nils-herrmann/clean_scirepeval huggingface dataset:
Data Curation with Deep Learning [Vision]: Towards Self Driving Data Curation

Past. Data curation – the process of discovering, integrating, and cleaning data – is one of the oldest data management problems. Unfortunately, it is still the most time consuming and least enjoyable work of data scientists. So far, successful data curation stories are mainly ad-hoc solutions that are either domain-specific (for example, ETL rules) or task-specific (for example, entity resolution). Present. In the era of big data, data curation plays the critical role of taking the value of big data to a new level. However, the power of current data curation solutions are not keeping up with the ever changing data ecosystem in terms of volume, velocity, variety and veracity, mainly due to the high human cost, instead of machine cost, needed for providing the ad-hoc solutions mentioned above. Meanwhile, deep learning is making strides in achieving remarkable successes in areas such as image recognition, natural language processing, and speech recognition. This is largely due to its ability of (automatically) understanding data (features) that are neither domain-specific nor task-specific. Future. Data curation solutions need to keep the pace with the fast-changing data ecosystem, where the main hope is to devise domain-agnostic and task-agnostic solutions. To this end, we start a new, five-year research project, called AutoDC, to unleash the potential of deep learning towards self-driving data curation. We will discuss how different deep learning concepts (for example, distributed representations, model pre-training, transfer learning, and neural program synthesis) can be adapted and extended to solve various data curation problems. We will also showcase some low-hanging fruits about the early encounters between deep learning and data curation happening in AutoDC. We believe that the directions pointed out by this work will not only drive AutoDC towards democratizing data curation, but also serve as a cornerstone for researchers and practitioners to move to a new realm of data curation solutions.  Saravanan Thirumuruganathan, Nan Tang & Mourad Ouzzani. Data Curation with Deep Learning. PVLDB, 11 (5): xxxx-yyyy, 2018.
Psychometric properties of the Spanish version of the UPPS-P Impulsive Behavior Scale: A Rasch rating scale analysis and confirmatory factor analysis.

The present study was aimed at determining the psychometric properties of the Spanish version of the UPPS-P Impulsive Behavior Scale in a sample of college students. Participants were 318 college students (36.2% men; mean age = 20.9 years, SD = 6.4 years). The psychometric properties of this Spanish version were analyzed using the Rasch model, and the factor structure was examined using confirmatory factor analysis. The verification of the global fit of the data showed adequate indexes for persons and items. The reliability estimates were high for both items and persons. Differential item functioning across gender was found for 23 items, which likely reflects known differences in impulsivity levels between men and women. The factor structure of the Spanish version of the UPPS-P replicates previous work with the original UPPS-P Scale. Overall, results suggest that test scores from the Spanish version of the UPPS-P show adequate psychometric properties to accurately assess the multidimensional model of impulsivity, which represents the most exhaustive measure of this construct.
AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model compression techniques rely on hand-crafted heuristics and rule-based policies that require domain experts to explore the large design space trading off among model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Model Compression (AMC) which leverage reinforcement learning to provide the model compression policy. This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor. Under 4× FLOPs reduction, we achieved 2.7% better accuracy than the handcrafted model compression policy for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved 1.81× speedup of measured inference latency on an Android phone and 1.43× speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 accuracy.
Nutrigenomics: the Rubicon of molecular nutrition.

The success of the Human Genome Project and the powerful tools of molecular biology have ushered in a new era of medicine and nutrition. The pharmaceutical industry expects to leverage data from the Human Genome Project to develop new drugs based on the genetic constitution of the patient; likewise, the food industry has an opportunity to position food and nutritional bioactives to promote health and prevent disease based on the genetic constitution of the consumer. This new era of molecular nutrition--that is, nutrient-gene interaction--can unfold in dichotomous directions. One could focus on the effects of nutrients or food bioactives on the regulation of gene expression (ie, nutrigenomics) or on the impact of variations in gene structure on one's response to nutrients or food bioactives (ie, nutrigenetics). The challenge of the public health nutritionist will be to balance the needs of the community with those of the individual. In this regard, the excitement and promise of molecular nutrition should be tempered by the need to validate the scientific data emerging from the disciplines of nutrigenomics and nutrigenetics and the need to educate practitioners and communicate the value to consumers-and to do it all within a socially responsible bioethical framework.
Denying humanness to others: a newly discovered mechanism by which violent video games increase aggressive behavior.

Past research has provided abundant evidence that playing violent video games increases aggressive behavior. So far, these effects have been explained mainly as the result of priming existing knowledge structures. The research reported here examined the role of denying humanness to other people in accounting for the effect that playing a violent video game has on aggressive behavior. In two experiments, we found that playing violent video games increased dehumanization, which in turn evoked aggressive behavior. Thus, it appears that video-game-induced aggressive behavior is triggered when victimizers perceive the victim to be less human.
DeepRED - Rule Extraction from Deep Neural Networks

Neural network classifiers are known to be able to learn very accurate models. In the recent past, researchers have even been able to train neural networks with multiple hidden layers (deep neural networks) more effectively and efficiently. However, the major downside of neural networks is that it is not trivial to understand the way how they derive their classification decisions. To solve this problem, there has been research on extracting better understandable rules from neural networks. However, most authors focus on nets with only one single hidden layer. The present paper introduces a new decompositional algorithm – DeepRED – that is able to extract rules from deep neural networks. The evaluation of the proposed algorithm shows its ability to outperform a pedagogical baseline on several tasks, including the successful extraction of rules from a neural network realizing the XOR function.
Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function.

Medial prefrontal cortex (MPFC) is among those brain regions having the highest baseline metabolic activity at rest and one that exhibits decreases from this baseline across a wide variety of goal-directed behaviors in functional imaging studies. This high metabolic rate and this behavior suggest the existence of an organized mode of default brain function, elements of which may be either attenuated or enhanced. Extant data suggest that these MPFC regions may contribute to the neural instantiation of aspects of the multifaceted "self." We explore this important concept by targeting and manipulating elements of MPFC default state activity. In this functional magnetic resonance imaging (fMRI) study, subjects made two judgments, one self-referential, the other not, in response to affectively normed pictures: pleasant vs. unpleasant (an internally cued condition, ICC) and indoors vs. outdoors (an externally cued condition, ECC). The ICC was preferentially associated with activity increases along the dorsal MPFC. These increases were accompanied by decreases in both active task conditions in ventral MPFC. These results support the view that dorsal and ventral MPFC are differentially influenced by attentiondemanding tasks and explicitly self-referential tasks. The presence of self-referential mental activity appears to be associated with increases from the baseline in dorsal MPFC. Reductions in ventral MPFC occurred consistent with the fact that attention-demanding tasks attenuate emotional processing. We posit that both self-referential mental activity and emotional processing represent elements of the default state as represented by activity in MPFC. We suggest that a useful way to explore the neurobiology of the self is to explore the nature of default state activity.
A Dual-Band SP6T T/R Switch in SOI CMOS With 37-dBm ${ P}_{-0.1\ {\rm {dB}}}$ for GSM/W-CDMA Handsets

This paper presents the circuit techniques to achieve superior linearity and isolation for a single-pole six-throw transmit/receive (T/R) switch designed for GSM/W-CDMA dual-band operation at 0.85-0.9 and 1.8-1.9 GHz. Implemented in a 0.18- μm thick-film silicon-on-insulator (SOI) CMOS process, the switch employs an LC-tuned asymmetric topology for the transmit (Tx) and receive (Rx) branch to handle the high-power GSM transmitter requirement. The proposed design also features a switchable double LC-tank acting as a variable impedance block to relax the tradeoff among linearity, insertion loss (IL), and isolation. Feed-forward capacitors, ac-floating bias techniques, and floating-body SOI devices are utilized to further improve the linearity. The measured P-0.1 dB, IL and Tx-Rx isolation in the lower and upper band are 37.2-35.6 dBm, 0.43-0.75 dB, and 45-37 dB, respectively. The proposed T/R switch design in SOI CMOS is an important building block toward more compact and lower cost RF frond-end modules, which integrate the switch, antenna tuning module, and control logic on the same chip.
Lossless Compression for Text and ImagesAlistair

Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images|particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certiied true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coeecients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.
Adaptive Affinity Fields for Semantic Segmentation

encode near / long range structural relations. • One size does not fit all classes; picking the one with minimal affinity loss results in trivial solutions. • Select the right size by pushing the affinity field matching to the hard negative cases. • Our adversarial learning for adaptive kernel sizes: Adaptive Affinity Fields for Semantic Segmentation Tsung-Wei Ke*, Jyh-Jing Hwang*, Ziwei Liu, Stella X. Yu
A face detection algorithm based on Adaboost and new Haar-Like feature

This paper uses a new face detection method based on Haar-Like feature. New Haar-Like feature is an extension of the Haar-Like feature basis. This article use four new Haar-Like feature, and these features with existing Haar-Like feature are input Adaboost classifier together to select feature, finally constructed classification performance and powerful cascade classifier for face detection. After detection experiments we can see, the algorithm can get better results compared with other traditional face detection classifiers like Haar-Like.
Progress and Tradeoffs in Neural Language Models

In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and energy consumption, which is particularly of concern in deployments on mobile devices. This paper, which examines the quality–performance tradeoff of various language modeling techniques, represents to our knowledge the first to make this observation. We compare state-of-the-art NLMs with “classic” Kneser-Ney (KN) LMs in terms of energy usage, latency, perplexity, and prediction accuracy using two standard benchmarks. On a Raspberry Pi, we find that orders of increase in latency and energy usage correspond to less change in perplexity, while the difference is much less pronounced on a desktop.
Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model

a Department of Communication, Management Science and Systems, 333 Lord Christopher Baldy Hall, State University of New York at Buffalo, Buffalo, NY 14260, United States b Department of Finance, Operations and Information Systems, Brock University, Canada c Department of Information Systems and Operations Management, Ball State University, United States d Department of Information Systems and Operations Management, University of Texas at Arlington, United States e Management Science and Systems, State University of New York at Buffalo, United States
Strategic niche management and sustainable innovation journeys: theory, findings, research agenda, and policy

This article discusses empirical findings and conceptual elaborations of the last 10 years in strategic niche management research (SNM). The SNM approach suggests that sustainable innovation journeys can be facilitated by creating technological niches, i.e. protected spaces that allow the experimentation with the co-evolution of technology, user practices, and regulatory structures. The assumption was that if such niches were constructed appropriately, they would act as building blocks for broader societal changes towards sustainable development. The article shows how concepts and ideas have evolved over time and new complexities were introduced. Research focused on the role of various niche-internal processes such as learning, networking, visioning and the relationship between local projects and global rule sets that guide actor behaviour. The empirical findings showed that the analysis of these niche-internal dimensions needed to be complemented with attention to niche external processes. In this respect, the multi-level perspective proved useful for contextualising SNM. This contextualisation led to modifications in claims about the dynamics of sustainable innovation journeys. Niches are to be perceived as crucial for bringing about regime shifts, but they cannot do this on their own. Linkages with ongoing external processes are also important. Although substantial insights have been gained, the SNM approach is still an unfinished research programme. We identify various promising research directions, as well as policy implications.
Carpooling: A Multi-Agent Simulation In Netlogo

The WiSafeCar (Wireless Traffic Safety Network between Cars) project aims at increasing the performance and reliability of the wireless transport and to provide traffic safety improvements. Within the context of this project, we have designed a Dynamic Carpooling System that will optimize the transport utilization by the ride sharing among people who usually cover the same route. An initial prototype of the system has been developed by using NetLogo. The information obtained from this simulator will be used to study the functioning of the clearing services, the current business models and to propose new ones. The first results seem encouraging, and the users have many economical advantages thanks to the sharing of costs which allows the individuals to retrench expenses and to contribute to the use of green technologies.
Automatic Colorization with Improved Spatial Coherence and Boundary Localization

Grayscale image colorization is an important computer graphics problem with a variety of applications. Recent fully automatic colorization methods have made impressive progress by formulating image colorization as a pixel-wise prediction task and utilizing deep convolutional neural networks. Though tremendous improvements have been made, the result of automatic colorization is still far from perfect. Specifically, there still exist common pitfalls in maintaining color consistency in homogeneous regions as well as precisely distinguishing colors near region boundaries. To tackle these problems, we propose a novel fully automatic colorization pipeline which involves a boundary-guided CRF (conditional random field) and a CNN-based color transform as post-processing steps. In addition, as there usually exist multiple plausible colorization proposals for a single image, automatic evaluation for different colorization methods remains a challenging task. We further introduce two novel automatic evaluation schemes to efficiently assess colorization quality in terms of spatial coherence and localization. Comprehensive experiments demonstrate great quality improvement in results of our proposed colorization method under multiple evaluation metrics.
Disaster risk reduction : a development concern

This document is an output from a project funded by the UK Department for International Development (DFID) for the benefit of developing countries. The views expressed are not necessarily those of DFID. Contents Contents i List of tables ii List of figures ii List of boxes ii Acronyms iii Acknowledgements iv Summary 1 1.  why worry about disasters? 7 Objectives of this Study 7 Global disaster trends 7 Why donors should be concerned 9 What donors can do 9 2. What makes a disaster? 11 Characteristics of a disaster 11 Disaster risk reduction 12 The diversity of hazards 12 Vulnerability and capacity, coping and adaptation 15 Resilience 16 Poverty and vulnerability: links and differences 16 'The disaster management cycle' 17 3. Why should disasters be a development concern? 19 3.1 Disasters hold back development 19 Disasters undermine efforts to achieve the Millennium Development Goals 19 Macroeconomic impacts of disasters 21 Reallocation of resources from development to emergency assistance 22 Disaster impact on communities and livelihoods 23 3.2 Disasters are rooted in development failures 25 Dominant development models and risk 25 Development can lead to disaster 26 Poorly planned attempts to reduce risk can make matters worse 29 Disaster responses can themselves exacerbate risk 30 3.3 'Disaster-proofing' development: what are the gains? 31 From 'vicious spirals' of failed development and disaster risk… 31 … to 'virtuous spirals' of risk reduction 32 Disaster risk reduction can help achieve the Millennium Development Goals 33 … and can be cost-effective 33 4. Why does development tend to overlook disaster risk? 36 4.1 Introduction 36 4.2 Incentive, institutional and funding structures 36 Political incentives and governance in disaster prone countries 36 Government-donor relations and moral hazard 37 Donors and multilateral agencies 38 NGOs 41 4.3 Lack of exposure to and information on disaster issues 41 4.4 Assumptions about the risk-reducing capacity of development 43 ii 5. Tools for better integrating disaster risk reduction into development 45 Introduction 45 Poverty Reduction Strategy Papers (PRSPs) 45 UN Development Assistance Frameworks (UNDAFs) 47 Country assistance plans 47 National Adaptation Programmes of Action (NAPAs) 48 Partnership agreements with implementing agencies and governments 49 Programme and project appraisal guidelines 49 Early warning and information systems 49 Risk transfer mechanisms 51 International initiatives and policy forums 51 Risk reduction performance targets and indicators for donors 52 6. Conclusions and recommendations 53 6.1 Main conclusions 53 6.2 Recommendations 54 Core recommendation …
Formal representations of uncertainty ∗

The recent development of uncertainty theories that account for the notion of belief is linked to the emergence, in the XXth century, of Decision Theory and Artificial Intelligence. Nevertheless, this topic was dealt with very differently by each area. Decision Theory insisted on the necessity to found representations on the empirical observation of individuals choosing between courses of action, regardless of any other type of information. Any axiom in the theory should be liable of empirical validation. Probabilistic representations of uncertainty can then be justified with a subjectivist point of view, without necessary reference to frequency. Degrees of probability then evaluate to what extent an agent believes in the occurrence of an event or in the truth of a proposition. In contrast, Artificial Intelligence adopted a more introspective approach aiming at formalizing intuitions, reasoning processes, through the statement of reasonable axioms, often without reference to probability. Actually, until the nineties Artificial Intelligence essentially focused on purely qualitative and ordinal (in fact, logical) representations.
Major flavonoids in grape seeds and skins: antioxidant capacity of catechin, epicatechin, and gallic acid.

Grape seeds and skins are good sources of phytochemicals such as gallic acid, catechin, and epicatechin and are suitable raw materials for the production of antioxidative dietary supplements. The differences in levels of the major monomeric flavanols and phenolic acids in seeds and skins from grapes of Vitis vinifera varieties Merlot and Chardonnay and in seeds from grapes of Vitis rotundifolia variety Muscadine were determined, and the antioxidant activities of these components were assessed. The contribution of the major monomeric flavonols and phenolic acid to the total antioxidant capacity of grape seeds and skins was also determined. Gallic acid, monomeric catechin, and epicatechin concentrations were 99, 12, and 96 mg/100 g of dry matter (dm) in Muscadine seeds, 15, 358, and 421 mg/100 g of dm in Chardonnay seeds, and 10, 127, and 115 mg/100 g of dm in Merlot seeds, respectively. Concentrations of these three compounds were lower in winery byproduct grape skins than in seeds. These three major phenolic constituents of grape seeds contributed <26% to the antioxidant capacity measured as ORAC on the basis of the corrected concentrations of gallic acid, catechin, and epicatechin in grape byproducts. Peroxyl radical scavenging activities of phenolics present in grape seeds or skins in decreasing order were resveratrol > catechin > epicatechin = gallocatechin > gallic acid = ellagic acid. The results indicated that dimeric, trimeric, oligomeric, or polymeric procyanidins account for most of the superior antioxidant capacity of grape seeds.