nils-herrmann/clean_scirepeval huggingface dataset:
Complexity leadership theory : An interactive perspective on leading in complex adaptive systems

leadership theory: An interactive perspective on leading in complex adaptive systems" (2006). Management Department Faculty Publications.  Leadership theory must transition to new perspectives that account for the complex adaptive needs of organizations. In this paper, we propose that leadership (as opposed to leaders) can be seen as a complex dynamic process that emerges in the interactive " spaces between " people and ideas. That is, leadership is a dynamic that transcends the capabilities of individuals alone; it is the product of interaction, tension, and exchange rules governing changes in perceptions and understanding. We label this a dynamic of adaptive leadership, and we show how this dynamic provides important insights about the nature of leadership and its outcomes in organizational fields. We define a leadership event as a perceived segment of action whose meaning is created by the interactions of actors involved in producing it, and we present a set of innovative methods for capturing and analyzing these contextually driven processes. We provide theoretical and practical implications of these ideas for organizational behavior and organization and management theory.
Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3D Object Recognition

This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.
Escaping Plato's Cave using Adversarial Training: 3D Shape From Unstructured 2D Image Collections

We develop PLATONICGAN to discover 3D structure of an object class from an unstructured collection of 2D images. The key idea is to learn a deep neural network that generates 3D shapes that are never objectionable to a discriminator looking only at its 2D projections, i. e., renderings of the generated volumes. Using such a 2D instead of a 3D discriminator allows tapping into massive 2D image collections instead of relying on much smaller 3D data sets. To establish constraints between 2D image observation and their 3D interpretation we suggest a family of rendering layers that are effectively back-propagatable. This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption (that can resolve occlusion if multiple 3D points project to the same 2D pixel). These layers are studied both on synthetic and real data in an application to reconstruct of 3D shape from 2D images.
A study of LoRa low power and wide area network technology

LoRa™ is low power wide area wireless network (LPWAN) protocol for Internet of Things (IoTs) applications. LPWAN has been enabling technology of large scale wireless sensor networks (WSNs). Effective cost, long range and energy efficiency of LPWANs make them most suitable candidates for smart city applications. These technologies offer novel communication paradigm to address discrete IoT's applications. LoRa is a recently proposed LPWAN technology based on spread spectrum technique with a wider band. LoRa uses the entire channel bandwidth to broadcast a signal which makes it resistant to channel noise, long term relative frequency, doppler effects and fading. This paper focuses on the emerging transmission technologies dedicated to IoT networks. Characteristics of LoRa are based on three basic parameters: Code Rate (CR), Spreading Factor (SF) and Bandwidth (BW). This paper provides in depth analysis of the impact of these three parameters on the data rate and time on air.
A Proposal for Statistical Outlier Detection in Relational Structures

This paper extends unsupervised statistical outlier detection to the case of relational data. For nonrelational data, where each individual is characterized by a feature vector, a common approach starts with learning a generative statistical model for the population. The model assigns a likelihood measure for the feature vector that characterizes the individual; the lower the feature vector likelihood, the more anomalous the individual. A difference between relational and nonrelational data is that an individual is characterized not only by a list of attributes, but also by its links and by attributes of the individuals linked to it. We refer to a relational structure that specifies this information for a specific individual as the individual’s database. Our proposal is to use the likelihood assigned by a generative model to the individual’s database as the anomaly score for the individual; the lower the model likelihood, the more anomalous the individual. As a novel validation method, we compare the model likelihood with metrics of individual success. An empirical evaluation reveals a surprising finding in soccer and movie data: We observe in the data a strong correlation between the likelihood and success metrics.
Stillbirths: economic and psychosocial consequences

Despite the frequency of stillbirths, the subsequent implications are overlooked and underappreciated. We present findings from comprehensive, systematic literature reviews, and new analyses of published and unpublished data, to establish the effect of stillbirth on parents, families, health-care providers, and societies worldwide. Data for direct costs of this event are sparse but suggest that a stillbirth needs more resources than a livebirth, both in the perinatal period and in additional surveillance during subsequent pregnancies. Indirect and intangible costs of stillbirth are extensive and are usually met by families alone. This issue is particularly onerous for those with few resources. Negative effects, particularly on parental mental health, might be moderated by empathic attitudes of care providers and tailored interventions. The value of the baby, as well as the associated costs for parents, families, care providers, communities, and society, should be considered to prevent stillbirths and reduce associated morbidity.
3D Printing for the Rapid Prototyping of Structural Electronics

In new product development, time to market (TTM) is critical for the success and profitability of next generation products. When these products include sophisticated electronics encased in 3D packaging with complex geometries and intricate detail, TTM can be compromised - resulting in lost opportunity. The use of advanced 3D printing technology enhanced with component placement and electrical interconnect deposition can provide electronic prototypes that now can be rapidly fabricated in comparable time frames as traditional 2D bread-boarded prototypes; however, these 3D prototypes include the advantage of being embedded within more appropriate shapes in order to authentically prototype products earlier in the development cycle. The fabrication freedom offered by 3D printing techniques, such as stereolithography and fused deposition modeling have recently been explored in the context of 3D electronics integration - referred to as 3D structural electronics or 3D printed electronics. Enhanced 3D printing may eventually be employed to manufacture end-use parts and thus offer unit-level customization with local manufacturing; however, until the materials and dimensional accuracies improve (an eventuality), 3D printing technologies can be employed to reduce development times by providing advanced geometrically appropriate electronic prototypes. This paper describes the development process used to design a novelty six-sided gaming die. The die includes a microprocessor and accelerometer, which together detect motion and upon halting, identify the top surface through gravity and illuminate light-emitting diodes for a striking effect. By applying 3D printing of structural electronics to expedite prototyping, the development cycle was reduced from weeks to hours.
Threat , Authoritarianism , and Selective Exposure to Information

We examined the hypothesis that threat alters the cognitive strategies used by high authoritarians in seeking out new political information from the environment. In a laboratory experiment, threat was manipulated through a “mortality salience” manipulation used in research on terror management theory (Pyszczynski, Solomon, & Greenberg, 2003). Subjects (N = 92) were then invited to read one of three editorial articles on the topic of capital punishment. We found that in the absence of threat, both low and high authoritarians were responsive to salient norms of evenhandedness in information selection, preferring exposure to a two-sided article that presents the merits of both sides of an issue to an article that selectively touts the benefits of the pro or con side of the issue. However, in the presence of threat, high but not low authoritarians became significantly more interested in exposure to an article containing uniformly pro-attitudinal arguments, and significantly less interested in a balanced, two-sided article. Finally, a path analysis indicated that selective exposure to attitude-congruent information led to more internally consistent policy attitudes and inhibited attitude change. Discussion focuses on the role of threat in conditioning the cognitive and attitudinal effects of authoritarianism.
Subjective Ratings of Robot Video Clips for Human Likeness , Familiarity , and Eeriness : An Exploration of the Uncanny Valley

Masahiro Mori observed that as robots come to look more humanlike, they seem more familiar, until a point is reached at which subtle deviations from human norms cause them to look creepy. He referred to this dip in familiarity and corresponding surge in strangeness as the uncanny valley. The eerie sensation associated with a mismatch between human expectations and a robot’s behavior provides a useful source of feedback to improve the cognitive models implemented in the robot. Is the uncanny valley a necessary property of near-humanlike forms? This paper contributes to ongoing work in understanding the nature and causes of the uncanny valley by means of an experiment: 56 participants were asked to rate 13 robots and 1 human, shown in video clips, on a very mechanical (1) to very humanlike (9) scale, a very strange (1) to very familiar (9) scale, and a not eerie (0) to extremely eerie (10) scale. Contrary to earlier studies with morphs [MacDorman and Ishiguro, 2006], plots of average and median values for ratings on these scales do not reveal a single U-shaped valley as predicted by Mori’s uncanny valley hypothesis [1970], although his hypothesis allows for some variation owing to movement. Robots rated similarly on the mechanical versus humanlike scale can be rated quite differently on the strange versus familiar or the eeriness scales. The results indicate that the perceived human likeness of a robot is not the only factor determining the perceived familiarity, strangeness, or eeriness of the robot. This suggests that other factors could be manipulated to vary the familiarity, strangeness, or eeriness of a robot independently of its human likeness.  To build robots that at least superficially approach human likeness is leading to insights in human perception and face-to-face interaction. These android robots possess the physical presence that simulated characters lack, yet can be more perfectly controlled than any human actor, to isolate the factor under study. Even in experiments in which the android’s responses are identical, we can observe how human responses vary according to their beliefs. For example, Japanese participants showed the same modesty with their eyes by averting gaze downward when interacting with an android as when interacting with a human interlocutor if they believed the android were under human control by telepresence [MacDorman et al., 2005]. In addition, androids provide an ideal testing ground for theories from the social and cognitive sciences because competing models can be implemented in an android and then tested by letting the android interact with industrial robot humanoid robot
Working memory as an emergent property of the mind and brain

Cognitive neuroscience research on working memory has been largely motivated by a standard model that arose from the melding of psychological theory with neuroscience data. Among the tenets of this standard model are that working memory functions arise from the operation of specialized systems that act as buffers for the storage and manipulation of information, and that frontal cortex (particularly prefrontal cortex) is a critical neural substrate for these specialized systems. However, the standard model has been a victim of its own success, and can no longer accommodate many of the empirical findings of studies that it has motivated. An alternative is proposed: Working memory functions arise through the coordinated recruitment, via attention, of brain systems that have evolved to accomplish sensory-, representation-, and action-related functions. Evidence from behavioral, neuropsychological, electrophysiological, and neuroimaging studies, from monkeys and humans, is considered, as is the question of how to interpret delay-period activity in the prefrontal cortex.
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
Efficient marginal likelihood optimization in blind deconvolution

In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k\y) and not only its mode. This leads to a distinction between MAPx, k strategies which estimate the mode pair x, k and often lead to undesired results, and MAPk strategies which select the best k while marginalizing over all possible x images. The MAPk principle is significantly more robust than the MAPx, k one, yet, it involves a challenging marginalization over latent images. As a result, MAPk techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAPk algorithm which involves only a modest modification of common MAPx, k algorithms. We show that MAPk can, in fact, be optimized easily, with no additional computational complexity.
Optimal Mass Transport for Registration and Warping

Image registration is the process of establishing a common geometric reference frame between two or more image data sets possibly taken at different times. In this paper we present a method for computing elastic registration and warping maps based on the Monge–Kantorovich theory of optimal mass transport. This mass transport method has a number of important characteristics. First, it is parameter free. Moreover, it utilizes all of the grayscale data in both images, places the two images on equal footing and is symmetrical: the optimal mapping from image A to image B being the inverse of the optimal mapping from B to A. The method does not require that landmarks be specified, and the minimizer of the distance functional involved is unique; there are no other local minimizers. Finally, optimal transport naturally takes into account changes in density that result from changes in area or volume. Although the optimal transport method is certainly not appropriate for all registration and warping problems, this mass preservation property makes the Monge–Kantorovich approach quite useful for an interesting class of warping problems, as we show in this paper. Our method for finding the registration mapping is based on a partial differential equation approach to the minimization of the L 2 Kantorovich–Wasserstein or “Earth Mover's Distance” under a mass preservation constraint. We show how this approach leads to practical algorithms, and demonstrate our method with a number of examples, including those from the medical field. We also extend this method to take into account changes in intensity, and show that it is well suited for applications such as image morphing.
Playability heuristics for mobile games

Expert evaluation is a widely used method for evaluating the usability of software products. When evaluating games, traditional usability heuristics lack comprehension and cannot be directly applied. In this paper, we introduce playability heuristics that are specifically designed for evaluating mobile games. Heuristics form a core model that can be used in any mobile game evaluation. The model consists of three modules: Game Usability, Mobility, and Gameplay. The mobile context has some unique characteristics, which require special attention during the evaluation. These characteristics are described in mobility heuristics. Mobile devices also set some of their own requirements for general usability and these issues are described along with game usability heuristics. These heuristics have been developed by using an iterative design process of a mobile game. In addition, we have validated the heuristics and evaluated five mobile games by using them with the expert evaluation method. The results indicate that playability problems, which violate game usability or mobility heuristics, are quite easy to identify. Gameplay problems are harder to find, but gameplay heuristics help in evaluation and focus on different aspects of the gameplay.
Shot boundary detection from videos using entropy and local descriptor

Video shot segmentation is an important step in key frame selection, video copy detection, video summarization, and video indexing for retrieval. Although some types of video data, e.g., live sports coverage, have abrupt shot boundaries that are easy to identify using simple heuristics, it is much more difficult to identify shot boundaries in other types such as cinematic movies. We propose an algorithm for shot boundary detection able to accurately identify not only abrupt shot boundaries, but also the fade-in and fade-out boundaries typical of cinematic movies. The algorithm is based on analysis of changes in the entropy of the gray scale intensity over consecutive frames and analysis of correspondences between SURF features over consecutive frames. In an experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm achieves substantial improvements over state of the art methods, with a precision of 97.8% and a recall of 99.3%.
Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study

Ability to model and predict the fuel consumption is vital in enhancing fuel economy of vehicles and preventing fraudulent activities in fleet management. Fuel consumption of a vehicle depends on several internal factors such as distance, load, vehicle characteristics, and driver behavior, as well as external factors such as road conditions, traffic, and weather. However, not all these factors may be measured or available for the fuel consumption analysis. We consider a case where only a subset of the aforementioned factors is available as a multi-variate time series from a long distance, public bus. Hence, the challenge is to model and/or predict the fuel consumption only with the available data, while still indirectly capturing as much as influences from other internal and external factors. Machine Learning (ML) is suitable in such analysis, as the model can be developed by learning the patterns in data. In this paper, we compare the predictive ability of three ML techniques in predicting the fuel consumption of the bus, given all available parameters as a time series. Based on the analysis, it can be concluded that the random forest technique produces a more accurate prediction compared to both the gradient boosting and neural networks.
Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data

Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a languageindependent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language’s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at http://trainomatic.org.
An expanding reference library for Peer-to-Peer content

Many Peer-to-Peer (P2P) networks have emerged as scalable platforms for enabling file-sharing and content-distribution applications. Due to their reliance on decentralized models encompassing millions of users, P2P networks are subjected to being infested with contraband material, copyright infringing content, as well as malicious viruses and programs. Moreover, the dynamics involved as users join and leave these networks make investigating them quite complex and time-dependent. To that end, this paper presents PxP, a tool that creates an expanding reference library for content that appears on P2P networks. PxP continuously searches the P2P networks for new files, downloads and fingerprints them, deletes them, and add their meta data to a public reference library. We envision that this library will aid investigators in criminal investigations that involve contraband material, civil investigations with copyright infringement content. In addition, this library can be used to speed up forensics examination of media through skipping over some of the files shared on P2P networks and in validating carving tools. We assess the performance of our framework through analysis, simulations and real experiments for content collected from the Gnutella P2P network.