This paper proposes a current research agenda on crowdfunding from two different perspectives, mass media and geography. It is believed that these two elements must exert some kind of influence on the dynamics of the investments made in that market. Semantic analysis of mass news can be a useful tool for investors to assess their exposure to risk as well as help predict financial returns. Geography, on the other hand, can be used on the origin of the capital contributions and, therefore, present information on the location and regional characteristics of the investors.
Microblogging platforms like Twitter, in the recent years, have become one of the important sources of information for a wide spectrum of users. As a result, these platforms have become great resources to provide support for emergency management. During any crisis, it is necessary to sieve through a huge amount of social media texts within a short span of time to extract meaningful information from them. Extraction of emergency-specific information, such as topic keywords or landmarks or geo-locations of sites, from these texts plays a significant role in building an application for emergency management. This paper thus highlights different aspects of automatic analysis of tweets to help in developing such an application. Hence, it focuses on: (1) identification of crisis-related tweets using machine learning, (2) exploration of topic model implementations and looking at its effectiveness on short messages (as short as 140 characters); and performing an exploratory data analysis on short texts related to crises collected from Twitter, and looking at different visualizations to understand the commonality and differences between topics and different crisis-related data, and (3) providing a proof of concept for identifying and retrieving different geo-locations from tweets and extracting the GPS coordinates from this data to approximately plot them in a map.
In this paper, a learning from demonstration (LFD) approach is used to design an autonomous meal-assistance agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of the Gaussian mixture model (GMM) are learnt using Gaussian mixture regression (GMR) and expectation maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot’s end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.
There have been an enormous number of publications on cancer research. These unstructured cancer-related articles are of great value for cancer diagnostics, treatment, and prevention. The aim of this study is to introduce a recommendation system. It combines text mining (LDA) and semantic computing (GloVe) to understand the meaning of user needs and to increase the recommendation accuracy.
Although mereotopological relationship theories and their qualification problems have been extensively studied in R-2, the qualification of mereotopological relations in R-3 remains challenging. This is due to the limited availability of topological operators and high costs of boundary intersection tests. In this paper, a novel qualification technique for mereotopological relations in R-3 is presented. Our technique rapidly computes RCC-8 base relations using precomputed signed distance fields, and makes no assumptions with regards to complexity or representation method of the spatial entities under consideration.
Academic institutions often assess the efficacy of courses by surveying students. These surveys are critical in structuring course content and evaluating instruction. Given the critical function of surveys for academic institutions, it is essential that surveys obtain data which is precise and accurate. Currently most institutions construct surveys employing the Likert scale: questions that require students to map their opinion on precise topics to a discrete, quantitative domain. These surveys uniformly weight response data, irrespective of student interest. We argue greater accuracy may be obtained by building Student-Directed Discussion Surveys (SDDSs) — surveys with several open-ended, student-directed questions, requiring free text responses. SDDSs retain precision by employing several Natural Language Processing (NLP) techniques including word frequency and sentiment analysis. We use SDDSs to improve course content and evaluate survey accuracy by comparing the results of an SDDS to a Likert-scaled survey administered to an overlapping population. We find that the results of these two survey techniques diverge when topics become increasingly significant to respondents. These results, in addition to the documented issues with Likert-scaled surveys, lead to the conclusion that SDDSs may provide more informative and insightful results.
This article puts forward a process model for information gathering, planning and execution for decision making in relation to the technological approach in agricultural pest control. The proposed model was tested on aerial application of pesticides for pest control. In here, we propose a methodology based on action–research for implementation of a business, system, and technology model to assist and facilitate the collaborative use of resources and expertise, as well as to adjust one solution based on technological knowledge and management. A case study on pest control on agricultural landscapes in Brazil is presented to illustrate the results of this action–research approach in three different contexts. Its relevance may be measured by its benefits captured by the sharing of information within the networks, to facilitate the flows from information collected to improve the perception from the environment in the form of collective intelligence, so as to compare with previous cases stored in the knowledgebase, for formulation of action planning. Such arrangements pave the way for non-linear methods replacing attempts at objectivity, linear thought and control, as well as risks consideration to be integrated into a social computing system. The results demonstrate that such command-and-control system could be expanded for deployment in other sectors related to agriculture.
Online Analytical Processing (OLAP) is an effective approach to analyzing various complex business problems, and graph is considered as a common scheme to represent the business datasets. Network analysis is a broad analytics scheme for exploring the connectivity and deriving useful analytics results. However, network analysis for graph-based OLAP presents a set of more specific analytics methods by utilizing graph model, network property, and OLAP principles. In this paper, we present a comprehensive survey on network analysis conducted on graph model for the purpose of OLAP, and we summarize the current research focus, paradigms, and the future needs on the target technology.
The abundance and ubiquity of graphs (e.g., semantic knowledge graphs, such as Google’s knowledge graph, DBpedia; online social networks such as Google[Formula: see text], Facebook; bibliographic graphs such as DBLP, etc.) necessitates the effective and efficient search over them. Thus, we propose a novel keyword search paradigm, where the result of a search is an Object Summary (OS). More precisely, given a set of keywords that can identify a Data Subject (DS), our paradigm produces a set of OSs as results. An OS is a tree structure rooted at the DS node (i.e., a node containing the keywords) with surrounding nodes that summarize all data held on the graph about the DS. An OS can potentially be very large in size and therefore unfriendly for users who wish to view synoptic information about the data subject. Thus, we investigate the effective and efficient retrieval of concise and informative OS snippets (denoted as size-[Formula: see text] OSs). A size-[Formula: see text] OS is a partial OS containing [Formula: see text] nodes such that the summation of their importance scores results in the maximum possible total score. However, the set of nodes that maximize the total importance score may result in an uninformative size-[Formula: see text] OSs, as very important nodes may be repeated in it, dominating other representative information. In view of this limitation, we investigate the effective and efficient generation of two novel types of OS snippets, i.e., diverse and proportional size-[Formula: see text] OSs, denoted as DSize-[Formula: see text] and PSize-[Formula: see text] OSs. Namely, besides the importance of each node, we also consider its pairwise relevance (similarity) to the other nodes in the OS and the snippet. We conduct an extensive evaluation on two real graphs (DBLP and Google[Formula: see text]). We verify effectiveness by collecting user feedback, e.g., by asking DBLP authors (i.e., the DSs themselves) to evaluate our results. In addition, we verify the efficiency of our algorithms and evaluate quality of the snippets that they produce.
In the article, we describe a trajectory planning problem for a 6-DOF robotic manipulator arm that carries an ultra-wideband (UWB) radar sensor with synthetic aperture (SAR). The resolution depends on the trajectory and velocity profile of the sensor head. The constraints can be modelled as an optimization problem to obtain a feasible, collision-free target trajectory of the end-effector of the manipulator arm in Cartesian coordinates that minimizes observation time. For 3D-reconstruction, the target is observed in multiple height slices. For Through-the-Wall radar the sensor can be operated in sliding mode for scanning larger areas. For IED inspection the spot-light mode is preferred, constantly pointing the antennas towards the target to obtain maximum azimuth resolution.
Graph is a widely used scheme for representing complex datasets in terms of graphical illustration comprised of nodes and edges. Diffusion is a paradigm of propagating or transmitting substances or knowledge pieces from nodes to nodes. Diffusion analysis takes a slightly different approach from the reachability-based graph analysis; it takes the phenomenon as diffusion problems. In this paper, we present a technical survey on literatures of diffusion analysis.
It is not unusual that efforts to validate a statistical model exceed those used to build the model. Multiple techniques are used to validate, compare and contrast among competing statistical models: Some are concerned with a model’s ability to predict new data while others are concerned with model descriptiveness of the data. Without claiming to provide a comprehensive view of the landscape, in this paper we will touch on both aspects of model validation. There is much more to the subject and the reader is referred to any of the many classical statistical texts including the revised two volumes of Bickel and Docksum (2016), the one by Hastie, Tibshirani, and Friedman [The Elements of Statistical Learning: Data Mining, Inference, and Predication, 2nd edn. (Springer, 2009)], and several others listed in the bibliography.
An Ontology defines a common vocabulary across diverse platforms, supporting machine-interpretable semantics of the domain concepts along with associated relations. Ontologies support the definitions of a class which defines concepts in a domain and exist as the central focus. Proprietary Ontologies allow for internally developed machine-usable content to be leveraged for greater purpose. In this context, Industrial Ontologies can be considered first and foremost, an integration technology, supporting connections between any number of disparate data sources. By this application, it is possible to leverage proprietary and public ontologies thus supporting a Federated Ontology. This paper explores the current methods and technologies of federated Ontologies. To this goal, we summarize the current state of publically available ontologies examining how they are currently utilized, their application challenges and a realistic assessment of their potential.
This paper focuses on enhancing the mission duration by deploying secondary agents to coordinate with the primary agents to accomplish the mission with a minimal interruption. The interruption considered here is due to limited fuel carrying capability of primary agents. In this study, primary and secondary agents refer to unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), respectively. Conventionally, UAVs are refueled with the fixed main charging stations which lead to interruption during the ongoing mission. In this work, we propose two-stage density estimation approach for efficiently distributing the swarm of UGVs to act as mobile refueling stations for UAVs. In the first stage, the optimal number of UGVs and their initial placement are computed. In the final stage, the UGVs minimize the average distance for the nearest UAVs to refuel. The performance of the proposed method is compared with the state of the art. The numerical simulation shows a better performance with the distributed UGVs than the state of the art.
In this context, a model is an algorithm or a procedure that applies to data resulting in a functional relation [Formula: see text] between “input space” [Formula: see text] and “output space” [Formula: see text]. In this short paper, we will delineate objective criteria which help to disambiguate and rate models’ credibility. We will define pertinent concepts and will voice an opinion on the matter of good versus bad versus so–so models.
The paper illustrates a cognitive architecture for computational creativity based on the Psi model and the mechanisms inspired by dual-process theories of reasoning and rationality. In particular, three applications of computational creativity will be summed up: (a) a robot capable of executing creative paintings through a multilayer mechanism that implements an associative memory and is capable to properly mix elements belonging to different domains; (b) a robot aimed at producing a collage formed by a mix of photo-montage and digital collage: the artwork is created after a visual and verbal interaction with a human user; and (c) a humanoid robot capable of improvising a dancing choreography in real-time according to the listened music. First results in computational creativity show a set of potentialities to be explored that can shed light on human and artificial creativities and artificial intelligent systems.
Word-play is as powerful learning and motivation tool often used by educators for teaching the ability of reading, which is a complex activity. In this paper, we introduce a system that exploits a Pepper humanoid robot acting as a playfellow in a word-play game based on portmanteau words. The robot shows the ability to play with children using a conversation engine, a portmanteau creation engine, and a definition engine. In this manner, Pepper can integrate itself within a group of kids, and it can support a teacher in her activities. The humanoid can be involved in a word-based round-game in which it can play the role of either answerer or generator of new words.
Designing a new Analytics programF requires not only identifying needed courses, but also tying the courses together into a cohesive curriculum with an overriding theme. Such a theme helps to determine the proper sequencing of courses and create a coherent linkage between different courses often taught by faculty staff from different domains. It is common to see a program with some courses taught by computer science faculty, other courses taught by faculty and staff from the statistics department, and others from operations research, economics, information systems, marketing or other disciplines. Applying an overriding theme not only helps students organize their learning and course planning, but it also helps the teaching faculty in designing their materials and choosing terminology. The InfoQ framework introduced by Kenett and Shmueli provides a theme that focuses the attention of faculty and students on the important question of the value of data and its analysis with flexibility that accommodates a wide range of data analysis topics. In this chapter, we review a number of programs focused on analytics and data science content from an InfoQ perspective. Our goal is to show, with examples, how the InfoQ dimensions are addressed in existing programs and help identify best practices for designing and improving such programs. We base our assessment on information derived from the program’s web site.
Goodness-of-fit is used for the evaluation a model. They are commonly used to compare among competing models. The material is mostly classic. For more on the subject the reader is referred to the References including the two revised volumes Bickel and Docksum (2016).
The rapid advances in robotics have recently led to the developments of a wide range of robotic platforms that exhibit significant differences at the hardware components level. Consequently, this poses a significant challenge to robot software developers since they have to know how every hardware device in the robot works to ensure their software’s compatibility when transferring/reusing their code on different robots. In this paper we present a new Robot Hardware Abstraction Layer (R-HAL) that permits to seamlessly program and control any robotic platform powered by the XBot control software framework. The implementation details of the R-HAL are introduced. The R-HAL is extensively validated through simulation trials and experiments with a wide range of dissimilar robotic platforms, among them the COMAN and WALK-MAN humanoids, the KUKA LWR and the CENTAURO upper body. The results attained demonstrate in practice the gained benefits in terms of code compatibility, reuse and portability, and finally unified application programming even for robots with significantly diverse hardware.
