nils-herrmann/clean_scirepeval huggingface dataset:
A gradient of childhood self-control predicts health, wealth, and public safety.

Policy-makers are considering large-scale programs aimed at self-control to improve citizens' health and wealth and reduce crime. Experimental and economic studies suggest such programs could reap benefits. Yet, is self-control important for the health, wealth, and public safety of the population? Following a cohort of 1,000 children from birth to the age of 32 y, we show that childhood self-control predicts physical health, substance dependence, personal finances, and criminal offending outcomes, following a gradient of self-control. Effects of children's self-control could be disentangled from their intelligence and social class as well as from mistakes they made as adolescents. In another cohort of 500 sibling-pairs, the sibling with lower self-control had poorer outcomes, despite shared family background. Interventions addressing self-control might reduce a panoply of societal costs, save taxpayers money, and promote prosperity.
Multicriteria Spatial Decision Support Systems : Overview , Applications , and Future Research Directions

Decision makers historically have indicated that inaccessibility of required geographic data and difficulties in synthesizing various recommendations are primary obstacles to spatial problem solving. Studies have shown that the quality of decisions (i.e., the ability to produce meaningful solutions) can be improved if these obstacles are lessened or removed through an integrated systems approach, such as a spatial decision support system (SDSS). In addition, multicriteria decision making (MCDM) and a wide range of related methodologies offer a variety of techniques and practices to uncover and integrate decision makers’ preferences in order to solve “real-world” GIS-based planning and management problems. However, because of conceptual difficulties (i.e., dynamic preference structures and large decision alternative and evaluation criteria sets) involved in formulating and solving spatial decision problems, researchers have developed multicriteria-spatial decision support systems (MC-SDSS). In this paper, we present a general overview of MC-SDSS, briefly review applications of MC-SDSS to a broad range of decision problems, and provide direction for future trends and research in this area.
Online and Incremental Appearance-based SLAM in Highly Dynamic Environments

This paper presents a novel method for online and incremental appearance-based localization and mapping in a highly dynamic environment. Using position-invariant robust features (PIRFs), the method can achieve a high rate of recall with 100% precision. It can handle both strong perceptual aliasing and dynamic changes of places efficiently. Its performance also extends beyond conventional images; it is applicable to omnidirectional images for which the major portions of scenes are similar for most places. The proposed PIRF-based Navigation method named PIRF-Nav is evaluated by testing it on two standard datasets as is in FAB-MAP and on an additional omnidirectional image dataset that we collected. This extra dataset is collected on two days with different specific events, i.e., an open-campus event, to present challenges related to illumination variance and strong dynamic changes, and to test assessment of dynamic scene changes. Results show that PIRF-Nav outperforms FAB-MAP; PIRF-Nav at precision-1 yields a recall rate about two times (approximately 80%) higher than that of FAB-MAP. Its computation time is sufficiently short for real-time applications. The method is fully incremental, and requires no offline process for dictionary creation. Additional testing using combined datasets proves that PIRF-Nav can function over a long term and can solve the kidnapped robot problem.
On Uncertainty, Ambiguity, and Complexity in Project Management

This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff. A key concept is the adequacy of the available information about states of the world and action effects. We express uncertainty, ambiguity, and complexity in terms of information adequacy. We identify three fundamental project management strategies: instructionism, learning, and selectionism. We show that classic project management methods emphasize adequate information and instructionism, and demonstrate how modern methods fit into the three fundamental strategies. The appropriate strategy is contingent on the type of uncertainty present and the complexity of the project payoff function. Our model establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructure—that is, systems for planning, coordination and incentives, and monitoring. (Project Management; Uncertainty; Complexity; Instructionalism; Project Selection; Ambiguity )
Anita: An Intelligent Text Adaptation Tool

We introduce Anita: a flexible and intelligent Text Adaptation tool for web content that provides Text Simplification and Text Enhancement modules. Anita’s simplification module features a state-of-the-art system that adapts texts according to the needs of individual users, and its enhancement module allows the user to search for a word’s definitions, synonyms, translations, and visual cues through related images. These utilities are brought together in an easy-to-use interface of a freely available web browser extension.
Beyond the Kalman Filter: Particle Filters for Tracking Applications

Target tracking is an important element of surveillance, guidance or obstacle avoidance, whose role is to determine the number, position and movement of targets. The fundamental building block of a tracking system is a filter for recursive state estimation. The Kalman filter has been flogged to death as the work-horse of tracking systems since its formulation in the 60's. In this talk we look beyond the Kalman filter at sequential Monte Carlo methods, collectively referred to as particle filters. Particle filters have become a popular method for stochastic dynamic estimation problems. This popularity can be explained by a wave of optimism among practitioners that traditionally difficult nonlinear/non-Gaussian dynamic estimation problems can now be solved accurately and reliably using this methodology. The computational cost of particle filters have often been considered their main disadvantage, but with ever faster computers and more efficient particle filter algorithms, this argument is becoming less relevant. The talk is organized in two parts. First we review the historical development and current status of particle filtering and its relevance to target tracking. We then consider in detail several tracking applications where conventional (Kalman based) methods appear inappropriate (unreliable or inaccurate) and where we instead need the potential benefits of particle filters. 1 The paper was written together with David Salmond,
Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles

Today's vehicles are becoming cyber-physical systems that not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies, and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is forensic analysis of traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to discover the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information in cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate vehicular public key infrastructure (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicles such as maintenance information/ history, car diagnosis reports, and so on. The proposed forensic framework enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead.
A Low Voltage RISC-V Heterogeneous System Boosted SRAMs , Machine Learning , and Fault Injection on VELOUR

Architectural research traditionally requires a reliance on abstractions, analytical tools, and models. However, the emerging open source hardware community centered around RISC-V, has enabled new, more intrusive approaches to architectural research characterized by: reuse of existing designs, new languages/tools, and workows. We provide, by means of a case study, details of ongoing work for the DARPA PERFECT project at IBM and the University of Virginia to develop a heterogeneous RISC-V-based system, VELOUR, capable of operating at very low voltage. VELOUR encompasses an open-source RISC-V microprocessor (Rocket) with a tightly coupled machine learning accelerator (DANA), a power/resiliency management unit (PRIME), low voltage SRAMs, and instrumentation using new tools and languages via critical path monitors (CPMs) and power proxy sensors. Evaluation of low voltage operation occurs with a new instrumentation framework for targeted latch-level fault-injection in FPGA (CHIFFRE). CCS CONCEPTS •Hardware → Process, voltage and temperature variations; •Computer systems organization→ Neural networks; Processors and memory architectures;
VinySLAM: An indoor SLAM method for low-cost platforms based on the Transferable Belief Model

A truly autonomous mobile robot have to solve the SLAM problem (i.e. simultaneous map building and pose estimation) in order to navigate in an unknown environment. Unfortunately, a universal solution for the problem hasn't been proposed yet. The tinySLAM algorithm that has a compact and clear code was designed to solve SLAM in an indoor environment using a noisy laser scanner. This paper introduces the vinySLAM method that enhances tinySLAM with the Transferable Belief Model to improve its robustness and accuracy. Proposed enhancements affect scan matching and occupancy tracking keeping simplicity and clearness of the original code. The evaluation on publicly available datasets shows significant robustness and accuracy improvements.
A pattern language for error management in voice user interfaces

Speech is not recognized with an accuracy of 100%. Even humans are not able to do that. There will always be some uncertainty in the recognized input, requiring strategies to cope. This is different from the experience with graphical user interfaces, where keyboard and mouse input are recognized without any doubts. Speech recognition and other errors occur frequently and reduce both the usefulness of applications and user satisfaction. This turns error handling into a crucial aspect of speech applications. Successful error handling methods can make even applications with poor recognition accuracy more successful. In [Sagawa et al. 2004] the authors show that the task completion rate increased from 86.4% to 93.4% and the average number of turns reduced by three after a better error handling method had been installed. On the other hand, poorly constructed error handling may bring unwanted complexity to the system and cause new errors and annoyances.
Relation Classification via Convolutional Deep Neural Network

State-of-the-art methods for relation classification are mostly based on statistical machine learning, and the performance heavily depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing NLP systems, which lead to the error propagation of existing tools and hinder the performance of the system. In this paper, we exploit a convolutional Deep Neural Network (DNN) to extract lexical and sentence level features. Our method takes all the word tokens as input without complicated pre-processing. First, all the word tokens are transformed to vectors by looking up word embeddings1. Then, lexical level features are extracted according to the given nouns. Meanwhile, sentence level features are learned using a convolutional approach. These two level features are concatenated as the final extracted feature vector. Finally, the features are feed into a softmax classifier to predict the relationship between two marked nouns. Experimental results show that our approach significantly outperforms the state-of-the-art methods.
A New Compact and High Gain Circularly-Polarized Slot Antenna Array for Ku-Band Mobile Satellite TV Reception

A compact and high-gain SIW-fed circularly polarized (CP) slot-antenna array with a stacked feed structure is presented for the application of Ku-band high-data-rate satellite communications. First, a novel probe-fed SIW cavity with four slots etched on the top surface is proposed as a high-gain radiating element for the array. The four slots in the cavity act as a <inline-formula> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula> array, and its directivity is 2.15 and 1.43 dB greater than that of the cavity-backed antenna of the same size using ring slot and split ring slot, respectively. Second, a compact 1–4 SIW power divider is designed for exciting a subarray. Third, the <inline-formula> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula> subarray is further expanded to an <inline-formula> <tex-math notation="LaTeX">$8\times16$ </tex-math></inline-formula> array by adopting an additional layer of 1–32 SIW feeding network to meet the gain requirement of the Ku-band mobile satellite TV reception. Finally, experiments are carried out to verify the designed prototypes. Measured results show that proposed 128-element array has a relative impedance bandwidth of 4.8% (11.84 to 12.42 GHz), AR bandwidth of 130 MHz (12.01 to 12.14 GHz), and a peak gain of 26.8 dBic at 12.06 GHz. Owing to the simple feeding networks and the compact radiating element, the antenna has a compact size of <inline-formula> <tex-math notation="LaTeX">$6.04\lambda _{ {0}} \times 11.96\lambda _{ {0}} \times 0.1\lambda _{ {0}}$ </tex-math></inline-formula>. Experimental results show that the proposed CP antenna array is suitable for applications of Ku-band mobile satellite TV reception.
Multimedia Big Data: Content Analysis and Retrieval

This chapter surveys recent developments in the area of multimedia big data, the biggest big data. One core problem is how to best process this multimedia big data in an efficient and scalable way. We outline examples of the use of the MapReduce framework, including Hadoop, which has become the most common approach to a truly scalable and efficient framework for common multimedia processing tasks, e.g., content analysis and retrieval. We also examine recent developments on deep learning which has produced promising results in large-scale multimedia processing and retrieval. Overall the focus has been on empirical studies rather than the theoretical so as to highlight the most practically successful recent developments and highlight the associated caveats or lessons learned.
Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Credit scoring has been regarded as a core appraisal tool of different institutions during the last few decades, and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria are investigated in the literature. The principal aim of this paper is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas, in general, but primarily in finance and banking, in particular. This paper also aims to investigate how credit scoring has developed in importance, and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients‟ behaviour. Finally, this paper also suggests a number of directions for future research.
L-Theanine reduces psychological and physiological stress responses.

L-Theanine is an amino acid contained in green tea leaves which is known to block the binding of L-glutamic acid to glutamate receptors in the brain. Because the characteristics of L-Theanine suggest that it may influence psychological and physiological states under stress, the present study examined these possible effects in a laboratory setting using a mental arithmetic task as an acute stressor. Twelve participants underwent four separate trials: one in which they took L-Theanine at the start of an experimental procedure, one in which they took L-Theanine midway, and two control trials in which they either took a placebo or nothing. The experimental sessions were performed by double-blind, and the order of them was counterbalanced. The results showed that L-Theanine intake resulted in a reduction in the heart rate (HR) and salivary immunoglobulin A (s-IgA) responses to an acute stress task relative to the placebo control condition. Moreover, analyses of heart rate variability indicated that the reductions in HR and s-IgA were likely attributable to an attenuation of sympathetic nervous activation. Thus, it was suggested that the oral intake of L-Theanine could cause anti-stress effects via the inhibition of cortical neuron excitation.
Big data, advanced analytics and the future of comparative effectiveness research.

The intense competition that accompanied the growth of internet-based companies ushered in the era of 'big data' characterized by major innovations in processing of very large amounts of data and the application of advanced analytics including data mining and machine learning. Healthcare is on the cusp of its own era of big data, catalyzed by the changing regulatory and competitive environments, fueled by growing adoption of electronic health records, as well as efforts to integrate medical claims, electronic health records and other novel data sources. Applying the lessons from big data pioneers will require healthcare and life science organizations to make investments in new hardware and software, as well as in individuals with different skills. For life science companies, this will impact the entire pharmaceutical value chain from early research to postcommercialization support. More generally, this will revolutionize comparative effectiveness research.
A classification of natural rivers

A classification system for natural rivers is presented in which a morphological arrangement of stream characteristics is organized into relatively homogeneous stream types. This paper describes morphologically similar stream reaches that are divided into 7 major stream type categories that differ in entrenchment, gradient, width/depth ratio, and sinuosity in various landforms. Within each major category are six additional types delineated by dominate channel materials from bedrock to silt/clay along a continuum of gradient ranges. Recent stream type data used to further define classification interrelationships were derived from 450 rivers throughout the U.S, Canada, and New Zealand. Data used in the development of this classification involved a great diversity of hydro-physiographic/geomorphic provinces from small to large rivers and in catchments from headwater streams in the mountains to the coastal plains. A stream hierarchical inventory system is presented which utilizes the stream classification system. Examples for use of this stream classification system for engineering, fish habitat enhancement, restoration and water resource management applications are presented. Specific examples of these applications include hydraulic geometry relations, sediment supply/availability, fish habitat structure evaluation, flow resistance, critical shear stress estimates, shear stress/velocity relations, streambank erodibility potential, management interpretations, sequences of morphological evolution, and river restoration principles. 1. General statement
Impact of Brand Awareness , Perceived Quality and Customer Loyalty on Brand Profitability and Purchase Intention : A Resellers ’ View

The study is to investigate resellers’ point of view about the impact of brand awareness, perceived quality and customer loyalty on brand profitability and purchase intention. Further the study is also focused on finding out the mediating role of purchase intension on the relationship of brand awareness and profitability, perceived quality and profitability and brand loyalty and profitability. The study was causal in nature and data was collected from 200 resellers. The results showed insignificant impact of brand awareness and loyalty whereas significant impact of perceived quality on profitability. Further the results revealed significant impact of brand awareness, perceived quality and loyalty on purchase intention. Sobel test for mediation showed that purchase intension mediates the relationship of the perceived quality and profitability only.
Pixel-Based Skin Detection for Pornography Filtering

A robust skin detector is the primary need of many fields of computer vision, including face detection, gesture recognition, and pornography filtering. Less than 10 years ago, the first paper on automatic pornography filtering was published. Since then, different researchers claim different color spaces to be the best choice for skin detection in pornography filtering. Unfortunately, no comprehensive work is performed on evaluating different color spaces and their performance for detecting naked persons. As such, researchers usualy refer to the results of skin detection based on the work doen for face detection, which underlies different imaging conditions. In this paper, we examine 21 color spaces in all their possible representations for pixel-based skin detection in pornographic images. Consequently, this paper holds a large investigation in the field of skin detection, and a specific run on the pornographic images.