nils-herrmann/clean_scirepeval huggingface dataset:
Running Head : Experiential learning critics Experiential Learning and Its Critics : Preserving the Role of Experience in Management Learning and Education

This paper considers John Dewey’s dual reformist-preservationist agenda for education in the context of current debates about the role of experience in management learning. The paper argues for preserving experience-based approaches to management learning by revising the concept of experience to more clearly account for the relationship between personal and social (i.e. , tacit/explicit) knowledge. By reviewing, comparing and extending critiques of Kolb’s experiential learning theory and reconceptualizing the learning process based on post-structural analysis of psychoanalyst Jacque Lacan, the paper defines experience within the context of language and social action. This perspective is contrasted to action, cognition, critical reflection and other experience-based approaches to management learning. Implications for management theory, pedagogy and practice suggest greater emphasis on language and conversation in the learning process. Future directions for research are explored.
Spread Spectrum − Secret Military Technology to 3

Guglielmo Marconi probably made the first spread spectrum transmission without even realizing it. His primitive spark-gap transmitter used an RF bandwidth that was much wider than the information bandwidth. Little did he know that his experiment would spark a revolution in communications technology. Today, wireless communications plays an integral part in our daily lives. Cellular phones are quite commonplace and we can hardly imagine life without them. In the future, we would not only use them to have conversations, but to access the internet, watch the latest news updates or even manage our daily schedules  the possibilities are endless. Yet, at the heart of this technological marvel lies an idea that was born 60 years ago, and at that time, was scoffed at as being unrealistic and impossible. This article traces the evolution of spread spectrum technology from the pre-war days till today.
Semantic Mapping with Simultaneous Object Detection and Localization

We present a filtering-based method for semantic mapping to simultaneously detect objects and localize their 6 degree-of-freedom pose. For our method, called Contextual Temporal Mapping (or CT-Map), we represent the semantic map as a belief over object classes and poses across an observed scene. Inference for the semantic mapping problem is then modeled in the form of a Conditional Random Field (CRF). CT-Map is a CRF that considers two forms of relationship potentials to account for contextual relations between objects and temporal consistency of object poses, as well as a measurement potential on observations. A particle filtering algorithm is then proposed to perform inference in the CT-Map model. We demonstrate the efficacy of the CT-Map method with a Michigan Progress Fetch robot equipped with a RGB-D sensor. Our results demonstrate that the particle filtering based inference of CT-Map provides improved object detection and pose estimation with respect to baseline methods that treat observations as independent samples of a scene.
Personal Cognitive Assistants for Military Intelligence Analysis : Mixed-Initiative Learning , Tutoring , and Problem Solving

This paper presents research on developing a new type of tool that can alleviate several sys-temic problems faced by the traditional intelligence analysis process. The tool is a personal cognitive assistant that can rapidly acquire expertise in intelligence analysis directly from intelligence analysts, can train new analysts, and can help analysts find solutions to complex problems through mixed-initiative reasoning, making possible the synergistic integration of a human's experience and creativity with an automated agent's knowledge and speed, and facilitating the collaboration with complementary experts and their agents.
THE PSYCHOLOGY OF SELF ‐ DEFENSE : SELF ‐ AFFIRMATION THEORY

In major league baseball, a hitter could have a long and productive career by maintaining a .300 average, that is, by getting a base hit 30% of the time. A great deal of money could be earned and fame accrued. Yet the other 70% of the time, this player would have failed. The vast majority of attempts to hit the ball would result in ‘‘making an out’’ and thus pose a potential threat to the player’s sense of personal worth and social regard. Like major league baseball players, people in contemporary society face innumerable failures and self‐threats. These include substandard performance on the job or in class, frustrated goals or aspirations, information challenging the validity of long‐held beliefs, illness, the defeat of one’s political party in an election or of one’s favorite sports team in a playoV, scientific evidence suggesting that one is engaging in risky health behavior, negative feedback at work or in school, rejection in a romantic relationship, real and perceived social slights, interpersonal and intergroup conflict, the misbehavior of one’s child, the loss of a loved one, and so on. In the course of a given day, the potential number of events that could threaten people’s ‘‘moral and adaptive adequacy’’—their sense of themselves as good, virtuous, successful, and able to control important life outcomes (Steele, 1988)—seems limitless and likely to exceed the small number of events that aYrm it. A major undertaking for most people is to sustain self‐integrity when faced with the inevitable setbacks and disappointments of daily life—the 70% of the time
Digital Transformation in Airport Ground Operations

Correspondence: ralf.mikut@kit.edu Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany Full list of author information is available at the end of the article Abstract How has digital transformation changed airport ground operations? Although the relevant peer-reviewed literature emphasizes the role of cost savings as a key driver behind digitalization of airport ground operations, the focus is on data-driven, customer-centric innovations. This paper argues that ground handling agents are deploying new technologies mainly to boost process efficiency and to cut costs. Our research shows that ground handling agents are embracing current trends to craft new business models and develop new revenue streams. In this paper, we examine the ground handling agent’s value chain and identify areas that are strongly affected by digital transformation and those that are not. We discuss different business scenarios for digital technology and link them with relevant research, such as automated service data capturing, new digital services for passengers, big data, indoor navigation, and wearables in airport ground operations. We assess the maturity level of discussed technologies using NASA technology readiness levels.
Survey of Deep Learning Applications to Medical Image Analysis

Recently, a machine learning (ML) area called deep learning emerged in the computer-vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide com putervision competition, ImageNet Classification. Since then, researchers in many fields, including medical image analysis, have started actively participating in the explosively growing field of deep learning. In this paper, deep learning techniques and their applications to medical image analysis are surveyed. This survey overviewed 1) standard ML techniques in the computer-vision field, 2) what has changed in ML before and after the introduction of deep learning, 3) ML models in deep learning, and 4) applications of deep learning to medical image analysis. The comparisons between MLs before and after deep learning revealed that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is learning image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The survey of deep learning also revealed that there is a long history of deep-learning techniques in the class of ML with image input, except a new term, “deep learning”. “Deep learning” even before the term existed, namely, the class of ML with image input was applied to various problems in medical image analysis including classification between lesions and non-lesions, classification between lesion types, segmentation of lesions or organs, and detection of lesions. ML with image input including deep learning is a very powerful, versatile technology with higher performance, which can bring the current state-of-the-art performance level of medical image analysis to the next level, and it is expected that deep learning will be the mainstream technology in medical image analysis in the next few decades. “Deep learning”, or ML with image input, in medical image analysis is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical image analysis in the next few decades.
Residual Networks of Residual Networks: Multilevel Residual Networks

A residual networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual network architecture, residual networks of residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds levelwise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets, and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-network-like structures. Our RoR-3-WRN58-4 + SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100, and SVHN, with the test errors of 3.77%, 19.73%, and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared with ResNets on the ImageNet data set.
COGNITIVE NEUROSCIENCE OF HUMAN SOCIAL BEHAVIOUR

A new field has emerged to investigate the cognitive neuroscience of social behaviour, the popularity of which is attested by recent conferences, special issues of journals and by books. But the theoretical underpinnings of this new field derive from an uneasy marriage of two different approaches to social behaviour: sociobiology and evolutionary psychology on the one hand, and social psychology on the other. The first approach treats the study of social behaviour as a topic in ethology, continuous with studies of motivated behaviour in other animals. The second approach has often emphasized the uniqueness of human behaviour, and the uniqueness of the individual person, their environment and their social surroundings. These two different emphases do not need to conflict with one another. In fact, neuroscience might offer a reconciliation between biological and psychological approaches to social behaviour in the realization that its neural regulation reflects both innate, automatic and COGNITIVELY IMPENETRABLE mechanisms, as well as acquired, contextual and volitional aspects that include SELF-REGULATION. We share the first category of features with other species, and we might be distinguished from them partly by elaborations on the second category of features. In a way, an acknowledgement of such an architecture simply provides detail to the way in which social cognition is complex — it is complex because it is not monolithic, but rather it consists of several tracks of information processing that can be variously recruited depending on the circumstances. Specifying those tracks, the conditions under which they are engaged, how they interact, and how they must ultimately be coordinated to regulate social behaviour in an adaptive fashion, is the task faced by a neuroscientific approach to social cognition.
A mechanics-based model for simulation and control of flexible needle insertion in soft tissue

In needle-based medical procedures, beveled-tip flexible needles are steered inside soft tissue with the aim of reaching pre-defined target locations. The efficiency of needle-based interventions depends on accurate control of the needle tip. This paper presents a comprehensive mechanics-based model for simulation of planar needle insertion in soft tissue. The proposed model for needle deflection is based on beam theory, works in real-time, and accepts the insertion velocity as an input that can later be used as a control command for needle steering. The model takes into account the effects of tissue deformation, needle-tissue friction, tissue cutting force, and needle bevel angle on needle deflection. Using a robot that inserts a flexible needle into a phantom tissue, various experiments are conducted to separately identify different subsets of the model parameters. The validity of the proposed model is verified by comparing the simulation results to the empirical data. The results demonstrate the accuracy of the proposed model in predicting the needle tip deflection for different insertion velocities.
An effective Model Predictive Control for grid connected Packed U Cells multilevel inverter

This paper presents a Model Predictive Control (MPC) for grid-tied Packed U Cells (PUC) multilevel inverter. The system under study consists of a single-phase 3-cell PUC inverter connected to the grid through filtering inductor. The proposed topology allows the generation of 7-level output voltage with reduction of passive and active components compared to the conventional multilevel inverters. The aim of the proposed MPC technique is to achieve grid-tied current injection, low Total Harmonic Distortion (THD) of the current, unity power factor, while balancing the capacitor voltages at maximum power point (MPP). The feasibility of this strategy is validated by simulation using Matlab/Simulink environment.
Social Network Extraction of Academic Researchers

This paper addresses the issue of extraction of an academic researcher social network. By researcher social network extraction, we are aimed at finding, extracting, and fusing the 'semantic '-based profiling information of a researcher from the Web. Previously, social network extraction was often undertaken separately in an ad-hoc fashion. This paper first gives a formalization of the entire problem. Specifically, it identifies the 'relevant documents' from the Web by a classifier. It then proposes a unified approach to perform the researcher profiling using conditional random fields (CRF). It integrates publications from the existing bibliography datasets. In the integration, it proposes a constraints-based probabilistic model to name disambiguation. Experimental results on an online system show that the unified approach to researcher profiling significantly outperforms the baseline methods of using rule learning or classification. Experimental results also indicate that our method to name disambiguation performs better than the baseline method using unsupervised learning. The methods have been applied to expert finding. Experiments show that the accuracy of expert finding can be significantly improved by using the proposed methods.
Siri On-Device Deep Learning-Guided Unit Selection Text-to-Speech System

This paper describes Apple’s hybrid unit selection speech synthesis system, which provides the voices for Siri with the requirement of naturalness, personality and expressivity. It has been deployed into hundreds of millions of desktop and mobile devices (e.g. iPhone, iPad, Mac, etc.) via iOS and macOS in multiple languages. The system is following the classical unit selection framework with the advantage of using deep learning techniques to boost the performance. In particular, deep and recurrent mixture density networks are used to predict the target and concatenation reference distributions for respective costs during unit selection. In this paper, we present an overview of the run-time TTS engine and the voice building process. We also describe various techniques that enable on-device capability such as preselection optimization, caching for low latency, and unit pruning for low footprint, as well as techniques that improve the naturalness and expressivity of the voice such as the use of long units.
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics

Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements. We present KeystoneML, a system that captures and optimizes the end-to-end large-scale machine learning applications for high-throughput training in a distributed environment with a high-level API. This approach offers increased ease of use and higher performance over existing systems for large scale learning. We demonstrate the effectiveness of KeystoneML in achieving high quality statistical accuracy and scalable training using real world datasets in several domains.
Text Classification Using WordNet Hypernyms

This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypernyms. Six binary classification tasks of varying diff iculty are defined, and the Ripper system is used to produce discrimination rules for each task using the new hypernym density representation. Rules are also produced with the commonly used bag-of-words representation, incorporating no knowledge from WordNet. Experiments show that for some of the more diff icult tasks the hypernym density representation leads to significantly more accurate and more comprehensible rules.
WHYPER: Towards Automating Risk Assessment of Mobile Applications

Application markets such as Apple’s App Store and Google’s Play Store have played an important role in the popularity of smartphones and mobile devices. However, keeping malware out of application markets is an ongoing challenge. While recent work has developed various techniques to determine what applications do, no work has provided a technical approach to answer, what do users expect? In this paper, we present the first step in addressing this challenge. Specifically, we focus on permissions for a given application and examine whether the application description provides any indication for why the application needs a permission. We present WHYPER, a framework using Natural Language Processing (NLP) techniques to identify sentences that describe the need for a given permission in an application description. WHYPER achieves an average precision of 82.8%, and an average recall of 81.5% for three permissions (address book, calendar, and record audio) that protect frequentlyused security and privacy sensitive resources. These results demonstrate great promise in using NLP techniques to bridge the semantic gap between user expectations and application functionality, further aiding the risk assessment of mobile applications.
Occupational therapy interventions for ADHD : a systematic review

ADHD can have a negative impact on occupational performance of a child, interfering with ADLs, IADLs, education, leisure, and play. However, at this time, a cumulative review of evidence based occupational therapy interventions for children with ADHD do not exist.  The purpose of this scholarly project was to complete a systematic review of what occupational therapy interventions are effective for school-aged children with ADHD.  An extensive systematic review for level T, II, or II research articles was completed using CINAHL and OT Search. Inclusion, exclusion, subject terms, and words or phrases were determined with assistance from the librarian at the Harley French Library at the University of North Dakota.  The systematic review yielded !3 evidence-based articles with interventions related to cognition, motor, sensory, and play. Upon completion of the systematic review, articles were categorized based upon an initial literature search understanding common occupational therapy interventions for children with ADHD. Specifically, level I, II, and III occupational therapy research is available for interventions addressing cognition, motor, sensory, and play.  Implications for practice and education include the need for foundational and continuing education opportunities reflecting evidenced-based interventions for ADHD. Further research is needed to solidify best practices for children with ADHD including more rigorous studies across interventions.
Barbed sutures "lunch time" lifting: evidence-based efficacy.

There is a growing trend nowadays for patients to seek the least invasive treatments possible with less risk of complications and downtime to correct rhytides and ptosis characteristic of aging. Nonsurgical face and neck rejuvenation has been attempted with various types of interventions. Suture suspension of the face, although not a new idea, has gained prominence with the advent of the so called "lunch-time" face-lift. Although some have embraced this technique, many more express doubts about its safety and efficacy limiting its widespread adoption. The present review aims to evaluate several clinical parameters pertaining to thread suspensions such as longevity of results of various types of polypropylene barbed sutures, their clinical efficacy and safety, and the risk of serious adverse events associated with such sutures. Early results of barbed suture suspension remain inconclusive. Adverse events do occur though mostly minor, self-limited, and of short duration. Less clear are the data on the extent of the peak correction and the longevity of effect, and the long-term effects of the sutures themselves. The popularity of barbed suture lifting has waned for the time being. Certainly, it should not be presented as an alternative to a face-lift.
Social neuroscience and health: neurophysiological mechanisms linking social ties with physical health

Although considerable research has shown the importance of social connection for physical health, little is known about the higher-level neurocognitive processes that link experiences of social connection or disconnection with health-relevant physiological responses. Here we review the key physiological systems implicated in the link between social ties and health and the neural mechanisms that may translate social experiences into downstream health-relevant physiological responses. Specifically, we suggest that threats to social connection may tap into the same neural and physiological 'alarm system' that responds to other critical survival threats, such as the threat or experience of physical harm. Similarly, experiences of social connection may tap into basic reward-related mechanisms that have inhibitory relationships with threat-related responding. Indeed, the neurocognitive correlates of social disconnection and connection may be important mediators for understanding the relationships between social ties and health.