nils-herrmann/clean_scirepeval huggingface dataset:
Human Resources Management in the Knowledge Management

Knowledge is increasingly claimed to be a key critical resource and source of competitive advantage in the modern global economy, especially with the rise of the service economy, the growth in the number of ‘knowledge workers’, the increasingly rapid flow of global information, and the growing recognition of the importance of intellectual capital and intellectual property rights. Knowledge, with its intangible aspects, is becoming a defining characteristic of economic activities, as opposed to tangibles such as goods, services or production processes. The rise of the knowledge economy has seen a proliferation of information and communication technologies, coupled with greater organizational complexity, the growth of virtual and global organizations and rapid change. This in turn requires drastic change within HRM to respond to changing demands of the knowledge economy.
Visions and Voices on Emerging Challenges in Digital Business Strategy

This section is a collection of shorter “Issue and Opinions” pieces that address some of the critical challenges around the evolution of digital business strategy. These voices and visions are from thought leaders who, in addition to their scholarship, have a keen sense of practice. They outline through their opinion pieces a series of issues that will need attention from both research and practice. These issues have been identified through their observation of practice with the eye of a scholar. They provide fertile opportunities for scholars in information systems, strategic management, and organizational theory.
CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures

Learning commonsense casual and temporal relation between events is one of the major steps towards deeper language understanding. This is crucial for understanding stories and narrative structure learning. In this paper we introduce a novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is a prerequisite for learning narrative structures of stories. This scheme is unique in capturing both temporal and causal aspects of inter-event relations. By annotating a total of 1,600 sentences in 320 five-sentence short stories sampled from ROCStories corpus, we demonstrate that these stories are indeed full of causal and temporal relations. Furthermore, we show that the CaTeRS annotation scheme enables high inter-annotator agreement for broad-coverage event entity annotation and moderate agreement on semantic link annotation.
Depressed dopamine activity in caudate and preliminary evidence of limbic involvement in adults with attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is the most prevalent psychiatric disorder of childhood. There is considerable evidence that brain dopamine is involved in ADHD, but it is unclear whether dopamine activity is enhanced or depressed.



To test the hypotheses that striatal dopamine activity is depressed in ADHD and that this contributes to symptoms of inattention.



Clinical (ADHD adult) and comparison (healthy control) subjects were scanned with positron emission tomography and raclopride labeled with carbon 11 (D2/D3 receptor radioligand sensitive to competition with endogenous dopamine) after placebo and after intravenous methylphenidate hydrochloride (stimulant that increases extracellular dopamine by blocking dopamine transporters). The difference in [11C]raclopride's specific binding between placebo and methylphenidate was used as marker of dopamine release. Symptoms were quantified using the Conners Adult ADHD Rating Scales.



Outpatient setting.



Nineteen adults with ADHD who had never received medication and 24 healthy controls.



With the placebo, D2/D3 receptor availability in left caudate was lower (P < .05) in subjects with ADHD than in controls. Methylphenidate induced smaller decrements in [11C]raclopride binding in left and right caudate (blunted DA increases) (P < .05) and higher scores on self-reports of "drug liking" in ADHD than in control subjects. The blunted response to methylphenidate in caudate was associated with symptoms of inattention (P < .05) and with higher self-reports of drug liking (P < .01). Exploratory analysis using statistical parametric mapping revealed that methylphenidate also decreased [11C]raclopride binding in hippocampus and amygdala and that these decrements were smaller in subjects with ADHD (P < .001).



This study reveals depressed dopamine activity in caudate and preliminary evidence in limbic regions in adults with ADHD that was associated with inattention and with enhanced reinforcing responses to intravenous methylphenidate. This suggests that dopamine dysfunction is involved with symptoms of inattention but may also contribute to substance abuse comorbidity in ADHD.
Image Processing with Complex Wavelets

We rst review how wavelets may be used for multi-resolution image processing, describing the lter-bank implementation of the discrete wavelet transform (dwt) and how it may be extended via separable ltering for processing images and other multi-dimensional signals. We then show that the condition for inversion of the dwt (perfect reconstruction) forces many commonly used wavelets to be similar in shape, and that this shape produces severe shift variance (variation of dwt coeecient energy at any given scale with shift of the input signal). It is also shown that separable ltering with the dwt prevents the transform from providing directionally selective lters for diagonal image features. Complex wavelets can provide both shift invariance and good directional se-lectivity, with only modest increases in signal redundancy and computation load. However development of a complex wavelet transform (cwt) with perfect reconstruction and good lter characteristics has proved diicult until recently. We now propose the dual-tree cwt as a solution to this problem, yielding a transform with attractive properties for a range of signal and image processing applications, including motion estimation, denoising, texture analysis and synthesis, and object segmentation.
Advances in natural language processing

Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today’s researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area.
Future Segmentation Using 3D Structure

Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making. Working towards this capability, we address the task of predicting future frame segmentation from a stream of monocular video by leveraging the 3D structure of the scene. Our framework is based on learnable sub-modules capable of predicting pixel-wise scene semantic labels, depth, and camera ego-motion of adjacent frames. We further propose a recurrent neural network based model capable of predicting future ego-motion trajectory as a function of a series of past ego-motion steps. Ultimately, we observe that leveraging 3D structure in the model facilitates successful prediction, achieving state of the art accuracy in future semantic segmentation.
Context-based detection of pedestrian crossing intention for autonomous driving in urban environments

This paper focuses on the detection of pedestrian crossing intention to improve the situation awareness for autonomous driving in urban environments. A new definition of pedestrian crossing intention is discussed, which allows self-driving vehicles to identify pedestrians, whose intended actions are relevant for the own behavior planning, at an early stage. We propose a context-based feature descriptor in combination with a SVM classifier for detecting this. The descriptor captures the movement of a pedestrian relative to the road and the spatial layout of other scene elements in a generic manner. The performance of the feature descriptor is evaluated in relation to various SVM setups. Feasibility of the approach is demonstrated with data captured on-board of a vehicle in real inner-city traffic. The evaluation of the classification results confirms, that context-based data is a promising indicator for pedestrian crossing intention and that the proposed feature descriptor is capable of representing this. It is further shown that a lack of information about the pedestrian's posture and body movement results in a delayed detection of the pedestrians changing their crossing intention when compared to a human observer.
Tackling the digitalization challenge : how to benefit from digitalization in practice

Digitalization has been identified as one of the major trends changing society and business. Digitalization causes changes for companies due to the adoption of digital technologies in the organization or in the operation environment. This paper discusses digitalization from the viewpoint of diverse case studies carried out to collect data from several companies, and a literature study to complement the data. This paper describes the first version of the digital transformation model, derived from synthesis of these industrial cases, explaining a starting point for a systematic approach to tackle digital transformation. The model is aimed to help companies systematically handle the changes associated with digitalization. The model consists of four main steps, starting with positioning the company in digitalization and defining goals for the company, and then analyzing the company’s current state with respect to digitalization goals. Next, a roadmap for reaching the goals is defined and implemented in the company. These steps are iterative and can be repeated several times. Although company situations vary, these steps will help to systematically approach digitalization and to take the steps necessary to benefit from it.
Family routines and rituals: a context for occupational therapy interventions.

This paper explores the importance of family daily routines and rituals for the family's functioning and sense of identity.



The findings of this paper are derived from an analysis of the morning routines of 40 families with children with disabilities in the United States and Canada. The participants lived in urban and rural areas. Forty of the 49 participants were mothers and the majority of the families were of European descent. Between one and four interviews were conducted with each participant. Topics included the family's story, daily routines, and particular occupations. Data on the morning routines of the families were analyzed for order and affective and symbolic meaning using a narrative approach.



The findings are presented as narratives of morning activities in five families. These narratives are examples for rituals, routines, and the absence of a routine. Rituals are discussed in terms of their affective and symbolic qualities, routines are discussed in terms of the order they give to family life, whereas the lack of family routine is discussed in terms of lack of order in the family.



Family routines and rituals are organizational and meaning systems that may affect family's ability to adapt them.
Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection

Computers still struggle to understand the interdependency of objects in the scene as a whole, e.g., relations between objects or their attributes. Existing methods often ignore global context cues capturing the interactions among different object instances, and can only recognize a handful of types by exhaustively training individual detectors for all possible relationships. To capture such global interdependency, we propose a deep Variation-structured Re-inforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image. First, a directed semantic action graph is built using language priors to provide a rich and compact representation of semantic correlations between object categories, predicates, and attributes. Next, we use a variation-structured traversal over the action graph to construct a small, adaptive action set for each step based on the current state and historical actions. In particular, an ambiguity-aware object mining scheme is used to resolve semantic ambiguity among object categories that the object detector fails to distinguish. We then make sequential predictions using a deep RL framework, incorporating global context cues and semantic embeddings of previously extracted phrases in the state vector. Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome dataset validate the superiority of VRL, which can achieve significantly better detection results on datasets involving thousands of relationship and attribute types. We also demonstrate that VRL is able to predict unseen types embedded in our action graph by learning correlations on shared graph nodes.
D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with LinkNet architecture and has dilated convolution layers in its center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. In the CVPR DeepGlobe 2018 Road Extraction Challenge, our best IoU scores on the validation set and the test set are 0.6466 and 0.6342 respectively.
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition

This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. To construct training data we develop a method for fitting 3D morphable model (3DMM) to multiple 2D images of a subject. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.
Are Molecular Vibration Patterns of Cell Structural Elements Used for Intracellular Signalling?

To date the manner in which information reaches the nucleus on that part within the three-dimensional structure where specific restorative processes of structural components of the cell are required is unknown. The soluble signalling molecules generated in the course of destructive and restorative processes communicate only as needed.



All molecules show temperature-dependent molecular vibration creating a radiation in the infrared region. Each molecule species has in its turn a specific frequency pattern under given specific conditions. Changes in their structural composition result in modified frequency patterns of the molecules in question. The main structural elements of the cell membrane, of the endoplasmic reticulum, of the Golgi apparatus, and of the different microsomes representing the great variety of polar lipids show characteristic frequency patterns with peaks in the region characterised by low water absorption. These structural elements are very dynamic, mainly caused by the creation of signal molecules and transport containers. By means of the characteristic radiation, the area where repair or substitution services are needed could be identified; this spatial information complements the signalling of the soluble signal molecules. Based on their resonance properties receptors located on the outer leaflet of the nuclear envelope should be able to read typical frequencies and pass them into the nucleus. Clearly this physical signalling must be blocked by the cell membrane to obviate the flow of information into adjacent cells.



If the hypothesis can be proved experimentally, it should be possible to identify and verify characteristic infrared frequency patterns. The application of these signal frequencies onto cells would open entirely new possibilities in medicine and all biological disciplines specifically to influence cell growth and metabolism. Similar to this intracellular system, an extracellular signalling system with many new therapeutic options has to be discussed.
Cascade context encoder for improved inpainting

In this paper, we analyze if cascade usage of the context encoder with increasing input can improve the results of the inpainting. For this purpose, we train context encoder for 64x64 pixels images in a standard way and use its resized output to fill in the missing input region of the 128x128 context encoder, both in training and evaluation phase. As the result, the inpainting is visibly more plausible. In order to thoroughly verify the results, we introduce normalized squared-distortion, a measure for quantitative inpainting evaluation, and we provide its mathematical explanation. This is the first attempt to formalize the inpainting measure, which is based on the properties of latent feature representation, instead of L2 reconstruction loss.
An Analysis of Annotated Corpora for Emotion Classification in Text

Several datasets have been annotated and published for classification of emotions. They differ in several ways: (1) the use of different annotation schemata (e. g., discrete label sets, including joy, anger, fear, or sadness or continuous values including valence, or arousal), (2) the domain, and, (3) the file formats. This leads to several research gaps: supervised models often only use a limited set of available resources. Additionally, no previous work has compared emotion corpora in a systematic manner. We aim at contributing to this situation with a survey of the datasets, and aggregate them in a common file format with a common annotation schema. Based on this aggregation, we perform the first cross-corpus classification experiments in the spirit of future research enabled by this paper, in order to gain insight and a better understanding of differences of models inferred from the data. This work also simplifies the choice of the most appropriate resources for developing a model for a novel domain. One result from our analysis is that a subset of corpora is better classified with models trained on a different corpus. For none of the corpora, training on all data altogether is better than using a subselection of the resources. Our unified corpus is available at http://www.ims.uni-stuttgart.de/data/unifyemotion. Title and Abstract in German Eine Analyse von annotierten Korpora zur Emotionsklassifizierung in Text Es existieren bereits verschiedene Textkorpora, welche zur Erstellung von Modellen für die automatische Emotionsklassifikation erstellt wurden. Sie unterscheiden sich (1) in den unterschiedlichen Annotationsschemata (z.B. diskrete Klassen wie Freude, Wut, Angst, Trauer oder kontinuierliche Werte wie Valenz und Aktivierung), (2) in der Domäne, und, auf einer technischen Ebene, (3) in den Dateiformaten. Dies führt dazu, dass überwacht erstellte Modelle typischerweise nur einen Teil der verfügbaren Ressourcen nutzen sowie kein systematischer Vergleich der Korpora existiert. Hier setzt unsere Arbeit mit einem Überblick der verfügbaren Datensätze an, welche wir in ein gemeinsames Format mit einem einheitlichen Annotationsschema konvertieren. Darauf aufbauend führen wir erste Experimente durch, in dem wir auf Teilmengen der Korpora trainieren und auf anderen testen. Dies steht im Sinne zukünftiger, durch unsere Arbeit ermöglichten Analysen, die Unterschiede zwischen den Annotationen besser zu verstehen.  Weiteren vereinfacht dies die Wahl einer angemessenen Ressource für die Erstellung von Modellen für eine neue Domäne. Wir zeigen unter anderem, dass die Vorhersagen für einige Korpora besser funktioniert, wenn ein Modell auf einer anderen Ressource trainiert wird. Weiterhin ist für kein Korpus die Vorhersage am besten, wenn alle Daten vereint werden. Unser aggregiertes Korpus ist verfügbar unter http://www.ims.uni-stuttgart.de/data/unifyemotion. This work is licenced under a Creative Commons Attribution 4.0 International Licence. Licence details: http:// creativecommons.org/licenses/by/4.0/
Early Prediction of Movie Success - What, Who, and When

Leveraging historical data from the movie industry, this study built a predictive model for movie success, deviating from past studies by predicting profit (as opposed to revenue) at early stages of production (as opposed to just prior to release) to increase investor certainty. Our work derived several groups of novel features for each movie, based on the cast and collaboration network (who’), content (‘what’), and time of release (‘when’).
Bayesian Gaussian Process Latent Variable Model

We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method allows us to variationally integrate out the input variables of the Gaussian process and compute a lower bound on the exact marginal likelihood of the nonlinear latent variable model. The maximization of the variational lower bound provides a Bayesian training procedure that is robust to overfitting and can automatically select the dimensionality of the nonlinear latent space. We demonstrate our method on real world datasets. The focus in this paper is on dimensionality reduction problems, but the methodology is more general. For example, our algorithm is immediately applicable for training Gaussian process models in the presence of missing or uncertain inputs.