segmentation tree. Specifically, it works in a three-stage process, such as document parsing, coarse segmentation based on document access structures and fine-grained segmentation based on lexical cohesion.

This paper analyzed limitations of document segmentation methods for the structured, digital library resources. Authors found that the combination of document access structures and lexical cohesion techniques should complement each other and allow for a better segmentation of structured, digital library resources. Based on this finding, this paper proposed the MHTSS for the structured, digital library resources. To evaluate it, MHTSS was compared to the TT and C99 algorithms on real-world digital library corpora. Through comparison, it was found that the MHTSS achieves top overall performance.

With MHTSS, digital library users can get their relevant information directly in segments instead of receiving the whole document. This will improve retrieval performance as well as dramatically reduce information overload.

This paper proposed MHTSS for the structured, digital library resources, which combines the document access structures and lexical cohesion techniques to decide section breaks. With this system, end-users can access a document by sections through a document structure tree.