nils-herrmann/clean_scirepeval huggingface dataset:
Computing and Visualizing Dynamic Time Warping Alignments in R : The dtw Package

This introduction to the R package dtw is a (slightly) modified version of Giorgino (2009), published in the Journal of Statistical Software. Dynamic time warping is a popular technique for comparing time series, providing both a distance measure that is insensitive to local compression and stretches and the warping which optimally deforms one of the two input series onto the other. A variety of algorithms and constraints have been discussed in the literature. The dtw package provides an unification of them; it allows R users to compute time series alignments mixing freely a variety of continuity constraints, restriction windows, endpoints, local distance definitions, and so on. The package also provides functions for visualizing alignments and constraints using several classic diagram types.
A Conceptual Model of Wayfinding Using Multiple Levels of Abstraction

Wayfinding is part of everyday life. This study concentrates on the development of a conceptual model of human navigation in the U.S. Interstate Highway Network. It proposes three different levels of conceptual understanding that constitute the cognitive map: the Planning Level, the Instructional Level, and the Driver Level. This paper formally defines these three levels and examines the conceptual objects that comprise them. The problem treated here is a simpler version of the open problem of planning and navigating a multi-mode trip. We expect the methods and preliminary results found here for the Interstate system to apply to other systems such as river transportation networks and railroad networks.
Psychological needs and the facilitation of integrative processes.

The assumption that there are innate integrative or actualizing tendencies underlying personality and social development is reexamined. Rather than viewing such processes as either nonexistent or as automatic, I argue that they are dynamic and dependent upon social-contextual supports pertaining to basic human psychological needs. To develop this viewpoint, I conceptually link the notion of integrative tendencies to specific developmental processes, namely intrinsic motivation; internalization; and emotional integration. These processes are then shown to be facilitated by conditions that fulfill psychological needs for autonomy, competence, and relatedness, and forestalled within contexts that frustrate these needs. Interactions between psychological needs and contextual supports account, in part, for the domain and situational specificity of motivation, experience, and relative integration. The meaning of psychological needs (vs. wants) is directly considered, as are the relations between concepts of integration and autonomy and those of independence, individualism, efficacy, and cognitive models of "multiple selves."
Action2Activity: Recognizing Complex Activities from Sensor Data

As compared to simple actions, activities are much more complex, but semantically consistent with a human’s real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a midlevel feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task Learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.
A user-centered approach to evaluating human interaction with Web search engines: an exploratory study

A growing body of studies is developing approaches to evaluating human interaction with Web search engines, including the usability and effectiveness of Web search tools. This study explores a user-centered approach to the evaluation of the Web search engine Inquirus – a Web metasearch tool developed by researchers from the NEC Research Institute. The goal of the study reported in this paper was to develop a user-centered approach to the evaluation including: (1) effectiveness: based on the impact of users' interactions on their information problem and information seeking stage, and (2) usability: including screen layout and system capabilities for users. Twenty-two (22) volunteers searched Inquirus on their own personal information topics. Data analyzed included: (1) user preand post-search questionnaires and (2) Inquirus search transaction logs.  Inquirus was rated highly by users on various usability measures, (2) all users experienced some level of shift/change in their information problem, information seeking, and personal knowledge due to their Inquirus interaction, (3) different users experienced different levels of change/shift, and (4) the search measure precision did not correlate with other user-based measures. Some users experienced major changes/shifts in various userbased variables, such as information problem or information seeking stage with a search of low precision and vice versa. Implications for the development of user-centered approaches to the evaluation of Web and IR systems and further research are discussed.
Towards location-based social networking services

Social networking applications have become very important web services that provide Internet-based platforms for their users to interact with their friends. With the advances in the location-aware hardware and software technologies, location-based social networking applications have been proposed to provide services for their users, taking into account both the spatial and social aspects. Unfortunately, none of existing location-based social networking applications is a holistic system nor equips database management systems to support scalable location-based social networking services. In this paper, we present GeoSocialDB; a holistic system providing three location-based social networking services, namely, location-based news feed, location-based news ranking, and location-based recommendation. In GeoSocialDB, we aim to implement these services as query operators inside a database engine to optimize the query processing performance. Within the GeoSocialDB framework, we discuss research challenges and directions towards the realization of scalable and practical query processing for location-based social networking services. In general, we discuss the challenges in designing location- and/or rank-aware query operators, materializing query answers, supporting continuous query processing, and providing privacy-aware query processing for our three location-based social networking services.
Semantic Image Segmentation via Deep Parsing Network

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy of 77.5%.
Configuration Checking with Aspiration in Local Search for SAT

An interesting strategy called configuration checking (CC) was recently proposed to handle the cycling problem in local search for Minimum Vertex Cover. A natural question is whether this CC strategy also works for SAT. The direct application of CC did not result in stochastic local search (SLS) algorithms that can compete with the current best SLS algorithms for SAT. In this paper, we propose a new heuristic based on CC for SLS algorithms for SAT, which is called configuration checking with aspiration (CCA). It is used to develop a new SLS algorithm called Swcca. The experiments on random 3-SAT instances show that Swcca significantly outperforms Sparrow2011, the winner of the random satisfiable category of the SAT Competition 2011, which is considered to be the best local search solver for random 3-SAT instances. Moreover, the experiments on structured instances show that Swcca is competitive with Sattime, the best local search solver for the crafted benchmark in the SAT Competition 2011.
SPORC: Group Collaboration using Untrusted Cloud Resources

Cloud-based services are an attractive deployment model for user-facing applications like word processing and calendaring. Unlike desktop applications, cloud services allow multiple users to edit shared state concurrently and in real-time, while being scalable, highly available, and globally accessible. Unfortunately, these benefits come at the cost of fully trusting cloud providers with potentially sensitive and important data. To overcome this strict tradeoff, we present SPORC, a generic framework for building a wide variety of collaborative applications with untrusted servers. In SPORC, a server observes only encrypted data and cannot deviate from correct execution without being detected. SPORC allows concurrent, low-latency editing of shared state, permits disconnected operation, and supports dynamic access control even in the presence of concurrency. We demonstrate SPORC’s flexibility through two prototype applications: a causally-consistent key-value store and a browser-based collaborative text editor. Conceptually, SPORC illustrates the complementary benefits of operational transformation (OT) and fork* consistency. The former allows SPORC clients to execute concurrent operations without locking and to resolve any resulting conflicts automatically. The latter prevents a misbehaving server from equivocating about the order of operations unless it is willing to fork clients into disjoint sets. Notably, unlike previous systems, SPORC can automatically recover from such malicious forks by leveraging OT’s conflict resolution mechanism.
Automatic Headline Generation for Newspaper Stories

In this paper we propose a novel application of Hidden Markov Models to automatic generation of informative headlines for English texts. We propose four decoding parameters to make the headlines appear more like Headlinese, the language of informative newspaper headlines. We also allow for morphological variation in words between headline and story English. Informal and formal evaluations indicate that our approach produces informative headlines, mimicking a Headlinese style generated by humans.
A 2.1-to-2.8-GHz Low-Phase-Noise All-Digital Frequency Synthesizer With a Time-Windowed Time-to-Digital Converter

A 2.1-to-2.8-GHz low-power consumption all-digital phase locked loop (ADPLL) with a time-windowed time-to-digital converter (TDC) is presented. The time-windowed TDC uses a two-step structure with an inverter- and a Vernier-delay time-quantizer to improve time resolution, which results in low phase noise. Time-windowed operation is implemented in the TDC, in which a single-shot pulse-based operation is used for low power consumption. The test chip implemented in 90-nm CMOS technology exhibits in-band phase noise of , where the loop-bandwidth is set to 500 kHz with a 40-MHz reference signal, and out-band noise of at a 1-MHz offset frequency. The chip core occupies 0.37 and the measured power consumption is 8.1 mA from a 1.2-V power supply.
The Turn - Integration of Information Seeking and Retrieval in Context

Search Engines: Information Retrieval In Practice is writen by Bruce Croft in English language. Release on 2009-02-16, this book has 552 page count that consist of helpful information with easy reading experience. The book was publish by Addison-Wesley, it is one of best subjects book genre that gave you everything love about reading. You can find Search Engines: Information Retrieval In Practice book with ISBN 0136072240.
A Report on the First Native Language Identification Shared Task

Native Language Identification, or NLI, is the task of automatically classifying the L1 of a writer based solely on his or her essay written in another language. This problem area has seen a spike in interest in recent years as it can have an impact on educational applications tailored towards non-native speakers of a language, as well as authorship profiling. While there has been a growing body of work in NLI, it has been difficult to compare methodologies because of the different approaches to pre-processing the data, different sets of languages identified, and different splits of the data used. In this shared task, the first ever for Native Language Identification, we sought to address the above issues by providing a large corpus designed specifically for NLI, in addition to providing an environment for systems to be directly compared. In this paper, we report the results of the shared task. A total of 29 teams from around the world competed across three different sub-tasks.
From SPARQL to rules (and back)

As the data and ontology layers of the Semantic Web stack have achieved a certain level of maturity in standard recommendations such as RDF and OWL, the current focus lies on two related aspects. On the one hand, the definition of a suitable query language for RDF, SPARQL, is close to recommendation status within the W3C. The establishment of the rules layer on top of the existing stack on the other hand marks the next step to be taken, where languages with their roots in Logic Programming and Deductive Databases are receiving considerable attention. The purpose of this paper is threefold. First, we discuss the formal semantics of SPARQLextending recent results in several ways. Second, weprovide translations from SPARQL to Datalog with negation as failure. Third, we propose some useful and easy to implement extensions of SPARQL, based on this translation. As it turns out, the combination serves for direct implementations of SPARQL on top of existing rules engines as well as a basis for more general rules and query languages on top of RDF.
State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

The computer graphics and vision communities have dedicated long standing efforts in building computerized tools for reconstructing, tracking, and analyzing human faces based on visual input. Over the past years rapid progress has been made, which led to novel and powerful algorithms that obtain impressive results even in the very challenging case of reconstruction from a single RGB or RGB-D camera. The range of applications is vast and steadily growing as these technologies are further improving in speed, accuracy, and ease of use. Motivated by this rapid progress, this state-of-the-art report summarizes recent trends in monocular facial performance capture and discusses its applications, which range from performance-based animation to real-time facial reenactment. We focus our discussion on methods where the central task is to recover and track a three dimensional model of the human face using optimization-based reconstruction algorithms. We provide an in-depth overview of the underlying concepts of real-world image formation, and we discuss common assumptions and simplifications that make these algorithms practical. In addition, we extensively cover the priors that are used to better constrain the under-constrained monocular reconstruction problem, and discuss the optimization techniques that are employed to recover dense, photo-geometric 3D face models from monocular 2D data. Finally, we discuss a variety of use cases for the reviewed algorithms in the context of motion capture, facial animation, as well as image and video editing. CCS Concepts •Computing methodologies → Reconstruction; Tracking; Motion capture; Shape modeling; 3D imaging;
Complex network approach for recurrence analysis of time series

We propose a novel approach for analysing time series using complex network theory. We identify the recurrence matrix (calculated from time series) with the adjacency matrix of a complex network and apply measures for the characterisation of complex networks to this recurrence matrix. By using the logistic map, we illustrate the potential of these complex network measures for the detection of dynamical transitions. Finally, we apply the proposed approach to a marine palaeo-climate record and identify the subtle changes to the climate regime.
8 A 236 nW-56 . 5 dBm-Sensitivity Bluetooth Low-Energy Wakeup Receiver with Energy Harvesting in 65 nm CMOS

Batteryless operation and ultra-low-power (ULP) wireless communication will be two key enabling technologies as the IC industry races to keep pace with the IoE projections of 1T-connected sensors by 2025. Bluetooth Low-Energy (BLE) is used in many consumer IoE devices now because it offers the lowest average power for a radio that can communicate directly to a mobile device . The BLE standard requires that the IoE device continuously advertises, which initiates the connection to a mobile device. Sub-1s advertisement intervals are common to minimize latency. However, this continuous advertising results in a typical minimum average power of 10’s of μW at low duty-cycles. This leads to the quoted 1-year lifetimes of event-driven IoE devices (e.g. tracking tags, ibeacons) that operate from coin-cell batteries. This minimum power is too high for robust, batteryless operation in a small form-factor.
Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services

Bringing smartness into tourism destinations requires dynamically interconnecting stakeholders through a technological platform on which information relating to tourism activities could be exchanged instantly. Instant information exchange has also created extremely large data sets known as Big Data that may be analysed computationally to reveal patterns and trends. Smart Tourism Destinations should make an optimal use of Big Data by offering right services that suit users’ preference at the right time. In relation thereto, this paper aims at contributing to the understanding on how Smart Tourism Destinations could potentially enhance tourism experience through offering products/services that are more personalised to meet each of visitor’s unique needs and preferences. Understanding the needs, wishes and desires of travellers becomes increasingly critical for the competitiveness of destinations. Therefore, the findings in the present research are insightful for number of tourism destinations.
An Assignment-Based Approach to Efficient Real-Time City-Scale Taxi Dispatching

This study proposes and evaluates an efficient real-time taxi dispatching strategy that solves the linear assignment problem to find a globally optimal taxi-to-request assignment at each decision epoch. The authors compare the assignment-based strategy with two popular rule-based strategies. They evaluate dispatching strategies in detail in the city of Berlin and the neighboring region of Brandenburg using the microscopic large-scale MATSim simulator. The assignment-based strategy produced better results for both drivers (less idle driving) and passengers (less waiting). However, computing the assignments for thousands of taxis in a huge road network turned out to be computationally demanding. Certain adaptations pertaining to the cost matrix calculation were necessary to increase the computational efficiency and assure real-time responsiveness.
Face off: Implications of visual cues on initiating friendship on Facebook

This research investigates how moderating factors and theoretically relevant contextual variables affect impression formation and the willingness to initiate virtual friendship. An experiment examined both main and interaction effects for visual cues, profile owner’s gender, and evaluator’s gender; a 2 (stimulus gender: male and female) 3 (visual conditions: attractive, unattractive, and no-photo) 2 (evaluator’s gender: male and female) between subjects model analysis of variance (ANOVA) was employed. A threeway interaction between gender and appearance was revealed. The results indicated that both male and female subjects were more willing to initiate friendships with opposite-sex profile owners with attractive photos. Subjects also displayed comparatively higher willingness to make friends with profile owners who did not include visual cues than with those who revealed an unattractive photo. The hyperpersonal model was supported and extended to address gender attributes.
MEC: Memory-efficient Convolution for Deep Neural Network

Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor performance. As an alternative, multiple indirect methods have been proposed including im2colbased convolution, FFT-based convolution, or Winograd-based algorithm. However, all these indirect methods have high memory-overhead, which creates performance degradation and offers a poor trade-off between performance and memory consumption. In this work, we propose a memory-efficient convolution or MEC with compact lowering, which reduces memoryoverhead substantially and accelerates convolution process. MEC lowers the input matrix in a simple yet efficient/compact way (i.e., much less memory-overhead), and then executes multiple small matrix multiplications in parallel to get convolution completed. Additionally, the reduced memory footprint improves memory subsystem efficiency, improving performance. Our experimental results show that MEC reduces memory consumption significantly with good speedup on both mobile and server platforms, compared with other indirect convolution algorithms.
Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition

In this paper, we present a Character-Aware Neural Network (Char-Net) for recognizing distorted scene text. Our CharNet is composed of a word-level encoder, a character-level encoder, and a LSTM-based decoder. Unlike previous work which employed a global spatial transformer network to rectify the entire distorted text image, we take an approach of detecting and rectifying individual characters. To this end, we introduce a novel hierarchical attention mechanism (HAM) which consists of a recurrent RoIWarp layer and a characterlevel attention layer. The recurrent RoIWarp layer sequentially extracts a feature region corresponding to a character from the feature map produced by the word-level encoder, and feeds it to the character-level encoder which removes the distortion of the character through a simple spatial transformer and further encodes the character region. The character-level attention layer then attends to the most relevant features of the feature map produced by the characterlevel encoder and composes a context vector, which is finally fed to the LSTM-based decoder for decoding. This approach of adopting a simple local transformation to model the distortion of individual characters not only results in an improved efficiency, but can also handle different types of distortion that are hard, if not impossible, to be modelled by a single global transformation. Experiments have been conducted on six public benchmark datasets. Our results show that CharNet can achieve state-of-the-art performance on all the benchmarks, especially on the IC-IST which contains scene text with large distortion. Code will be made available.