nils-herrmann/clean_scirepeval huggingface dataset:
Effects of sleep deprivation on procedural errors.

In a large sample (N = 234), we tested effects of 24-hr of sleep deprivation on error rates in a procedural task that requires memory maintenance of task-relevant information. In the evening, participants completed the task under double-blind conditions and then either stayed awake in the lab overnight or slept at home. In the morning, participants completed the task again. Sleep-deprived participants were more likely to suffer a general breakdown in ability (or willingness) to meet a modest accuracy criterion they had met the night before. Among sleep-deprived participants who could still perform the task, error rates were elevated, and errors reflecting memory failures increased with time-on-task. The results suggest that sleep-deprived individuals should not perform procedural tasks associated with interruptions and costly errors-or, if they must, they should perform such tasks only for short periods.
Matter , Mathematics , and God

This paper contrasts materialism and theism in their ability to give a plausible account of mathematics. The ideal, abstract entities of mathematics do not fit readily within a materialist world. Truth, logic, and mathematics require the existence of universal norms. However, materialism has no place for abstract entities or universal norms. How can such ideal norms, inert in themselves, influence our minds? The indispensability of mathematics to physics is a strong argument for realism. Theism posits that God upholds mathematical truths, which reside in the divine mind. Classical mathematics presumes the existence of an Ideal Mathematician—an all-powerful, all-knowing and infinite God. Since theism holds that man is created in God’s image and that God can communicate truth to us, humans may be expected to have access to mathematical knowledge. Since God has created the world according to a rational plan, the world may be expected to have a mathematical structure.
Reconfigurable pipelined 2-D convolvers for fast digital signal processing

In order to make software applications simpler to write and easier to maintain, a software digital signal processing library that performs essential signal and image processing functions is an important part of every DSP developer’s toolset. In general, such a library provides high-level interface and mechanisms, therefore developers only need to known how to use algorithm, not the details of how they work. Then, complex signal transformations become function calls, e.g. C-callable functions. Considering the 2-D convolver function as an example of great significance for DSPs, this work proposes to replace this software function by an emulation on a FPGA initially configured by software programming. Therefore, the exploration of the 2-D convolver’s design space will provide guidelines for the development of a library of DSP-oriented hardware configurations intended to significantly speed-up the performance of general DSP processors. Based on the specific convolver, and considering operators supported in the library as hardware accelerators, a series of trade-offs for efficiently exploiting the bandwidth between the general purpose DSP and the accelerators are proposed. In terms of implementation, this work explores the performance and architectural tradeoffs involved in the design of an FPGA-based 2D convolution coprocessor for the TMS320C40 DSP microprocessor from Texas Instruments. However, the proposed concept is not limited to a particular processor.   The paper will be published in 1999. Personnel use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collectives works for resale or redistribution to servers or lists, or to reuse any copyrithed component of this work in other works, must be obtained from the IEEE.  Manager, Copyrights and Permissions / IEEE service Center / 445 Hoes Lane / P.O. Box 1331 / Pistacataway, NJ 08855-1331, USA.
Affective video content representation and modeling

This paper looks into a new direction in video content analysis - the representation and modeling of affective video content . The affective content of a given video clip can be defined as the intensity and type of feeling or emotion (both are referred to as affect) that are expected to arise in the user while watching that clip. The availability of methodologies for automatically extracting this type of video content will extend the current scope of possibilities for video indexing and retrieval. For instance, we will be able to search for the funniest or the most thrilling parts of a movie, or the most exciting events of a sport program. Furthermore, as the user may want to select a movie not only based on its genre, cast, director and story content, but also on its prevailing mood, the affective content analysis is also likely to contribute to enhancing the quality of personalizing the video delivery to the user. We propose in this paper a computational framework for affective video content representation and modeling. This framework is based on the dimensional approach to affect that is known from the field of psychophysiology. According to this approach, the affective video content can be represented as a set of points in the two-dimensional (2-D) emotion space that is characterized by the dimensions of arousal (intensity of affect) and valence (type of affect). We map the affective video content onto the 2-D emotion space by using the models that link the arousal and valence dimensions to low-level features extracted from video data. This results in the arousal and valence time curves that, either considered separately or combined into the so-called affect curve, are introduced as reliable representations of expected transitions from one feeling to another along a video, as perceived by a viewer.
The Collaborative Economy: A Disruptive Innovation or Much Ado about Nothing?

An economy based on the exchange of capital, assets and services between individuals has grown significantly, spurred by proliferation of internet-based platforms that allow people to share underutilized resources and trade with reasonably low transaction costs. The movement toward this economy of “sharing” translates into market efficiencies that bear new products, reframe established services, have positive environmental effects, and may generate overall economic growth. This emerging paradigm, entitled the collaborative economy, is disruptive to the conventional company-driven economic paradigm as evidenced by the large number of peer-to-peer based services that have captured impressive market shares sectors ranging from transportation and hospitality to banking and risk capital. The panel explores economic, social, and technological implications of the collaborative economy, how digital technologies enable it, and how the massive sociotechnical systems embodied in these new peer platforms may evolve in response to the market and social forces that drive this emerging ecosystem.
Mercury Isotopes as Proxies to Identify Sources and Environmental Impacts of Mercury in Sphalerites

During the past few years, evidence of mass independent fractionation (MIF) for mercury (Hg) isotopes have been reported in the Earth's surface reservoirs, mainly assumed to be formed during photochemical processes. However, the magnitude of Hg-MIF in interior pools of the crust is largely unknown. Here, we reported significant variation in Hg-MIF signature (Δ(199)Hg: -0.24 ~ + 0.18‰) in sphalerites collected from 102 zinc (Zn) deposits in China, indicating that Hg-MIF can be recorded into the Earth's crust during geological recycling of crustal material. Changing magnitudes of Hg-MIF signals were observed in Zn deposits with different formations, evidence that Hg isotopes (especially Hg-MIF) can be a useful tracer to identify sources (syngenetic and epigenetic) of Hg in mineral deposits. The average isotopic composition in studied sphalerites (δ(202)Hg average: -0.58‰; Δ(199)Hg average: +0.03‰) may be used to fingerprint Zn smelting activities, one of the largest global Hg emission sources.
Smartphone-Based Health Technologies for Dementia Care: Opportunities, Challenges, and Current Practices.

Most of the 5.4 million people affected by Alzheimer's disease and other forms of dementia (AD) are noninstitutionalized, receiving care by unpaid family caregivers and medically managed by a primary care provider (PCP). Health Information Technology has been recognized for its potential in improving efficiency and quality of AD care and support for AD caregivers. Simultaneously, smartphone technologies have become an increasingly common way to deliver physical and behavioral health care. However, little is known about how smartphone technologies have been used to support AD caregiving and care. This article highlights the current need for smartphone-based interventions for AD and systematically identified and appraised current smartphone apps targeting and available for AD caregivers. Findings indicate that individual available apps have limited functions (compared with the complex needs of caregivers) and little has been done to extend AD caregiving apps to Hispanic populations. Implications for research, practice, and policy are discussed.
Crime Prediction using K-means Algorithm

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. About 10% of the criminals commit about 50% of the crimes. Even though we cannot predict who all may be the victims of crime but can predict the place that has probability for its occurrence. K-means algorithm is done by partitioning data into groups based on their means. K-means algorithm has an extension called expectation maximization algorithm where we partition the data based on their parameters. This easy to implement data mining framework works with the geospatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. This system can also be used for the Indian crime departments for reducing the crime and solving the crimes with less time.  Prediction, K-Means, Clustering, Data Mining, Crime Prone Areas
Improvements in speech of children with apraxia: The efficacy of a Treatment for Establishing Motor Program Organization (TEMPO)

Childhood apraxia of speech (CAS) is a motor speech disorder characterized by distorted phonemes, segmentation (increased segment and intersegment durations), and impaired production of lexical stress. This study investigated the efficacy of Treatment for Establishing Motor Program Organization (TEMPO) in nine participants (ages 5 to 8) using a delayed treatment group design. Children received four weeks of intervention for four days each week. Experimental probes were administered at baseline and posttreatment—both immediately and one month after treatment—for treated and untreated stimuli. Significant improvements in specific acoustic measures of segmentation and lexical stress were demonstrated following treatment for both the immediate and delayed treatment groups. Treatment effects for all variables were maintained at one-month post-treatment. These results support the demonstrated efficacy of TEMPO in improving the speech of children with CAS.
BEHAVIORAL THEORIES OF LEADERSHIP : AN INTEGRATION AND META-ANALYTIC TEST OF THEIR RELATIVE VALIDITY

The leadership literature suffers from a lack of theoretical integration (Avolio, 2007, American Psychologist, 62, 25–33). This article addresses that lack of integration by developing an integrative trait-behavioral model of leadership effectiveness and then examining the relative validity of leader traits (gender, intelligence, personality) and behaviors (transformational-transactional, initiating structure-consideration) across 4 leadership effectiveness criteria (leader effectiveness, group performance, follower job satisfaction, satisfaction with leader). Combined, leader traits and behaviors explain a minimum of 31% of the variance in leadership effectiveness criteria. Leader behaviors tend to explain more variance in leadership effectiveness than leader traits, but results indicate that an integrative model where leader behaviors mediate the relationship between leader traits and effectiveness is warranted.
1 Sustainable business models : a review 2

A sustainable business model describes the rationale of how an organization creates, 12 delivers, and captures value, in economic, social, cultural or other contexts in a sustainable way. The 13 process of sustainable business model construction forms an innovative part of business strategy. 14 Different industries and business types have utilized sustainability business models to satisfy their 15 economic, environmental and social goals simultaneously. This study is conducted to present the 16 state of the art of sustainable business models in various application areas. The business models are 17 classified and reviewed in different application groups. To do so, a review is conducted, and the 18 findings reveal that the application of sustainability business models can be classified in 16 unique 19 categories. The key contribution of this study is providing an insight about the state of the art of 20 sustainable business models in various application areas and its research path.
An Analysis on Time- and Session-aware Diversification in Recommender Systems

In modern recommender systems, diversity has been widely acknowledged as an important factor to improve user experience and, more recently, intent-aware approaches to diversification have been proposed to provide the user with a list of recommendations covering different aspects of her behavior. In this paper, we propose and analyze the performances of two diversification methods taking into account temporal aspects of the user profile: in the first one we adopt a temporal decay function to emphasize the importance of more recent items in the user profile while in the second one we perform an evaluation based on the identification and analysis of temporal sessions. The two proposed methods have been implemented as temporal variants of the well-known xQuAD framework. In both cases, experimental results on Netflix 100M show an improvement in terms of accuracy-diversity balance.
A Sequential Neural Information Diffusion Model with Structure Attention

In this paper, we propose a novel sequential neural network with structure attention to model information diffusion. The proposed model explores both sequential nature of an information diffusion process and structural characteristics of user connection graph. The recurrent neural network framework is employed to model the sequential information. The attention mechanism is incorporated to capture the structural dependency among users, which is defined as the diffusion context of a user. A gating mechanism is further developed to effectively integrate the sequential and structural information. The proposed model is evaluated on the diffusion prediction task. The performances on both synthetic and real datasets demonstrate its superiority over popular baselines and state-of-the-art sequence-based models.
AutoML Challenge 2015 : Design and First Results

ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing di culty are introduced throughout the six rounds of the challenge. (Participants can enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML.
Design Principles of Augmented Reality Focusing on the Ageing Population

Augmented Reality (AR) is growing rapidly and becoming a mature and robust technology, which combines virtual information with the real environment and real-time performance. It is important to ensure the acceptance and success of augmented reality systems. With the growth of elderly users, evidence shows potential trends for AR systems to support the elderly, including transport, ageing in place, entertainment and training. However, there is a lack of research to provide the theoretical framework or AR design principles to support designers when developing suitable AR applications for specific populations (e.g. older people). In my PhD thesis, I will focus on the possibility of developing and applying AR design principles to support the design of applications that address older people's requirements. In this paper, I first discuss the architecture of augmented reality and identify the relationship between different elements. Secondly, the relevant literature has been reviewed in terms of design challenges of AR and design principles. Thirdly, I formulate the five initial design principles as the fundamental work of my PhD. It is expected that design principles could help AR designers to explore quality design alternatives, which could potentially benefit the ageing population. Fourthly, I identify the AR pillbox as an example to explain how design principles can be applied to AR applications. In terms of the methodology, preparation, refinement and validation are the three main stages to achieve the research goal. Preparation stage aims to generate the preliminary AR design principles and identify the relevant scenarios that might assist the designers to understand the principles and explore the design alternatives. In the stages of refinement, a half-day workshop has been conducted to explore different design issues based on different scenarios and refine the preliminary design principles. After that, a new set of design principles will be formulated. The final stage is to validate the effectiveness of new design principles based on the previous workshop’s feedback.
Counterfeit Integrated Circuits: Detection, Avoidance, and the Challenges Ahead

The counterfeiting of electronic components has become a major challenge in the 21st century. The electronic component supply chain has been greatly affected by widespread counterfeit incidents. A specialized service of testing, detection, and avoidance must be created to tackle the worldwide outbreak of counterfeit integrated circuits (ICs). So far, there are standards and programs in place for outlining the testing, documenting, and reporting procedures. However, there is not yet enough research addressing the detection and avoidance of such counterfeit parts. In this paper we will present, in detail, all types of counterfeits, the defects present in them, and their detection methods. We will then describe the challenges to implementing these test methods and to their effectiveness. We will present several anti-counterfeit measures to prevent this widespread counterfeiting, and we also consider the effectiveness and limitations of these anti-counterfeiting techniques.
Learning Disentangled Representations with Reference-Based Variational Autoencoders

Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically requires to explicitly label all the factors of interest in training images. To alleviate the annotation cost, we introduce a learning setting which we refer to as reference-based disentangling. Given a pool of unlabelled images, the goal is to learn a representation where a set of target factors are disentangled from others. The only supervision comes from an auxiliary reference set containing images where the factors of interest are constant. In order to address this problem, we propose reference-based variational autoencoders, a novel deep generative model designed to exploit the weak-supervision provided by the reference set. By addressing tasks such as feature learning, conditional image generation or attribute transfer, we validate the ability of the proposed model to learn disentangled representations from this minimal form of supervision.
Automatic Cartoon Colorization Based on Convolutional Neural Network

This paper deals with automatic cartoon colorization. This is a hard issue, since it is an ill-posed problem that usually requires user intervention to achieve high quality. Motivated by the recent successes in natural image colorization based on deep learning techniques, we investigate the colorization problem at the cartoon domain using Convolutional Neural Network. To our best knowledge, no existing papers or research studies address this problem using deep learning techniques. Here we investigate a deep Convolutional Neural Network based automatic color filling method for cartoons.
MORE THAN A LINGUISTIC REFERENCE: THE INFLUENCE OF CORPUS TECHNOLOGY ON L2 ACADEMIC WRITING

Hyunsook Yoon Dongguk University, Korea This paper reports on a qualitative study that investigated the changes in students’ writing process associated with corpus use over an extended period of time. The primary purpose of this study was to examine how corpus technology affects students’ development of competence as second language (L2) writers. The research was mainly based on case studies with six L2 writers in an English for Academic Purposes writing course. The findings revealed that corpus use not only had an immediate effect by helping the students solve immediate writing/language problems, but also promoted their perceptions of lexicogrammar and language awareness. Once the corpus approach was introduced to the writing process, the students assumed more responsibility for their writing and became more independent writers, and their confidence in writing increased. This study identified a wide variety of individual experiences and learning contexts that were involved in deciding the levels of the students’ willingness and success in using corpora. This paper also discusses the distinctive contributions of general corpora to English for Academic Purposes and the importance of lexical and grammatical aspects in L2 writing pedagogy.
basic human values an overview

When we think of our values, we think of what is important to us in our lives (e.g., security, independence, wisdom, success, kindness, pleasure). Each of us holds numerous values with varying degrees of importance. A particular value may be very important to one person, but unimportant to another. Consensus regarding the most useful way to conceptualize basic values has emerged gradually since the 1950’s. We can summarize the main features of the conception of basic values implicit in the writings of many theorists and researchers as follows: