nils-herrmann/clean_scirepeval huggingface dataset:
Answering Location-Aware Graph Reachability Queries on GeoSocial Data

Thanks to the wide spread use of mobile and wearable devices, popular social networks, e.g., Facebook, prompts users to add spatial attributes to social entities, e.g., check-ins, traveling posts, and geotagged photos, leading to what is known as, The GeoSocial Graph. In such graph, usersmay issue a Reachability Query with Spatial Range Predicate (abbr. RangeReach). RangeReach finds whether an input vertex can reach any spatial vertex that lies within an input spatial range. The paper proposes GEOREACH, an approach that adds spatial data awareness to a graph database management system. GEOREACH allows efficient execution of RangeReach queries, yet without compromising a lot on the overall system scalability. Experiments based on system implementation inside Neo4j prove that GEOREACH exhibits up to two orders of magnitude better query performance and up to four times less storage than the state-of-the-art spatial and reachability indexing approaches.
Improvement of Fingerprint Retrieval by a Statistical Classifier

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple k-nearest neighbor (k-NN) classification. However, the k-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-N fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved.
Random Walk with Restart on Large Graphs Using Block Elimination

Given a large graph, how can we calculate the relevance between nodes fast and accurately? Random walk with restart (RWR) provides a good measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection. Since calculating RWR from scratch takes a long time, various preprocessing methods, most of which are related to inverting adjacency matrices, have been proposed to speed up the calculation. However, these methods do not scale to large graphs because they usually produce large dense matrices that do not fit into memory. In addition, the existing methods are inappropriate when graphs dynamically change because the expensive preprocessing task needs to be computed repeatedly.
 In this article, we propose Bear, a fast, scalable, and accurate method for computing RWR on large graphs. Bear has two versions: a preprocessing method BearS for static graphs and an incremental update method BearD for dynamic graphs. BearS consists of the preprocessing step and the query step. In the preprocessing step, BearS reorders the adjacency matrix of a given graph so that it contains a large and easy-to-invert submatrix, and precomputes several matrices including the Schur complement of the submatrix. In the query step, BearS quickly computes the RWR scores for a given query node using a block elimination approach with the matrices computed in the preprocessing step. For dynamic graphs, BearD efficiently updates the changed parts in the preprocessed matrices of BearS based on the observation that only small parts of the preprocessed matrices change when few edges are inserted or deleted. Through extensive experiments, we show that BearS significantly outperforms other state-of-the-art methods in terms of preprocessing and query speed, space efficiency, and accuracy. We also show that BearD quickly updates the preprocessed matrices and immediately computes queries when the graph changes.
Towards Know-how Mapping Using Goal Modeling

In organizing the knowledge in a field of study, it is common to use classification techniques to organize concepts and approaches along dimensions of interest. In technology domains, an advance often appears in the form of a new way or method for achieving an objective. This paper proposes to use goal modeling to map the means-ends knowledge (“know-how”) in a domain. A know-how map highlights the structure of recognized problems and known solutions in the domain, thus facilitating gap identification and prompting new research and innovation. We contrast the proposed goal-oriented approach with a claim-oriented approach, using Web Page Ranking as a sample domain.
TECA: A Parallel Toolkit for Extreme Climate Analysis

We present TECA, a parallel toolkit for detecting extreme events in large climate datasets. Modern climate datasets expose parallelism across a number of dimensions: spatial locations, timesteps and ensemble members. We design TECA to exploit these modes of parallelism and demonstrate a prototype implementation for detecting and tracking three classes of extreme events: tropical cyclones, extra-tropical cyclones and atmospheric rivers. We process a massive 10TB CAM5 simulation dataset with TECA, and demonstrate good runtime performance for the three case studies.
A Micro-Spatial Analysis of the Demographic and Criminogenic Environment of Drug Markets in Philadelphia

THE AUSTRALIAN AND NEW ZEALAND JOURNAL OF CRIMINOLOGY VOLUME 40 NUMBER 1 2007 PP. 43–63 Address for correspondence: Jerry H. Ratcliffe, Associate Professor, Department of Criminal Justice, Gladfelter Hall, Temple University, 1115 W Berks Street, Philadelphia PA 19122, United States of America.  A Micro-Spatial Analysis of the Demographic and Criminogenic Environment of Drug Markets in Philadelphia
Domain Adaptation for Statistical Classifiers

The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the “in-domain” test data is drawn from a distribution that is related, but not identical, to the “out-of-domain” distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.
Lease/Release: Architectural Support for Scaling Contended Data Structures

High memory contention is generally agreed to be a worst-case scenario for concurrent data structures. There has been a significant amount of research effort spent investigating designs that minimize contention, and several programming techniques have been proposed to mitigate its effects. However, there are currently few architectural mechanisms to allow scaling contended data structures at high thread counts.
 In this article, we investigate hardware support for scalable contended data structures. We propose Lease/Release, a simple addition to standard directory-based MESI cache coherence protocols, allowing participants to lease memory, at the granularity of cache lines, by delaying coherence messages for a short, bounded period of time. Our analysis shows that Lease/Release can significantly reduce the overheads of contention for both non-blocking (lock-free) and lock-based data structure implementations while ensuring that no deadlocks are introduced. We validate Lease/Release empirically on the Graphite multiprocessor simulator on a range of data structures, including queue, stack, and priority queue implementations, as well as on transactional applications. Results show that Lease/Release consistently improves both throughput and energy usage, by up to 5x, both for lock-free and lock-based data structure designs.
A genome triplication associated with early diversification of the core eudicots

Although it is agreed that a major polyploidy event, gamma, occurred within the eudicots, the phylogenetic placement of the event remains unclear. To determine when this polyploidization occurred relative to speciation events in angiosperm history, we employed a phylogenomic approach to investigate the timing of gene set duplications located on syntenic gamma blocks. We populated 769 putative gene families with large sets of homologs obtained from public transcriptomes of basal angiosperms, magnoliids, asterids, and more than 91.8 gigabases of new next-generation transcriptome sequences of non-grass monocots and basal eudicots. The overwhelming majority (95%) of well-resolved gamma duplications was placed before the separation of rosids and asterids and after the split of monocots and eudicots, providing strong evidence that the gamma polyploidy event occurred early in eudicot evolution. Further, the majority of gene duplications was placed after the divergence of the Ranunculales and core eudicots, indicating that the gamma appears to be restricted to core eudicots. Molecular dating estimates indicate that the duplication events were intensely concentrated around 117 million years ago. The rapid radiation of core eudicot lineages that gave rise to nearly 75% of angiosperm species appears to have occurred coincidentally or shortly following the gamma triplication event. Reconciliation of gene trees with a species phylogeny can elucidate the timing of major events in genome evolution, even when genome sequences are only available for a subset of species represented in the gene trees. Comprehensive transcriptome datasets are valuable complements to genome sequences for high-resolution phylogenomic analysis.
Performance Analysis of Software-Defined Networking (SDN)

Software-Defined Networking (SDN) approaches were introduced as early as the mid-1990s, but just recently became a well-established industry standard. Many network architectures and systems adopted SDN, and vendors are choosing SDN as an alternative to the fixed, predefined, and inflexible protocol stack. SDN offers flexible, dynamic, and programmable functionality of network systems, as well as many other advantages such as centralized control, reduced complexity, better user experience, and a dramatic decrease in network systems and equipment costs. However, SDN characterization and capabilities, as well as workload of the network traffic that the SDN-based systems handle, determine the level of these advantages. Moreover, the enabled flexibility of SDN-based systems comes with a performance penalty. The design and capabilities of the underlying SDN infrastructure influence the performance of common network tasks, compared to a dedicated solution. In this paper we analyze two issues: a) the impact of SDN on raw performance (in terms of throughput and latency) under various workloads, and b) whether there is an inherent performance penalty for a complex, more functional, SDN infrastructure. Our results indicate that SDN does have a performance penalty, however, it is not necessarily related to the complexity level of the underlying SDN infrastructure.
Analysis of digital bang-bang clock and data recovery for multi-gigabit/s serial transceivers

A Harmonic Balance method for analyzing digital bang-bang clock and data recovery (CDR) is proposed in this paper. The jitter tolerance performance of the CDR is predicted by a function with variables that can be easily correlated to design parameters. A 6.25Gb/s serial transceiver was fabricated in 90nm CMOS technology. Measurements show that the jitter tolerance performance can be accurately predicted by the proposed method.
A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification

High dimensionality of text can be a deterrent in applying co mplex learners such as Support Vector Machines to the task of text classification. Feature cluster ing is a powerful alternative to feature selection for reducing the dimensionality of text data. In t his paper we propose a new informationtheoretic divisive algorithm for feature/word clustering and apply it to text classification. Existing techniques for such “distributional clustering” of words a re gglomerative in nature and result in (i) sub-optimal word clusters and (ii) high computational cost . In order to explicitly capture the optimality of word clusters in an information theoretic framewo rk, we first derive a global criterion for feature clustering. We then present a fast, divisive algori thm that monotonically decreases this objective function value. We show that our algorithm minimize s the “within-cluster Jensen-Shannon divergence” while simultaneously maximizing the “between -cluster Jensen-Shannon divergence”. In comparison to the previously proposed agglomerative str ategies our divisive algorithm is much faster and achieves comparable or higher classification acc ur ies. We further show that feature clustering is an effective technique for building smaller c lass models in hierarchical classification. We present detailed experimental results using Naive Bayes nd Support Vector Machines on the 20Newsgroups data set and a 3-level hierarchy of HTML docume nts collected from the Open Directory project ( www.dmoz.org).
Efficient Locking for Concurrent Operations on B-Trees

The B-tree and its variants have been found to be highly useful (both theoretically and in practice) for storing large amounts of information, especially on secondary storage devices. We examine the problem of overcoming the inherent difficulty of concurrent operations on such structures, using a practical storage model. A single additional “link” pointer in each node allows a process to easily recover from tree modifications performed by other concurrent processes. Our solution compares favorably with earlier solutions in that the locking scheme is simpler (no read-locks are used) and only a (small) constant number of nodes are locked by any update process at any given time. An informal correctness proof for our system is given.
Constructing sexuality and identity in an online teen chat room

In this article, we propose that adolescents’ online interactions are both a literal and a metaphoric screen for representing major adolescent developmental issues, such as sexuality and identity. Because of the public nature of Internet chat rooms, they provide an open window into the expression of adolescent concerns. Our study utilizes this window to explore how issues of sexuality and identity are constructed in a teen chat room. We adapt qualitative discourse methodology to microanalyze a half-hour transcript from a monitored teen chat room, comparing it, where relevant, to a second transcript, used in a prior study [Greenfield, P.M., Subrahmanyam, K., 2003. Online discourse in a teen chat room: New codes and new modes of coherence in a visual medium. Journal of Applied Developmental Psychology 24, 713–738.]. Our microanalysis reveals that participants use the online space of teen chat to air adolescent concerns about sexuality and to develop creative strategies to exchange identity information with their peers. This exchange is critical to the activity of bpairing offQ, an important teenage expression of emerging sexuality. Developmental issues from adolescents’ offline lives are reconstructed online with some important differences. The virtual world of teen chat may offer a safer environment for exploring emerging sexuality than the real world. Through the verbally explicit exchange of identity information, participants are able to bpair offQ with partners of their choice, despite the disembodied nature of chat participants. D 2004 Elsevier Inc. All rights reserved.
Expanding Rural Cellular Networks with Virtual Coverage

The cellular system is the world’s largest network, providing service to over five billion people. Operators of these networks face fundamental trade-offs in coverage, capacity and operating power. These trade-offs, when coupled with the reality of infrastructure in poorer areas, mean that upwards of a billion people lack access to this fundamental service. Limited power infrastructure, in particular, hampers the economic viability of wide-area rural coverage. In this work, we present an alternative system for implementing large-scale rural cellular networks. Rather than providing constant coverage, we instead provide virtual coverage: coverage that is only present when requested. Virtual coverage powers the network ondemand, which reduces overall power draw, lowers the cost of rural connectivity, and enables new markets. We built a prototype cellular system utilizing virtual coverage by modifying a GSM base station and a set of Motorola phones to support making and receiving calls under virtual coverage. To support the billions of already-deployed devices, we also implemented a small radio capable of adding backwards-compatible support for virtual coverage to existing GSM handsets. We demonstrate a maximum of 84% power and cost savings from using virtual coverage. We also evaluated virtual coverage by simulating the potential power savings on real-world cellular networks in two representative developing counties: one in sub-Saharan Africa and one in South Asia. Simulating power use based on realworld call records obtained from local mobile operators, we find our system saves 21-34% of power draw at night, and 7-21% during the day. We expect even more savings in areas currently off the grid. These results demonstrate the feasibility of implementing such a system, particularly in areas with solar or otherwiseintermittent power sources.
Enabling Technologies for Smart City Services and Applications

Smart mobile devices are fast becoming the epicentre of people's lives. Most smart phones are currently embedded with powerful and programmable sensors such as GPS, gyroscope, microphone, camera, accelerometer etc. These sensor-enabled smart-phones would form an important element of the future networked-infrastructure. A new wave of services is bound to erupt from such connected infrastructure and smart devices that will influence all aspects of our social ecosystem. In the context of Smart Cities, this position paper and the associated invited talk presents the Mobile Technology perspective of the Smart-city architecture by presenting a conceptualized framework and highlights the open and emerging research challenges in this landscape.
A planar probe double ladder waveguide power divider

The successful demonstration of a 1:4 power divider using microstrip probes and a WR-430 rectangular waveguide is presented. The 15-dB return loss bandwidth of the nonoptimized structure is demonstrated to be 22% and its 0.5-dB insertion loss bandwidth 26%. While realized through conventional machining, such a structure is assembled in a fashion consistent with proven millimeter and submillimeter-wave micromachining techniques. Thus, the structure presents a potential power dividing and power combining architecture, which through micromachining, may be used for applications well above 100GHz.
Deep Sparse Subspace Clustering

In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC). Regularized by the unit sphere distribution assumption for the learned deep features, DSSC can infer a new data affinity matrix by simultaneously satisfying the sparsity principle of SSC and the nonlinearity given by neural networks. One of the appealing advantages brought by DSSC is: when original real-world data do not meet the class-specific linear subspace distribution assumption, DSSC can employ neural networks to make the assumption valid with its hierarchical nonlinear transformations. To the best of our knowledge, this is among the first deep learning based subspace clustering methods. Extensive experiments are conducted on four real-world datasets to show the proposed DSSC is significantly superior to 12 existing methods for subspace clustering.
Shallow Methods for Named Entity Coreference Resolution

Nous nous int́ eressons dans cet article aux m éthodes superficielles de r ́ solution d’anaphores et de construction des cha ı̂nes de ŕ eférence, que nous avons d évelopṕees comme modules du syst̀eme d’extraction d’information ANNIE. La module ”orthomatcher” traite la cor éfé ence orthographique des noms propres et le module de r ésolution d’anaphores traite les anaphores pronominales dont les ant éćedents sont des entit és nomḿees. Tous les deux construisent des châınes de ŕ eférence, i.e. elles identifient des ́ quivalences entre les entit és nomḿees qui apparaissent dans le texte. Les r ésultats de pŕ ecision et de rappel pour l’orthomatcher sont 96/93% pour l’orthomatcher et 66/46% pour la r ésolution d’anaphores pronominaux.
Towards unobtrusive emotion recognition for affective social communication

Awareness of the emotion of those who communicate with others is a fundamental challenge in building affective intelligent systems. Emotion is a complex state of the mind influenced by external events, physiological changes, or relationships with others. Because emotions can represent a user's internal context or intention, researchers suggested various methods to measure the user's emotions from analysis of physiological signals, facial expressions, or voice. However, existing methods have practical limitations to be used with consumer devices, such as smartphones; they may cause inconvenience to users and require special equipment such as a skin conductance sensor. Our approach is to recognize emotions of the user by inconspicuously collecting and analyzing user-generated data from different types of sensors on the smartphone. To achieve this, we adopted a machine learning approach to gather, analyze and classify device usage patterns, and developed a social network service client for Android smartphones which unobtrusively find various behavioral patterns and the current context of users. Also, we conducted a pilot study to gather real-world data which imply various behaviors and situations of a participant in her/his everyday life. From these data, we extracted 10 features and applied them to build a Bayesian Network classifier for emotion recognition. Experimental results show that our system can classify user emotions into 7 classes such as happiness, surprise, anger, disgust, sadness, fear, and neutral with a surprisingly high accuracy. The proposed system applied to a smartphone demonstrated the feasibility of an unobtrusive emotion recognition approach and a user scenario for emotion-oriented social communication between users.
Automatic prediction of frustration

Predicting when a person might be frustrated can provide an intelligent system with important information about when to initiate interaction. For example, an automated Learning Companion or Intelligent Tutoring System might use this information to intervene, providing support to the learner who is likely to otherwise quit, while leaving engaged learners free to discover things without interruption. This paper presents the first automated method that assesses, using multiple channels of affect-related information, whether a learner is about to click on a button saying ‘‘I’m frustrated.’’ The new method was tested on data gathered from 24 participants using an automated Learning Companion. Their indication of frustration was automatically predicted from the collected data with 79% accuracy ðchance 1⁄4 58%Þ. The new assessment method is based on Gaussian process classification and Bayesian inference. Its performance suggests that non-verbal channels carrying affective cues can help provide important information to a system for formulating a more intelligent response.