Question: Divide and refine: utilizing clusters for efficient evaluation of brief responses at scale.

Enhanced Peer Assessment Algorithms for Improved Accuracy in MOOCs

In the realm of massive open-access online courses (MOOCs), peer grading has emerged as a vital mechanism for managing the grading of intricate, open-ended tasks in classes with an enrollment of tens to hundreds of thousands of students. However, despite its early successes, peer grading often falls short in terms of accuracy when compared to evaluations by human experts. This paper presents advanced algorithms designed to quantify and adjust for grader biases and reliabilities, resulting in a marked enhancement in the accuracy of peer grading. This is achieved through analysis of extensive real-world data, encompassing 63,199 peer evaluations from Coursera's Human-Computer Interaction (HCI) course offerings, the largest dataset of peer grading networks ever analyzed. The algorithms also explore correlations between grader biases and reliabilities and various student attributes, such as engagement levels, performance metrics, and commenting styles. Furthermore, the paper demonstrates that these models can facilitate more strategic matching of graders to students, thereby optimizing the peer assessment process.

In this paper, we present a simple and efficient Context-Aware Bandit (CAB) algorithm. With CAB we attempt to craft a bandit algorithm that can capture collaborative effects and that can be easily deployed in a real-world recommendation system, where the multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-aware clustering technique augmenting exploration-exploitation strategies. CAB dynamically clusters the users based on the content universe under consideration. We provide a theoretical analysis in the standard stochastic multi-armed bandits setting. We demonstrate the efficiency of our approach on production and real-world datasets, showing the scalability and, more importantly, the significantly increased prediction performance against several existing state-of-theart methods.

This system demonstration paper presents IRIS (Informal Response Interactive System), a chat-oriented dialogue system based on the vector space model framework. The system belongs to the class of examplebased dialogue systems and builds its chat capabilities on a dual search strategy over a large collection of dialogue samples. Additional strategies allowing for system adaptation and learning implemented over the same vector model space framework are also described and discussed.

The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct differences between the LRFs (including magnitudes and directions) of the data points close to the cluster centers and at the boundary of the clusters. To capture these differences efficiently, two new local measures named centrality and coordination are further investigated. Based on empirical observations, two new clustering methods called local gravitation clustering and communication with local agents are designed, and several test cases are conducted to verify their effectiveness. The experiments on synthetic data sets and real-world data sets indicate that both clustering approaches achieve good performance on most of the data sets.

This paper presents the design of an MPI-based parallel and distributed machine learning platform on large-scale HPC clusters. Researchers and practitioners can implement easily a class of parallelizable machine learning algorithms on the platform, or port quickly an existing non-parallel implementation of a parallelizable algorithm to the platform with only minor modifications. Complicated functions in parallel programming such as scheduling, caching and load balancing are handled automatically by the platform. The platform performance was evaluated in a series of stress tests by using a k-means clustering task on 7,500 hours of speech data (about 2.7 billion 52dimensional feature vectors). Good scalability is demonstrated on an HPC cluster with thousands of CPU cores.

Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor, which we call BRIEF, on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.

Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/ approximately maqin/bicluster. A server version of QUBIC is also available upon request.

Summary form only given. The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.

The amount of controversial issues being discussed on the Web has been growing dramatically. In articles, blogs, and wikis, people express their points of view in the form of arguments, i.e., claims that are supported by evidence. Discovery of arguments has a large potential for informing decision-making. However, argument discovery is hindered by the sheer amount of available Web data and its unstructured, free-text representation. The former calls for automatic text-mining approaches, whereas the latter implies a need for manual processing to extract the structure of arguments. In this paper, we propose a crowdsourcing-based approach to build a corpus of arguments, an argumentation base, thereby mediating the trade-off of automatic text-mining and manual processing in argument discovery. We develop an end-to-end process that minimizes the crowd cost while maximizing the quality of crowd answers by: (1) ranking argumentative texts, (2) pro-actively eliciting user input to extract arguments from these texts, and (3) aggregating heterogeneous crowd answers. Our experiments with real-world datasets highlight that our method discovers virtually all arguments in documents when processing only 25% of the text with more than 80% precision, using only 50% of the budget consumed by a baseline algorithm.

Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that "look like" good communities for the application of interest.
 In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.

We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints. Furthermore, we utilize critical non-textual clues such as time between two consecutive posts and people mentions within the posts. To ascertain the effectiveness of the proposed method, we have collected data from public Slack conversations and Fresco, a proprietary platform deployed inside our organization. Experiments demonstrate that the proposed method yields an absolute (relative) improvement of as high as 3.23% (11.25%). To facilitate future research, we are releasing manual annotations for segmentation on public Slack conversations.


Question: Divide and evaluate: utilizing clusters to assess brief responses in large quantities.

This paper investigates the pivotal role of retrieval practice in the consolidation of learning, challenging the conventional notion that learning culminates with the correct answer to a question. In our study, participants in one group learned foreign language vocabulary through repeated cycles of study and test. In contrast, three other groups, upon correctly recalling a vocabulary item, either continued studying it but ceased testing, proceeded with testing it but discontinued studying, or halted both studying and testing. Our findings revealed that continuous studying post-learning had negligible impact on long-term recall. Conversely, persistent testing post-learning yielded a substantial positive effect on delayed recall. Moreover, the study uncovered a disconnect between students' predictions of their performance and their actual performance, indicating that even university-level learners might be unaware of the significance of retrieval practice in solidifying knowledge. These results underscore the critical importance of retrieval in learning and highlight the potential for educational strategies that emphasize testing as a means to enhance learning outcomes.

Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that "look like" good communities for the application of interest.
 In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.

Crisis informatics investigates how society's pervasive access to technology is transforming how it responds to mass emergency events. To study this transformation, researchers require access to large sets of data that because of their volume and heterogeneous nature are difficult to collect and analyze. To address this concern, we have designed and implemented an environment - EPIC Analyze - that supports researchers with the collection and analysis of social media data. Our research has identified the types of components - such as NoSQL, MapReduce, caching, and search - needed to ensure that these services are reliable, scalable, extensible, and efficient. We describe the design challenges encountered - such as data modeling, time vs. Space tradeoffs, and the need for a useful and usable system - when building EPIC Analyze and discuss its scalability, performance, and functionality.

Word-sized visualizations for eye movement data allow analysts to compare a variety of experiment conditions or participants at the sametime. Weimplementedasetofsuchword-sizedvisualizations as part of an analysis framework. We want to ﬁnd out which of the visualizations is most suitable for different analysis tasks. To this end, we applied the framework to data from an eye tracking study on the reading behavior of users studying metro maps. In anexpertevaluationwithﬁveanalysts,weidentiﬁeddistinguishing characteristics of the different word-sized visualizations.

Many analytic applications require analyzing user interaction data. In particular, such data can be aggregated over a window to build user activity profiles. Clustering such aggregate profiles is useful for grouping together users with similar behaviors, so that common models could be built for them. In this paper, we present an approach for clustering profiles that are incrementally maintained over a stream of updates. Owing to the potentially large number of users and high rate of interactions, maintaining profile clusters can have high processing and memory resource requirements. To tackle this problem, we apply distributed stream processing. However, in the presence of distributed state, it is a major challenge to partition the profiles over nodes such that memory and computation balance is maintained, while keeping the clustering accuracy high. Furthermore, in order to adapt to potentially changing user interaction patterns, the partitioning of profiles to nodes should be continuously revised, yet one should minimize the migration of profiles so as not to disturb the online processing of updates. We develop a re-partitioning technique that achieves all these goals. To achieve this, we keep micro-cluster summaries at each node and periodically collect these summaries at a central node to perform re-partitioning. We use a greedy algorithm with novel affinity heuristics to revise the partitioning and update the routing tables without introducing a lengthy pause. We showcase the effectiveness of our approach using an application that clusters customers of a telecommunications company based on their aggregate calling profiles.

In the big data era, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high when the data size and the cluster number are large. The processing bottleneck of k-means lies in the operation of seeking the closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore broken. The most interesting thing is that k-nearest neighbor graph is constructed by calling the fast k-means itself. Compared with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, it would take 3 years for traditional k-means to fulfill the same scale of clustering.


Question: Divide and evaluate: utilizing clusters to assess brief responses efficiently.

This paper outlines the emerging field of personalized online education, highlighting the pivotal role of crowdsourcing mechanisms in realizing its full potential. The recent triumphs of platforms like Khan Academy and Stanford's online courses have ignited a surge in interest for online learning, yet the underlying technology remains in its nascent stages. Recognizing the limitations of current online education systems, the paper identifies key challenges and invites Human Computation and Crowdsourcing (HCOMP) researchers to contribute their expertise. Among the issues discussed are the scalability of personalized learning, the creation of dynamic and adaptive content, and the integration of human intelligence in automated grading systems. The authors emphasize the need for innovative solutions that leverage the collective intelligence of the crowd to enhance the quality and accessibility of online education, particularly in areas such as the assessment of short answer questions. They envision a future where personalized online learning experiences are not only possible but also enriched by the diverse insights of a global community of contributors.

Recent studies of probabilistic reasoning have postulated general-purpose inference algorithms that can be used to answer arbitrary queries. These algorithms are memoryless, in the sense that each query is processed independently, without reuse of earlier computation. We argue that the brain operates in the setting of amortized inference, where numerous related queries must be answered (e.g., recognizing a scene from multiple viewpoints); in this setting, memoryless algorithms can be computationally wasteful. We propose a simple form of flexible reuse, according to which shared inferences are cached and composed together to answer new queries. We present experimental evidence that humans exploit this form of reuse: the answer to a complex query can be systematically predicted from a person’s response to a simpler query if the simpler query was presented first and entails a sub-inference (i.e., a sub-component of the more complex query). People are also faster at answering a complex query when it is preceded by a sub-inference. Our results suggest that the astonishing efficiency of human probabilistic reasoning may be supported by interactions between inference and memory.

We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.

This paper presents a new method for extracting unique features of items based on their textual reviews. The method is built of two similar iterations of applying a weighting scheme and then clustering the resultant set of vectors. In the first iteration, restaurants of similar food genres are grouped together into clusters. The second iteration reduces the importance of common terms in each such cluster, and highlights those that are unique to each specific restaurant. Clustering the restaurants again, now according to their unique features, reveals very interesting connections between the restaurants.

Text analytics applications using machine learning techniques have grown in importance with ever increasing amount of data being generated from web-scale applications, social media and digital repositories. Apart from being large in size, these generated data are often unstructured and are heavily sparse in nature. The performance of these applications on current systems is hampered by hard to predict branches and low compute-per-byte ratio. This paper proposes a set of fine-grained accelerators that improve the performance and energy-envelope of these applications by an order of magnitude.

For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system “in the wild”, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of “exploring” a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.

Some queries cannot be answered by machines only. Processing such queries requires human input for providing information that is missing from the database, for performing computationally difficult functions, and for matching, ranking, or aggregating results based on fuzzy criteria. CrowdDB uses human input via crowdsourcing to process queries that neither database systems nor search engines can adequately answer. It uses SQL both as a language for posing complex queries and as a way to model data. While CrowdDB leverages many aspects of traditional database systems, there are also important differences. Conceptually, a major change is that the traditional closed-world assumption for query processing does not hold for human input. From an implementation perspective, human-oriented query operators are needed to solicit, integrate and cleanse crowdsourced data. Furthermore, performance and cost depend on a number of new factors including worker affinity, training, fatigue, motivation and location. We describe the design of CrowdDB, report on an initial set of experiments using Amazon Mechanical Turk, and outline important avenues for future work in the development of crowdsourced query processing systems.

Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that "look like" good communities for the application of interest.
 In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.

We propose a novel probabilistic technique for modeling and extracting salient structure from large document collections. As in clustering and topic modeling, our goal is to provide an organizing perspective into otherwise overwhelming amounts of information. We are particularly interested in revealing and exploiting relationships between documents. To this end, we focus on extracting diverse sets of threads—singlylinked, coherent chains of important documents. To illustrate, we extract research threads from citation graphs and construct timelines from news articles. Our method is highly scalable, running on a corpus of over 30 million words in about four minutes, more than 75 times faster than a dynamic topic model. Finally, the results from our model more closely resemble human news summaries according to several metrics and are also preferred by human judges.

K-means clustering is one of the most popular clustering algorithms used in data mining. However, clustering is a time consuming task, particularly w ith the large data sets found in data mining. In this p aper we show how bootstrap averaging with k-means can produce results comparable to clustering all of the data but in much less time. The approach of bootstr ap (sampling with replacement) averaging consists of running k-means clustering to convergence on small bootstrap samples of the training data and averagin g similar cluster centroids to obtain a single model. We show why our approach should take less computation time and empirically illustrate its benefits. We sh ow that the performance of our approach is a monotonic function of the size of the bootstrap sample. Howev er, knowing the size of the bootstrap sample that yield s as good results as clustering the entire data set rema ins an open and important question.

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds.
 Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated.
 Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB’s superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.


Question: Divide and refine: utilizing clusters for efficient grading of short answers at scale.

This paper presents a novel technique, coined "powergrading," which significantly enhances the efficiency of grading short answer questions by leveraging machine assistance. Building upon previous work in automated grading, our method begins by training a similarity metric to evaluate student responses. However, the innovation lies in the subsequent use of this metric to categorize responses into clusters and subclusters. These categorized groups enable educators to assess multiple answers simultaneously, offering comprehensive feedback to students with similar responses, and identifying common misconceptions among learners. This amplification of grading efforts is termed "powergrading." To further streamline the grading process, especially when an answer key is available, we automate certain grading actions, reducing the burden on educators. The effectiveness of our method is demonstrated through a comparative analysis with an LDA-based approach, showcasing improved grading progress with a limited "budget" of human actions. The analysis is conducted on a dataset comprising 10 questions answered by 698 students, illustrating the potential of powergrading to transform the grading landscape for short answer questions.

Entertainity AB plans to build a news service to provide news to end-users in an innovative way. The service must include a way to automatically group series of news from different sources and publications, based on the stories they are covering. This thesis include three contributions: a survey of known clustering methods, an evaluation of human versus human results when grouping news articles in an event-centric manner, and last an evaluation of an incremental clustering algorithm to see if it is possible to consider a reduced input size and still get a sufficient result. The conclusions are that the result of the human evaluation indicates that users are different enough to warrant a need to take that into account when evaluating algorithms. It is also important that this difference is considered when conducting cluster analysis to avoid overfitting. The evaluation of an incremental event-centric algorithm shows it is desirable to adjust the similarity threshold, depending on what result one want. When running tests with different input sizes, the result implies that a short summary of a news article is a natural feature selection when performing cluster analysis.

Formative assessment allows students to judge their learning progress against a given standard or assessment criteria during the course of study. There are challenges related to the generation and delivery of feedback for formative purposes, for instance the students' engagement in their learning, the pedagogy adopted and the usability of the technology tools used in this regard. This paper presents a study of iPad mobile tablet technology-supported peer-to-peer assessment (P2PASS), which is a modern form of formative assessment. The experimental work was undertaken at Kigali Institute of Science and Technology (KIST) in Rwanda. The implementation of P2PASS in this study shows the students' engagement in learning mathematics, and allowed the investigation on the possibilities of improving their mastery of the subject. This paper further discusses the students' attitudes towards the P2PASS process as well as the technological tools used. The usability problems of the iPad applications are discussed, together with the implications of using mobile tablet technology in providing the feedback on the mathematical work.

The amount of controversial issues being discussed on the Web has been growing dramatically. In articles, blogs, and wikis, people express their points of view in the form of arguments, i.e., claims that are supported by evidence. Discovery of arguments has a large potential for informing decision-making. However, argument discovery is hindered by the sheer amount of available Web data and its unstructured, free-text representation. The former calls for automatic text-mining approaches, whereas the latter implies a need for manual processing to extract the structure of arguments. In this paper, we propose a crowdsourcing-based approach to build a corpus of arguments, an argumentation base, thereby mediating the trade-off of automatic text-mining and manual processing in argument discovery. We develop an end-to-end process that minimizes the crowd cost while maximizing the quality of crowd answers by: (1) ranking argumentative texts, (2) pro-actively eliciting user input to extract arguments from these texts, and (3) aggregating heterogeneous crowd answers. Our experiments with real-world datasets highlight that our method discovers virtually all arguments in documents when processing only 25% of the text with more than 80% precision, using only 50% of the budget consumed by a baseline algorithm.

This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and its variants have a time complexity which is linear in the number of documents, but are thought to produce inferior clusters. Sometimes K-means and agglomerative hierarchical approaches are combined so as to “get the best of both worlds.” However, our results indicate that the bisecting K-means technique is better than the standard K-means approach and as good or better than the hierarchical approaches that we tested for a variety of cluster evaluation metrics. We propose an explanation for these results that is based on an analysis of the specifics of the clustering algorithms and the nature of document data. 1 Background and Motivation Document clustering has been investigated for use in a number of different areas of text mining and information retrieval. Initially, document clustering was investigated for improving the precision or recall in information retrieval systems [Rij79, Kow97] and as an efficient way of finding the nearest neighbors of a document [BL85]. More recently, clustering has been proposed for use in browsing a collection of documents [CKPT92] or in organizing the results returned by a search engine in response to a user’s query [ZEMK97]. Document clustering has also been used to automatically generate hierarchical clusters of documents [KS97]. (The automatic generation of a taxonomy of Web documents like that provided by Yahoo!

Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple ($>$ 2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.

Community-based Question Answering sites, such as Yahoo! Answers or Baidu Zhidao, allow users to get answers to complex, detailed and personal questions from other users. However, since answering a question depends on the ability and willingness of users to address the asker's needs, a significant fraction of the questions remain unanswered. We measured that in Yahoo! Answers, this fraction represents 15% of all incoming English questions. At the same time, we discovered that around 25% of questions in certain categories are recurrent, at least at the question-title level, over a period of one year.
 We attempt to reduce the rate of unanswered questions in Yahoo! Answers by reusing the large repository of past resolved questions, openly available on the site. More specifically, we estimate the probability whether certain new questions can be satisfactorily answered by a best answer from the past, using a statistical model specifically trained for this task. We leverage concepts and methods from query-performance prediction and natural language processing in order to extract a wide range of features for our model. The key challenge here is to achieve a level of quality similar to the one provided by the best human answerers.
 We evaluated our algorithm on offline data extracted from Yahoo! Answers, but more interestingly, also on online data by using three "live" answering robots that automatically provide past answers to new questions when a certain degree of confidence is reached. We report the success rate of these robots in three active Yahoo! Answers categories in terms of both accuracy, coverage and askers' satisfaction. This work presents a first attempt, to the best of our knowledge, of automatic question answering to questions of social nature, by reusing past answers of high quality.

In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.

The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.

We address therating-inference problem, wherein rather than simply decide whether a review is “thumbs up” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, “three stars” is intuitively closer to “four stars” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labelingformulation of the problem, that alters a given n-ary classifier’s output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem. Publication info: Proceedings of the ACL, 2005.

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results.


Question: Evaluation of various grid abstractions for route-finding on maps.

This document outlines the development and details of a substantial annotated corpus of English, specifically the Penn Treebank, which contains over 4.5 million words of American English. Throughout the initial three-year phase of the Penn Treebank Project (1989-1992), this corpus has been meticulously annotated for part-of-speech (POS) information. Moreover, more than half of the corpus has received additional annotation for skeletal syntactic structure. The Penn Treebank serves as a significant resource for linguistic research, providing detailed analysis and tagging of English language usage. This technical report, authored by Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz from the University of Pennsylvania's Department of Computer and Information Science, offers comprehensive insights into the creation and structure of the Penn Treebank. It is available at ScholarlyCommons, providing researchers and linguists with a valuable tool for studying English language syntax and semantics.

**Question**: An Analysis of the Impact of Social Media on Mental Health

**Documents**: The Role of Social Support in the Relationship Between Social Media Use and Depression Among Young Adults
This study examined the role of social support in the relationship between social media use and depression among young adults. A sample of 284 young adults (mean age = 23.5 years) completed an online survey measuring social media use, social support, and depression. Results indicated that social media use was positively associated with depression, but this relationship was moderated by social support. Specifically, the positive association between social media use and depression was stronger among individuals who reported lower levels of social support. These findings suggest that social support may serve as a protective factor against the negative effects of social media use on mental health among young adults.

**Similar Documents**: This research paper investigates the influence of social support on the correlation between social media usage and depression in young adults. A group of 284 young adults (average age of 23.5 years) participated in an online survey that assessed their social media habits, the level of social support they received, and their depressive symptoms. The analysis revealed a positive correlation between social media use and depression. However, this relationship was found to be contingent on social support. More specifically, the link between social media use and depression was notably stronger in individuals who reported lower levels of social support. These findings highlight the potential of social support as a protective mechanism against the detrimental impact of social media on the mental health of young adults, suggesting that a strong social support network may mitigate the

We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form “Is there a target node that is reachable from the current node?”. This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowdsourcing platforms such as Mechanical Turk.

Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.

Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries on-the-fly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadth-first search trees materialized is reduced from O(N) to O(|Δ|), where |Δ| ≤ N is the number of orbits in the graph. We explore orbit adjacency and local symmetry to obtain compact breadth-first-search trees (compact BFS-trees). An extensive empirical study using both synthetic data and real data shows that compact BFS-trees can be built efficiently and the space cost can be reduced substantially. Moreover, online shortest path query answering can be achieved using compact BFS-trees.

Real-time robot motion planning using rasterizing computer graphics hardware. In Proc. OY82] C. O'D unlaing and C.K. Yap. A retraction method for planning the motion of a disc. A local approach for path planning of manipulators with a high number of degrees of freedom. a path generation algorithm into oo-line programming of airbus panels. Motion planning for many degrees of freedom-random reeections at c-space obstacles. REFERENCES 31 iterative collision checker. We observed no dramatic slowdown of the planner. A challenging research goal would now be to extend the method to dynamic scenes. One rst question is: How should a roadmap computed for a given workspace be updated if a few obstacles are removed or added? Answering this question would be useful to apply our method to scenes subject to small incremental changes. Such changes occur in many manufacturing (e.g., assembly) cells; while most of the geometry of such a cell is permanent and stationary, a few objects (e.g., xtures) are added or removed between any two consecutive manufacturing operations. Similar incremental changes also occur in automatic graphic animation. A second question is: How should the learning and query phase be modiied if some obstacles are moving along known trajectories? An answer to this question might consist of applying our roadmap method in the conngurationtime space of the robot Lat91]. The roadmap would then have to be built as a directed graph, since local paths between any two nodes must monotonically progress along the time axis, with possibly additional constraints on their slope and curvature to reeect bounds on the robot's velocity and acceleration. A motion planner with performance proportional to task diiculty. 30 In KL94a, KL94b, O S94] prior versions of the method have been applied to a great variety of holonomic robots including planar and spatial articulated robots with revolute, prismatic, and/or spherical joints, xed or free base, and single or multiple kinematic chains. In Sve93, SO94] a variation of the method (essentially one with a diierent general local planner) was also run successfully on examples involving nonholonomic car-like robots. Experimental results show that our method can eeciently solve problems which are beyond the capabilities of other existing methods. For example, for planar articulated robots with many dofs, the customized implementation of Section 5 is much more consistent than the Randomized Path Planner (RPP) of BL91]. Indeed, the latter can be very fast on some diicult problems, but it …

Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

We combine topological and geometric methods to construct a multiresolution representation for a function over a two-dimensional domain. In a preprocessing stage, we create the Morse-Smale complex of the function and progressively simplify its topology by cancelling pairs of critical points. Based on a simple notion of dependency among these cancellations, we construct a hierarchical data structure supporting traversal and reconstruction operations similarly to traditional geometry-based representations. We use this data structure to extract topologically valid approximations that satisfy error bounds provided at runtime.

One of the most popular graph drawing methods is based of achieving graphtheoretic target ditsances. This method was used by Kamada and Kawai [15], who formulated it as an energy optimization problem. Their energy is known in the multidimensional scaling (MDS) community as the stress function. In this work, we show how to draw graphs by stress majorization, adapting a technique known in the MDS community for more than two decades. It appears that majorization has advantages over the technique of Kamada and Kawai in running time and stability. We also present a few extensions to the basic energy model which can improve layout quality and computation speed in practice. Majorization-based optimization is essential to these extensions.

Many driver assistant and safety systems depend on an accurate environmental model containing the positions of stationary objects, the states of dynamic objects and information about valid driving corridors. Therefore, a robust differentiation between moving and stationary objects is required. This is challenging for laser scanners, because these sensors are not able to measure the velocity of objects directly. Therefore, an advanced occupancy grid approach, the online map, is introduced, which enables the robust separation of moving and stationary objects. The online map is used for the robust detection of the road boundaries for the determination of driving corridors in urban and highway scenarios. An algorithm for the detection of arbitrary moving objects using the online map is proposed.

Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialized population of ISAc lists while the other initializes populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.

Many applications work with graph-structured data. As graphs grow in size, indexing becomes essential to ensure sufficient query performance. We present the GRIPP index structure (GRaph Indexing based on Pre- and Postorder numbering) for answering reachability queries in graphs.
 GRIPP requires only linear time and space. Using GRIPP, we can answer reachability queries on graphs with 5 million nodes on average in less than 5 milliseconds, which is unrivaled by previous methods. We evaluate the performance and scalability of our approach on real and synthetic random and scale-free graphs and compare our approach to existing indexing schemes. GRIPP is implemented as stored procedure inside a relational database management system and can therefore very easily be integrated into existing graph-oriented applications.


Question: Evaluation of various grid abstractions for route finding on geographical maps.

This research introduces an algorithm aimed at the unsupervised learning or induction of a basic morphological lexicon for a natural language. The approach employs a probabilistic maximum a posteriori model that constructs hierarchical representations for a collection of morphs, akin to morpheme-like elements, derived from unannotated text corpora. The algorithm generates a morph lexicon that encapsulates parameters relevant to both the semantic and phonetic aspects of the morphs. These parameters influence how the morphs function within words. The model is tested in the context of unsupervised morpheme segmentation, specifically for Finnish and English words. The outcomes are notably successful for Finnish, with similarly impressive results achieved for English, demonstrating the algorithm's efficacy in handling morphological analysis for both languages.

Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.

Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets, and entity-relationship diagrams, basic graph drawing algorithms are required. The state of the art in automatic drawing is surveyed comprehensively in [7,19]. There have been only a few algorithms for general undirected graphs. This paper presents a simple but successful algorithm for drawing undirected graphs and weighted graphs. The basic idea of our algorithm is as follows. We regard the desirable "geometric" (Euclidean) distance between two vertices in the drawing as the "graph theoretic" distance between them in the corresponding graph. We introduce a virtual dynamic system in which every two vertices are connected by a "spring" of such desirable length. Then, we regard the optimal layout of vertices as the state in which the total spring energy of the system is minimal. The "spring" idea for drawing general graphs was introduced in [6], and similar methods were used for drawing planar graphs with fixed boundary [2,20]. This paper brings a new significant result in graph drawing based on the spring model.

With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a “geographic fence” that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris’ wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy. 1

A survey is presented of an approach to motion planning that emphasizes object-oriented, exact, and discrete (or combinatorial) algorithmic techniques in which worst-case asymptotically efficient solutions are being sought. Following a statement of the problem, motion planning in static and known environments is treated. The discussion covers general solutions, lower bounds, the projection method, the retraction method, the expanded obstacles, the single-component approach, and a mobile convex object moving in a 2D polygonal space. Variants of the motion-planning problem are then considered, namely, optimal motion planning, adaptive and exploratory motion planning, motion planning in the presence of moving obstacles, constrained motion planning, motion planning with uncertainty, and general task planning.<<ETX>>

Art often provides valuable hints for technological innovations especially in the field of Image Processing and Computer Graphics. In this paper we survey in a unified framework several methods to transform raster input images into good quality mosaics. For each of the major different approaches in literature the paper reports a short description and a discussion of the most relevant issues. To complete the survey comparisons among the different techniques both in terms of visual quality and computational complexity are provided.

We consider the computational problem of nding nearest neighbors in general metric spaces Of particular interest are spaces that may not be conveniently embedded or approxi mated in Euclidian space or where the dimensionality of a Euclidian representation is very high Also relevant are high dimensional Euclidian settings in which the distribution of data is in some sense of lower di mension and embedded in the space The vp tree vantage point tree is introduced in several forms together with associated algorithms as an improved method for these di cult search problems Tree construc tion executes in O n log n time and search is under certain circumstances and in the limit O log n expected time The theoretical basis for this approach is developed and the results of several experiments are reported In Euclidian cases kd tree performance is compared

Geographic information extraction from textual data sources, called geoparsing, is a key task in text processing and central to subsequent spatial analysis approaches. Several geoparsers are available that support this task, each with its own (often limited or specialized) gazetteer and its own approaches to toponym detection and resolution. In this demonstration paper, we present HeidelPlace, an extensible framework in support of geoparsing. Key features of HeidelPlace include a generic gazetteer model that supports the integration of place information from different knowledge bases, and a pipeline approach that enables an effective combination of diverse modules tailored to specific geoparsing tasks. This makes HeidelPlace a valuable tool for testing and evaluating different gazetteer sources and geoparsing methods. In the demonstration, we show how to set up a geoparsing workflow with HeidelPlace and how it can be used to compare and consolidate the output of different geoparsing approaches.

SpatialHadoop is an extended MapReduce framework that supp orts global indexing that spatial partitions the data across machines providing orders of magnitude speedup, compared to traditi onal Hadoop. In this paper, we describe seven alternative partit ioning techniques and experimentally study their effect on the qua lity of the generated index and the performance of range and spatial join queries. We found that using a 1% sample is enough to produce high quality partitions. Also, we found that the total area o f partitions is a reasonable measure of the quality of indexes when r unning spatial join. This study will assist researchers in cho osing a good spatial partitioning technique in distributed enviro nments. 1. INDEXING IN SPATIALHADOOP SpatialHadoop [2, 3] provides a generic indexing algorithm which was used to implement grid, R-tree, and R+-tree based p artitioning. This paper extends our previous study by introduci ng four new partitioning techniques, Z-curve, Hilbert curve, Quad tree, and K-d tree, and experimentally evaluate all of the seven techn iques. The partitioning phase of the indexing algorithm runs in thr ee steps, where the first step is fixed and the last two steps are customiz ed for each partitioning technique. The first step computes number of desired partitionsn based on file size and HDFS block capacity which are both fixed for all partitioning techniques. The second st ep reads a random sample, with a sampling ratio ρ, from the input file and uses this sample to partition the space into cells such that number of sample points in each cell is at most ⌊k/n⌋, wherek is the sample size. The third step actually partitions the file by as signing each record to one or more cells. Boundary objects are handle d using either thedistribution or replication methods. Thedistribution method assigns an object to exactly one overlapping cell and the cell has to be expanded to enclose all contained records. The replication method avoids expanding cells by replicating each record to all overlapping cells but the query processor has to employ a duplicate avoidance technique to account for replicated records . ∗This work is supported in part by the National Science Founda tion, USA, under Grants IIS-0952977 and IIS-1218168 and the University of Minnesota Doctoral Disseration Fellowship. This work is licensed under the Creative Commons Attributio nNonCommercial-NoDerivs 3.0 Unported License. To view a cop y f this license, visit http://creativecommons.org/licenses/by-n c-nd/3.0/. Obtain permission prior to any use beyond those covered by the license. Contact copyright holder by emailing info@vldb.org. Articles fromthis volume were invited to present their results at the 41st Internatio l Conference on Very Large Data Bases, August 31st September 4th 2015, Koha la Coast, Hawaii. Proceedings of the VLDB Endowment, Vol. 8, No. 12 Copyright 2015 VLDB Endowment 2150-8097/15/08. 2. EXPERIMENTAL SETUP All experiments run on Amazon EC2 ‘m1.large’ instances which have a dual core processor, 7.5 GB RAM and 840 GB disk storage. We use Hadoop 1.2.1 running on Java 1.6 and CentOS 6. Each machine is configured to run three mappers and two reduce rs. Tables 1 and 2 summarize the datasets and configuration param eters used in our experiments, respectively. Default parame ters (in parentheses) are used unless otherwise mentioned. In the fo llowing part, we describe the partitioning techniques, the quer ies we run, and the performance metrics measured in this paper. 2.1 Partitioning Techniques This paper employsgrid and Quad tree as space partitioning techniques;STR, STR+, and K-d tree as data partitioning techniques; andZ-curve andHilbert curve as space filling curve (SFC) partitioning techniques. These techniques can also be grou ped, according to boundary object handling, into replication-based techniques (i.e., Grid, Quad, STR+, and K-d tree) and distributionbased techniques (i.e., STR, Z-Cruve, and Hilbert). Figure 1 illustrates these techniques, where sample points and partition bou daries are shown as dots and rectangles, respectively. 1. Uniform Grid: This technique does not require a random sample as it divides the input MBR using a uniform grid of ⌈√n⌉ × ⌈√n⌉ grid cells and employs the replication method to handle boundary objects. 2. Quad tree: This technique inserts all sample points into a quad tree [6] with node capacity of ⌊k/n⌋, wherek is the sample size. The boundaries of all leaf nodes are used as cell boundaries. W use the replication method to assign records to cells. 3. STR: This technique bulk loads the random sample into an Rtree using the STR algorithm [8] and the capacity of each node is set to⌊k/n⌋. The MBRs of leaf nodes are used as cell boundaries. Boundary objects are handled using the distribution method where it assigns a record r to the cell with maximal overlap. 4. STR+: This technique is similar to the STR technique but it uses the replication method to handle boundary objects. 5. K-d tree: This technique uses the K-d tree [1] partitioning method to partition the space into n cells. It starts with the input MBR as one cell and partitions it n − 1 times to producen cells. Records are assigned to cells using the replication method. 6. Z-curve: This technique sorts the sample points by their order on the Z-curve and partitions the curve into n splits, each containing roughly⌊k/n⌋ points. It uses the distribution method to assign a recordr to one cell by mapping the center point of its MBR to one of then splits. 7. Hilbert curve: This technique is exactly the same as the Zcurve technique but it uses Hilbert space filling curve which has better spatial properties. (a) Grid (b) Quad Tree (c) STR and STR+ (d) K-d Tree (e) Z-curve (f) Hilbert Curve Figure 1: Partitioning Techniques. Examples of nearly-empty partitions are starred. Name Size Records Average Record Size All Objects 88GB 250M 378 bytes Buildings 25GB 109M 234 bytes Roads 23GB 70M 337 bytes Lakes 8.6GB 9M 1KB Cities 1.4GB 170K 8.4KB

We present a new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes. It automatically achieves desired region areas while maintaining correct map topology. The algorithm is compared with a number of existing methods, and results are shown to be superior in both accuracy and preservation of shape recognition cues. Through hierarchical resolution, we first perform gross adjustments upon a coarsely resampled map and later refine the map at progressively higher levels of detail.

One of the most popular graph drawing methods is based of achieving graphtheoretic target ditsances. This method was used by Kamada and Kawai [15], who formulated it as an energy optimization problem. Their energy is known in the multidimensional scaling (MDS) community as the stress function. In this work, we show how to draw graphs by stress majorization, adapting a technique known in the MDS community for more than two decades. It appears that majorization has advantages over the technique of Kamada and Kawai in running time and stability. We also present a few extensions to the basic energy model which can improve layout quality and computation speed in practice. Majorization-based optimization is essential to these extensions.


Question: Evaluation of various grid abstractions for route finding on geographical maps.

This research presents an innovative application of minimum description length (MDL) analysis to simulate the unsupervised acquisition of morphological segmentation in European languages. Utilizing corpora with varying sizes, from 5,000 to 500,000 words, we have devised a series of heuristics capable of swiftly constructing a probabilistic morphological grammar. Central to our methodology is the employment of MDL as the primary criterion for assessing the viability of modifications proposed by these heuristics. The resulting grammar closely mirrors the segmentation one might expect from a human morphologist. In the concluding section, we explore the parallels between this MDL-based grammatical analysis and the concept of evaluation metrics as initially proposed in generative grammar theories.

**Question**: The Role of Social Media in Modern Political Campaigns

**Documents**: The Impact of Social Media on Political Participation and Civic Engagement
This paper examines the impact of social media on political participation and civic engagement. It explores how social media platforms have transformed the way citizens engage with politics and participate in civic activities. The study reveals that social media has increased political awareness and facilitated the mobilization of individuals for political causes. It also highlights the potential downsides, such as the spread of misinformation and the creation of echo chambers. The findings suggest that social media can play a significant role in promoting democratic participation, but its effects are nuanced and context-dependent.

**Similar Documents**: This paper delves into the profound influence of social media on political participation and civic engagement, detailing the transformation of citizen involvement in politics and civic affairs through digital platforms. It uncovers the ways in which social media has bolstered political consciousness and enabled the swift mobilization of individuals for political initiatives. However, the research also cautions about the potential pitfalls, including the dissemination of false information and the formation of isolated information bubbles. The analysis underscores the pivotal role social media can assume in enhancing democratic engagement, though it emphasizes the complexity and variability of its impacts, which are contingent upon specific contexts.

For autonomous vehicles and advanced driver assistance systems, information on the actual state of the environment is fundamental for localization and mapping tasks. Localization benefits from multiple observations of the same location at different times as these may provide important information on static and mobile objects. For efficient mapping, the environment may be explored in parallel. For these purposes, multiple observations represented by grid maps have to be aligned into one mutual frame. This paper addresses the problem of group-wise grid map registration using an image processing approach. For registration, a rotational-invariant descriptor is proposed in order to provide the correspondences of points of interest in radar-based occupancy grid maps. As pairwise registration of multiple grid maps suffers from bias, this paper proposes a graph-based approach for robust registration of multiple grid maps. This will facilitate highly accurate range sensor maps for the aforementioned purposes. Large-scale experiments show the benefit of the proposed methods and compare it to state-of-the-art algorithms on radar measurements.

The paper discussu some antral irrw in the cooperation between disuibuted agcnu using lhe following case study: The objective is to explore a distant planet. more concretely to collect samples of a parricular typ of precious ruck. The location of the ruck .iamplu is unknown in advance but they arc rypiwlly clustered in cenain spm There i S a vehicle that can drive around on the planet and later reenter the s p x d (0 g0 back to ewlh There is no deuiled map of the t e m h although it h known hat the terrain is fuU of obstacles. hills. valleys. etc.

Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

Accurate maps of the static environment are essential for many advanced driver-assistance systems. A new method for the fast computation of occupancy grid maps with laser range-finders and radar sensors is proposed. The approach utilizes the Graphics Processing Unit to overcome the limitations of classical occupancy grid computation in automotive environments. It is possible to generate highly accurate grid maps in just a few milliseconds without the loss of sensor precision. Moreover, in the case of a lower resolution radar sensor it is shown that it is suitable to apply super-resolution algorithms to achieve the accuracy of a higher resolution laser-scanner. Finally, a novel histogram based approach for road boundary detection with lidar and radar sensors is presented.

Hadoop, employing the MapReduce programming paradigm, has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not truly exploited towards processing large-scale computational geometry operations. This paper introduces CG_Hadoop; a suite of scalable and efficient MapReduce algorithms for various fundamental computational geometry problems, namely, polygon union, skyline, convex hull, farthest pair, and closest pair, which present a set of key components for other geometric algorithms. For each computational geometry operation, CG_Hadoop has two versions, one for the Apache Hadoop system and one for the SpatialHadoop system; a Hadoop-based system that is more suited for spatial operations. These proposed algorithms form a nucleus of a comprehensive MapReduce library of computational geometry operations. Extensive experimental results on a cluster of 25 machines of datasets up to 128GB show that CG_Hadoop achieves up to 29x and 260x better performance than traditional algorithms when using Hadoop and SpatialHadoop systems, respectively.

An important aspect of the production of digital games is user-oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and design.

We present a new approach to interactive ray tracing of moderate-sized animated scenes based on traversing frustum-bounded packets of coherent rays through uniform grids. By incrementally computing the overlap of the frustum with a slice of grid cells, we accelerate grid traversal by more than a factor of 10, and achieve ray tracing performance competitive with the fastest known packet-based kd-tree ray tracers. The ability to efficiently rebuild the grid on every frame enables this performance even for fully dynamic scenes that typically challenge interactive ray tracing systems.

Over the past 40 years or so, human activities and movements in space-time have attracted considerable research interest in geography. One of the earliest analytical perspectives for the analysis of human activity patterns and movements in spacetime is time geography. Despite the usefulness of time geography in many areas of geographical research, there are very few studies that actually implemented its constructs as analytical methods up to the mid-1990s. With increasing availability of geo-referenced individual-level data and improvement in the geo-computational capabilities of Geographical Information Systems (GIS), it is now more feasible than ever before to operationalize and implement time-geographic constructs. This paper discusses recent applications of GIS-based geo-computation and three-dimensional (3-D) geo-visualization methods in time-geographic research. The usefulness of these methods is illustrated through examples drawn from the author’s recent studies. The paper attempts to show that GIS provides an effective environment for implementing timegeographic constructs and for the future development of operational methods in time-geographic research.

We present a new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes. It automatically achieves desired region areas while maintaining correct map topology. The algorithm is compared with a number of existing methods, and results are shown to be superior in both accuracy and preservation of shape recognition cues. Through hierarchical resolution, we first perform gross adjustments upon a coarsely resampled map and later refine the map at progressively higher levels of detail.

By repeatedly combining the source node's nearest neighbor, we propose a node combination (NC) method to implement the Dijkstra's algorithm. The NC algorithm finds the shortest paths with three simple iterative steps: find the nearest neighbor of the source node, combine that node with the source node, and modify the weights on edges that connect to the nearest neighbor. The NC algorithm is more comprehensible and convenient for programming as there is no need to maintain a set with the nodes' distances. Experimental evaluations on various networks reveal that the NC algorithm is as efficient as Dijkstra's algorithm. As the whole process of the NC algorithm can be implemented with vectors, we also show how to find the shortest paths on a weight matrix. } is the set of nodes, E = {e ij j if there is a link from v i to v j } is the set of edges and W = {w ij j 1 6 i, j 6 N} is the weight matrix for E. Given two nodes v s , v t of G, the shortest path problem can be defined as how to find a path with the minimum sum of the weights on the edges in a v s , v t-path. Generally, v s and v t are called source node and sink node, respectively. The shortest path problem is one of the most fundamental network optimization problems with widespread applications [1–4]. Among the various shortest path algorithms developed [5–12], Dijkstra's algorithm is probably the most well-known. It maintains a set S of solved nodes, comprising those nodes whose final shortest path distance from the source v s has determined, and labels d(i), storing the upper bound of the shortest path distance from v s to v i. The algorithm repeatedly selects the node v k 2 VnS with the minimum d(i), adds v k to S, and updates d(i) for nodes that are incident to v k Step 1. Select a node v k from Q such that dðv k Þ ¼ min v j 2Q dðv j Þ, if d(v k) = 1, stop, otherwise go to Step 2. Step 3. for every v j 2 Q, update d(v j) = min{d(v j), d(v k) + w kj }. Go to Step 1. In practice, Dijkstra's algorithm relies heavily on the strategies used to select the next minimum labeled …