Reviewerly/exHarmony huggingface dataset:
Logsonomy — A Search Engine Folksonomy

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.
Recommending Fresh URLs Using Twitter Lists

Recommender systems for social media have attracted considerable attentions due to its inherent features, such as a huge amount of information, social networks, and real-time features. In microblogs, which have been recognized as one of the most popular social media, most of URLs posted by users are considered to be fresh (i.e., shortly after creation). Hence, it is important to recommend URLs in microblogs for appropriate users because users become able to obtain such fresh URLs immediately. In this paper, we propose a URL recommender system using Twitter user lists. Twitter user list is the official functionality to group users into a list along with the name of it. Since it is expected that the members of a list (i.e., users included in the list) have similar characteristics, we utilize this feature to capture the user interests. Experimental results show that our proposed method achieves higher effectiveness than other methods based on the follow-followed network which does not offer user interests explicitly.
Simulation Vs. Real Time Control; With Applications To Robotics And Neural Networks

Simulations are often used to model real physical systems prior to electrical, mechanical, and computer hardware development.This allows engineers and scientists to experiment with various concepts before committing time and effort into hardware.Simulations can also be run concurrently with real-time systems to build knowledge of the environment that the real-time system is operating in, then provide feedback to the system to optimize its performance.For both of these types of simulations, the simulation must accurately model the real physical system.A comparison of simulations to real-time controlled physical systems is illustrated in this paper using several simple robotic and artificial Neural Network examples.The robotics examples show how real-time control of mobile robots and robotic-arms, and the resultant governing equations and software algorithms can provide several interesting simulation problems to overcome if the simulation is to accurately model the physical system.The Neural Network example demonstrates how computational speed and numerical precision can become an issue when comparing simulations to real-time Neural Network hardware.In general, comparison of simulations to real physical systems often enhances understanding of the underlying governing principles and equations, and results in simulations that accurately model the real world.
Generating Coherent Summaries with Textual Aspects

Initiated by TAC 2010, aspect-guided summaries not only address specific user need, but also ameliorate content-level coherence by using aspect information. This paper presents a full-fledged system composed of three modules: finding sentence-level textual aspects, modeling aspect-based coherence with an HMM model, and selecting and ordering sentences with aspect information to generate coherent summaries. The evaluation results on the TAC 2011 datasets show the superiority of aspect-guided summaries in terms of both information coverage and textual coherence.
Shape-Keeping Technique and Its Application to Checkmate Problem Composition

The checkmate problem in Shogi (Japanese Chess) is a puzzle within the game itself. These puzzles have enjoyed a long play and have been the subject of centuries of analysis. The subject of this research is defining the aesthetic criteria of great Shogi problems, and finding new methods for composing interesting checkmate problems in Shogi. First we examine the results of previous studies of aesthetics in Shogi checkmate problems. For this purpose, we focus on the Proof Number Search algorithm and record the data while solving checkmate problems. We analyzed these data and we calculated the proof number related to the evaluation of the checkmate problem. Good checkmate problems have large proof numbers. Next, we present a new technique for automatic composition of checkmate problems in Shogi. This technique uses already existing checkmate problems in Shogi and develops them further. Finally, we can compose new checkmate problems which have bigger proof numbers than original ones. This work is not yet sufficient unto itself.
Music Design with Audio Oracle Using Information Rate

In the proposed demo we will present a method for design of musical composition using the Audio Oracle (AO) - a machine improvisation method that constructs and produces variation from a music recording using a graph of repeated factors found in the recording. The compositional / improvisation use of AO involves controlling the rate of recombination, the length of common history (length of the repeated factors) and selection of regions in the AO where the machine operates. One of the challenges in working with AO is understanding the generative potential of different audio materials and marking and allocating regions in a recording where AO should operate to achieve the desired musical result. Recently we introduced a novel analysis method "on top" of the AO structure that captures aspects of order and complexity that we call Information Rate. This measure, belonging broadly to a field of study know as Musical Information Dynamics, is related to Bense formulation of aesthetics in terms of entropy and compression. In the demo we will briefly explain the theory behind Audio Oracle and Information Rate and demonstrate a process of designing a composition / structured improvisation based on analysis of the AO graph. In contrast to the usual live interaction method where both the audio input to the oracle and the improvisation are done "on the fly", in this presentation the audio analysis will be done prior to the presentation, while during the talk we will show the process of planning a composition and then do a live performance with the AO based on this design.
Coupling Spatiotemporal Disease Modeling with Diagnosis

Modelling the density of an infectious disease in space and time is a task generally carried out separately from the diagnosis of that disease in individuals. These two inference problems are complementary, however: diagnosis of disease can be done more accurately if prior information from a spatial risk model is employed, and in turn a disease density model can benefit from the incorporation of rich symptomatic information rather than simple counts of presumed cases of infection. We propose a unifying framework for both of these tasks, and illustrate it with the case of malaria. To do this we first introduce a state space model of malaria spread, and secondly a computer vision based system for detecting plasmodium in microscopical blood smear images, which can be run on location-aware mobile devices. We demonstrate the tractability of combining both elements and the improvement in accuracy this brings about.
Fine’s Theorem on First-Order Complete Modal Logics

Fine’s influential Canonicity Theorem states that if a modal logic is determined by a first-order definable class of Kripke frames, then it is valid in its canonical frames. This article reviews the background and context of this result, and the history of its impact on further research. It then develops a new characterization of when a logic is canonically valid, providing a precise point of distinction with the property of first-order completeness. The ultimate point is that the construction of the canonical frame of a modal algebra does not commute with the ultrapower construction.
Complex Task Learning from Unstructured Demonstrations

Much work in learning from demonstration has focused on learning simple tasks from structured demonstrations that have a well-defined beginning and end. As we attempt to scale robot learning to increasingly complex tasks, it becomes intractable to learn task policies monolithically. Furthermore, it is desirable to be able to learn from natural, unstructured demonstrations, which are unsegmented, possibly incomplete, and may come from different tasks. We propose a three-part approach to designing a natural, scalable system that allows a robot to learn tasks of increasing complexity by automatically building and refining a library of skills over time. First, we describe a Bayesian nonparametric model that can segment unstructured demonstrations into appropriate numbers of component skills and recognize repeated skills across demonstrations and tasks. These skills can then be parameterized and generalized to new situations. Second, we propose to create a system that allows the user to provide unstructured corrections and feedback to the robot, without requiring any knowledge of the robot's underlying representation of the task or its component skills. Third, we propose to infer the user's intentions for each segmented skill and autonomously improve these skills using reinforcement learning. This approach will be applied to learn and generalize complex, multi-step tasks that are beyond the reach of current LfD methods, using the PR2 mobile manipulator as a testing platform.
Combating Threats to Collective Attention in Social Media: An Evaluation

Breaking news, viral videos, and popular memes are all examples of the collective attention of huge numbers of users focusing in large-scale social systems. But this self-organization, leading to user attention quickly coalescing and then collectively focusing around a phenomenon, opens these systems to new threats like collective attention spam. Compared to many traditional spam threats, collective attention spam relies on the insidious property that users themselves will intentionally seek out the content where the spam will be encountered, potentially magnifying its effectiveness. Our goal in this paper is to initiate a study of this phenomenon. How susceptible are social systems to collective attention threats? What strategies by malicious users are most effective? Can a system automatically inoculate itself from emerging threats? Towards beginning our study of these questions, we take a two fold approach. First, we develop data-driven models to simulate large-scale social systems based on parameters derived from a real system. In this way, we can vary parameters — like the fraction of malicious users in the system, their strategies, and the countermeasures available to system operators — to explore the resilience of these systems to threats to collective attention. Second, we pair the data-driven model with a comprehensive evaluation over a Twitter system trace, in which we evaluate the effectiveness of countermeasures deployed based on the first moments of a bursting phenomenon in a real system. Our experimental study shows the promise of these countermeasures to identifying threats to collective attention early in the lifecycle, providing a shield for unsuspecting social media users.
On the k-Anonymization of Time-Varying and Multi-Layer Social Graphs

The popularity of online social media platforms provides an unprecedented opportunity to study real-world complex networks of interactions. However, releasing this data to researchers and the public comes at the cost of potentially exposing private and sensitive user information. It has been shown that a naive anonymization of a network by removing the identity of the nodes is not sufficient to preserve users' privacy. In order to deal with malicious attacks, k-anonymity solutions have been proposed to partially obfuscate topological information that can be used to infer nodes' identity. In this paper, we study the problem of ensuring k-anonymity in time-varying graphs, i.e., graphs with a structure that changes over time, and multi-layer graphs, i.e., graphs with multiple types of links. More specifically, we examine the case in which the attacker has access to the degree of the nodes. The goal is to generate a new graph where, given the degree of a node in each (temporal) layer of the graph, such a node remains indistinguishable from other k-1 nodes in the graph. In order to achieve this, we find the optimal partitioning of the graph nodes such that the cost of anonymizing the degree information within each group is minimum. We show that this reduces to a special case of a Generalized Assignment Problem, and we propose a simple yet effective algorithm to solve it. Finally, we introduce an iterated linear programming approach to enforce the realizability of the anonymized degree sequences. The efficacy of the method is assessed through an extensive set of experiments on synthetic and real-world graphs.
Reaching Cognitive Consensus with Improvisational Agents

A common approach to interactive narrative involves imbuing the computer with all of the potential story pre-authored story experiences (e.g. as beats, plot points, planning operators, etc.). This has resulted in an accepted paradigm where stories are not created by or with the user; rather, the user is given piecemeal access to the story from the gatekeeper of story knowledge: the computer (e.g. as an AI drama manager). This article describes a formal process that provides for the equal co-creation of story-rich experiences, where neither the user nor computer is in a privileged position in an interactive narrative. It describes a new formal approach that acts as a first step for the real-time co-creation of narrative in games that rely on the negotiated shared mental model between a human actor and an AI improv agent.
The potentialities of corpus-based techniques for analyzing literature

This paper presents an attempt to explore the analytical potential of five corpus-based techniques: concordances, frequency lists, keyword lists, collocate lists, and dispersion plots. The basic question addressed is related to the contribution that these techniques make to gain more objective and insightful knowledge of the way literary meanings are encoded and of the way the literary language is organized. Three sizable English novels (Joyc's Ulysses, Woolf's The Waves, and Faulkner's As I Lay Dying) are laid to corpus linguistic analysis. It is only by virtue of corpus-based techniques that huge amounts of literary data are analyzable. Otherwise, the data will keep on to be not more than several lines of poetry or short excerpts of narrative. The corpus-based techniques presented throughout this paper contribute more or less to a sort of rigorous interpretation of literary texts far from the intuitive approaches usually utilized in traditional stylistics.
Chinglish in Chinese-English Translation

English, as an international language, plays an important role in the communication between China and foreigners. However, the existence of Chinglish has a very bad effect on the quality of Chinese-English translation. So it is quite necessary to study Chinglish. To start with, this thesis introduces the definition of Chinglish on the basis of the former researches. Then, the causes of Chinglish are discussed. The thesis hopes that it can help people to improve the quality of the translation so as to communicate more effectively.
Opportunities And Challenges To Developing A Bachelor's Degree Program In Information Technology

The rapidly changing Northern Virginia information technology (IT) workforce has a continuing need for competent, effective IT professionals.Competent professionals are well grounded in both the principles and practice of IT and are able to take care of standard requests from their customers.George Mason University's School of Information Technology and Engineering (IT&E) is committed to continually reviewing, evaluating, and improving its curricula so as to meet the changing needs of the Northern Virginia area and the nation.While no silver-bullet solution to the IT worker shorter exists, multiple and creative ways to attack this problem are needed.One source of IT labor that is not being fully exploited is the potentials for movement of people who are already in the workforce, many who have non-traditional technical preparation, into the ranks of the IT professional.Traditionally, this has required returning to college for a second bachelor's or graduate degree in an IT-related field.Completing such a program while remaining employed typically takes from four to six years and a correspondingly high level of dedication and sacrifice.There are many more who are currently working in a technical field who would make this move to become an IT professional if there were more reasonable and practical ways to accomplish it.This Bachelor's of Science in Information Technology (BS IT) program fills the gap between training/associate's degree programs and graduate programs.This paper discusses challenges and opportunities encountered while developing a curriculum to achieve these outcomes-a curriculum to educate industry-ready IT professionals.The curriculum is a four-year bachelor's degree program that focuses on areas of concentration during the last two years.This new undergraduate program is perfectly suited for the student who does not enter with a formal training in computing as well as for the student who might not have a love for theoretical aspects of the mathematical and natural sciences.The jobs that IT majors are expected to fill focus on the application of computer and communication technologies in other disciplines.The new BS IT curriculum is sufficiently flexible to serve existing, emerging, and future IT educational needs while preserving the integrity of existing degree programs in disciplines such as Page 7.899.
Path Planning in Dynamic Environments with Adaptive Dimensionality

Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic.
Evaluating Analogy-Based Story Generation: An Empirical Study

Evaluation is one of the major open problems in computational narrative. In this paper, we present an empirical study of SAM, an analogy-based story generation (ASG) algorithm, that was created as part of our Riu interactive narrative system. Specifically, our study focuses on SAM's capability to retrieve and generate short non-interactive stories. Combining qualitative and quantitative methods from different disciplines, the methodology in this study can be extended to evaluating other computational narrative systems.
Different News for Different Views: Political News-Sharing Communities on Social Media Through the UK General Election in 2015

Media exposure is a central concept in understanding the dynamics of public opinion and political change. Traditional models of media exposure have been severely challenged by the shift to online news consumption and news-sharing on social media. Here we use network analysis and automated content analysis to examine the interaction between news media and social media around the UK General Election in 2015. We study a large corpus of UK newspaper articles and Twitter content, finding significant temporal correlations between newspaper topic coverage and the content discussed on Twitter. We also identify news-sharing communities around groups of news sources that are ideologically clustered. Analysis of topics covered within each group shows that different communities are exposed to different news content during the election. Our results confirm that ideological bias and selective news-sharing affect patterns of online media exposure in social media.
A Study In Use Of Technology In Distance Education And On Line Learning

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2620 A Study in Use of Technology in Distance Education and On-line Learning Azim Danesh, Munir Mandviwalla, Chang Liu Pennsylvania State University at Harrisburg / Temple University /Shippensburg University Introduction With the advent of the Internet online/distance education gaining popularity, everyday more and more colleges are introducing new online courses. Computer and telecommunication technological advances have provided alternatives to the traditional classroom setting. Technology and interaction software are the basic blocks of the online courses. While distance education and online learning overcome many barriers and allow learners and educators many new opportunities over the traditional education, it also introduces many shortcomings and challenges. This paper will review different studies regarding communication, distance education and the use of technology in online courses. It will focus on technology issues in the area of interaction and communication. The processes involved and the problems that exist will be identified and discussed. As part of the research, a survey regarding these issues will be given to students who have tried online education and the results will be evaluated. Historical Definition and Background The concept of distance education is not new; universities have been offering correspondence courses since the nineteenth century (McIsaac & Gunawardena , 1996). In fact, the foundation of a correspondence course at the Illinois State University in 1874 has been credited as the start of distance education at the university level in the United States (Rumble & Harry, 1982). Distance education is primarily made up of four types of media: print, voice, video, and computer (Charles, 1991). Because of the sharp increase in the use of the computer, the use of distance education in higher education has increased dramatically. According to a U.S. Department of Education study (“ED Study..., “ 1997), about ninety percent of all higher education institutions with enrollments of ten thousand or more will be offering some form of distance education by the fall of 1998; seventy-six percent were offering distance education programs in the fall of 1995.