nils-herrmann/clean_scirepeval huggingface dataset:
Negative Association Rules

Mining association rules associates events that took place together. In market basket analysis, these discovered rules associate items purchased together. Items that are not part of a transaction are not considered. In other words, typical association rules do not take into account items that are part of the domain but that are not together part of a transaction. Association rules are based on frequencies and count the transactions where items occur together. However, counting absences of items is prohibitive if the number of possible items is very large, which is typically the case. Nonetheless, knowing the relationship between the absence of an item and the presence of another can be very important in some applications. These rules are called negative association rules. We review current approaches for mining negative association rules and we discuss limitations and future research directions.
An Abductive Counterfactual Reasoning Approach in Logic Programming

We construct a counterfactual statement when we reason conjecturally about an event which did or did not occur in the past: If an event had occurred, what would have happened? Would it be relevant? Real world examples, as studied by Byrne, Rescher and many others, show that these conditionals involve a complex reasoning process. An intuitive and elegant approach to evaluate counterfactuals, without deep revision mechanisms, is proposed by Pearl. His Do-Calculus identifies causal relations in a Bayesian network resorting to counterfactuals. Though leaving out probabilities, we adopt Pearl’s stance, and its prior epistemological justification to counterfactuals in causal Bayesian networks, but for programs. Logic programming seems a suitable environment for several reasons. First, its inferential arrow is adept at expressing causal direction and conditional reasoning. Secondly, together with its other functionalities such as abduction, integrity constraints, revision, updating and debugging (a form of counterfactual reasoning), it proffers a wide range of expressibility itself. We show here how programs under the weak completion semantics in an abductive framework, comprising the integrity constraints, can smoothly and uniformly capture well-known and off-the-shelf counterfactual problems and conundrums, taken from the psychological and philosophical literature. Our approach is adroitly reconstructable in other three-valued LP semantics, or restricted to two-valued ones.
Seismic Attributes – a promising aid for geologic prediction

110 CSEG RECORDER 2006 Special Edition Continued on Page 111 Seismic attributes have come a long way since their intro d u ction in the early 1970s and have become an integral part of seismic interpretation projects. To d a y, they are being used widely for lithological and petrophysical prediction of re s e rvoirs and various methodologies have been developed for their application to broader hydrocarbon exploration and development decision making. Beginning with the digital re c o rding of seismic data in the early 1960s and the ensuing bright spot analysis, the 1970s saw the introduction of complex trace attributes and seismic inversion along with their color displays. This was followed by the development of response attributes, introduction of texture analysis, 2D attributes, horizon and interval attributes and the pervasive use of c o l o r. 3D seismic acquisition dominated the 1990s as the most successful exploration technology of several decades and along with that came the seismic sequence attributes. The c o h e rence technology introduced in the mid 1990s significantly changed the way geophysicists interpreted seismic data. This was followed by the introduction of spectral decomposition in the late 1990s and a host of methods for evaluation of a combination of attributes. These included pattern recognition techniques as well as neural network applications. These developments continued into the new millennium, with enhanced visualization and 3D computation and interpretation of texture and curvature attributes coming to the fore f ront. Of course all this was possible with the power of scientific computing making significant advances during the same period of time. A detailed re c o ns t ruction of these key historical events that lead to the modern seismic attribute analysis may be found in Chopra and Marfurt (2005). The proliferation of seismic attributes in the last two decades has led to attempts to their classification and to bring some order to their chaotic development.
Interactive Architecture: Exploring and Unwrapping the Potentials of Organic User Interfaces

Organic User Interfaces (OUIs) are flexible, actuated interfaces characterized by being aesthetically pleasing, intuitively manipulated and ubiquitously embedded in our daily life. In this paper, we critically survey the state-of-the-art for OUIs in interactive architecture research at two levels: 1) Architecture and Landscape; and 2) Interior Design. We postulate that OUIs have specific qualities that offer great potential for building interactive interiors and entire architectures that have the potential to -finally- transform the vision of smart homes and ubiquitous computing environments (calm computing) into reality. We formulate a manifesto for OUI Architecture in both exterior and interior design, arguing that OUIs should be at the core of a new interdisciplinary field driving research and practice in architecture. Based on this research agenda we propose concerted efforts to be made to begin addressing the challenges and opportunities of OUIs. This agenda offers us the strongest means through which to deliver a future of interactive architecture.
The costs and benefits of the Dark Triad: Implications for mate poaching and mate retention tactics

The Dark Triad – narcissism, Machiavellianism, and psychopathy – have traditionally been considered to be undesirable traits. However, emerging work suggest that not only may there be a positive side to possessing these traits but they may also serve important adaptive functions, even if the strategies associated with them are viewed as socially undesirable. In an online survey (N = 336), we investigated the costs and benefits of the Dark Triad within the domain of mating psychology. The social style and lower order personality traits of the Dark Triad traits facilitated increased mateships in the form of poaching mates from others and being poached oneself to form mateships, pointing to possible benefits of possessing the Dark Triad traits. However, the costside was evidenced with rates of mates abandoning their current relationship for a new one. Mate retention is a problem faced by those with these traits and the tactics used to retain mates were characteristic of the Dark Triad: aggressive and narcisstic. Results are discussed using an adaptionist paradigm.
A Heterogeneous Service-Oriented Deep Packet Inspection and Analysis Framework for Traffic-Aware Network Management and Security Systems

A variety of Web-based applications, mobile apps, and other over the top data services with affordable 3G/4G enabled smart devices are major factors for enormous increase in heterogeneous data traffic at enterprise and mobile networks. This creates challenges regarding traffic management and requires traffic-aware intelligent network management to deliver sustained quality of experience for subscribers. Deep packet inspection and analysis (DPIA) provides base platform for development of traffic-aware intelligent network management and security systems. However, computationally complex DPIA-related packet processing for high speed data traffic makes these systems expensive. Furthermore, conventionally these traffic-aware network management and security systems are deployed in enterprise networks with independent and dedicated DPIA-related processing resources and require multiple copies of passively provisioned high speed data from network, while performing similar DPIA operations over the same data again and again. This duplicate deployment of expensive software and hardware resources for DPIA processing eventually results in higher capital expenditures as well as operational expenditures for network operators. We have proposed a novel service-oriented framework for heterogeneous deep packet inspection and analysis (SoDPI) that simultaneously provides diversified DPIA services to multiple client applications for network management and security operations in high-speed networks. Proposed framework provides flexible and comprehensive API-based service interface for client applications to register required DPIA services. SoDPI framework implementation is based on commodity hardware and deploys shared set of DPIA-related packet processing components, requiring only single copy of passive data provisioned from network. Experimental evaluations show that novel SoDPI framework requires considerably reduced amount of software and hardware resources to fulfill heterogeneous DPIA packet processing requirements for multiple client applications in comparison with conventional network management and security applications with dedicated DPIA components. This results in lower cost impacts for network operators with more network manageability.
Graph Theoretical Similarity Approach To Compare Molecular Electrostatic Potentials

In this work we introduce a graph theoretical method to compare MEPs, which is independent of molecular alignment. It is based on the edit distance of weighted rooted trees, which encode the geometrical and topological information of Negative Molecular Isopotential Surfaces. A meaningful chemical classification of a set of 46 molecules with different functional groups was achieved. Structure--activity relationships for the corticosteroid binding affinity (CBG) of 31 steroids by means of hierarchical clustering resulted in a clear partitioning in high, intermediate, and low activity groups, whereas the results from quantitative structure--activity relationships, obtained from a partial least-squares analysis, showed comparable or better cross-validated correlation coefficients than the ones reported for previous methods based solely in the MEP.
Morphological Modularity Can Enable the Evolution of Robot Behavior to Scale Linearly with the Number of Environmental Features

In evolutionary robotics, populations of robots are typically trained in simulation before one or more of them are instantiated as physical robots. However, in order to evolve robust behavior, each robot must be evaluated in multiple environments. If an environment is characterized by f free parameters, each of which can take one of np features, each robot must be evaluated in all np environments to ensure robustness. Here, we show that if the robots are constrained to have modular morphologies and controllers, they only need to be evaluated in np environments to reach the same level of robustness. This becomes possible because the robots evolve such that each module of the morphology allows the controller to independently recognize a familiar percept in the environment, and each percept corresponds to one of the environmental free parameters. When exposed to a new environment, the robot perceives it as a novel combination of familiar percepts which it can solve without requiring further training. A non-modular morphology and controller however perceives the same environment as a completely novel environment, requiring further training. This acceleration in evolvability – the rate of the evolution of adaptive and robust behavior – suggests that evolutionary robotics may become a scalable approach for automatically creating complex autonomous machines, if the evolution of neural and morphological modularity is taken into account.
GeoS: Geodesic Image Segmentation

This paper presents GeoS, a new algorithm for the efficient segmentation of n-dimensional image and video data. The segmentation problem is cast as approximate energy minimization in a conditional random field. A new, parallel filtering operator built upon efficient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. An economical search algorithm finds the solution with minimum energy within a sensible and highly restricted subset of all possible labellings. Advantages include: i) computational efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to handle generally complex energy models. Comparison with max-flow indicates up to 60 times greater computational efficiency as well as greater memory efficiency. GeoS is validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data. Numerous results on interactive and automatic segmentation of photographs, video and volumetric medical image data are presented.
Role of Environmental Contaminants in the Etiology of Alzheimer's Disease: A Review

Alzheimer's dis ease (AD) is a leading cause of mortality in the developed world with 70% risk attributable to genetics. The remaining 30% of AD risk is hypothesized to include environmental factors and human lifestyle patterns. Environmental factors possibly include inorganic and organic hazards, exposure to toxic metals (aluminium, copper), pesticides (organochlorine and organophosphate insecticides), industrial chemicals (flame retardants) and air pollutants (particulate matter). Long term exposures to these environmental contaminants together with bioaccumulation over an individual's life-time are speculated to induce neuroinflammation and neuropathology paving the way for developing AD. Epidemiologic associations between environmental contaminant exposures and AD are still limited. However, many in vitro and animal studies have identified toxic effects of environmental contaminants at the cellular level, revealing alterations of pathways and metabolisms associated with AD that warrant further investigations. This review provides an overview of in vitro, animal and epidemiological studies on the etiology of AD, highlighting available data supportive of the long hypothesized link between toxic environmental exposures and development of AD pathology.
FIHT2 Algorithm: A Fast Incremental Hough Transform

FIHT2 algorithm defined by p = x . cos 0 + y .sin 0 + (a/(21()) . x . s ine at 0 5 6 < a / 2 and at p = x . cose + y . sin 0 + (aJ(2It')) . y . cos 0 at a / 2 5 0 < a is a Hough transform which requires nothing of the trigonometric and functional operations to generate the Hough distributions. It is demonstrated in this paper that the FIHT2 is a complete alternative of the usual Hough transform(HT) defined by p = x.cos O+y.sin 0 in the sense that the both transforms could work perfectly as a line detector. It is easy to show that the Hough curves of the FIIJT2 can be generated in a incremental way where addition operation is exclusively needed. It is also investigated that the difference between HT and FIHT2 could be estimated to be neglected.
Double dissociation without modularity: evidence from connectionist neuropsychology.

Many theorists assume that the cognitive system is composed of a collection of encapsulated processing components or modules, each dedicated to performing a particular cognitive function. On this view, selective impairments of cognitive tasks following brain damage, as evidenced by double dissociations, are naturally interpreted in terms of the loss of particular processing components. By contrast, the current investigation examines in detail a double dissociation between concrete and abstract work reading after damage to a connectionist network that pronounces words via meaning and yet has no separable components (Plaut & Shallice, 1993). The functional specialization in the network that gives rise to the double dissociation is not transparently related to the network's structure, as modular theories assume. Furthermore, a consideration of the distribution of effects across quantitatively equivalent individual lesions in the network raises specific concerns about the interpretation of single-case studies. The findings underscore the necessity of relating neuropsychological data to cognitive theories in the context of specific computational assumptions about how the cognitive system operates normally and after damage.
Spatial computation

This paper describes a computer architecture, Spatial Computation (SC), which is based on the translation of high-level language programs directly into hardware structures. SC program implementations are completely distributed, with no centralized control. SC circuits are optimized for wires at the expense of computation units.In this paper we investigate a particular implementation of SC: ASH (Application-Specific Hardware). Under the assumption that computation is cheaper than communication, ASH replicates computation units to simplify interconnect, building a system which uses very simple, completely dedicated communication channels. As a consequence, communication on the datapath never requires arbitration; the only arbitration required is for accessing memory. ASH relies on very simple hardware primitives, using no associative structures, no multiported register files, no scheduling logic, no broadcast, and no clocks. As a consequence, ASH hardware is fast and extremely power efficient.In this work we demonstrate three features of ASH: (1) that such architectures can be built by automatic compilation of C programs; (2) that distributed computation is in some respects fundamentally different from monolithic superscalar processors; and (3) that ASIC implementations of ASH use three orders of magnitude less energy compared to high-end superscalar processors, while being on average only 33% slower in performance (3.5x worst-case).
Surgical Gesture Segmentation and Recognition

Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work  shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.
Muscular fatigue patterning in power grip assessment

Physical fatigue has been identified as a risk factor associated with the onset of occupational injury. Muscular fatigue developed from repetitive hand-gripping tasks is of particular concern. This study examined the use of a maximal, repetitive, static power grip test of strength-endurance in detecting differences in exertions between workers with uninjured and injured hands, and workers who were asked to provide insincere exertions. The main dependent variable of interest was power grip muscular force measured with a force strain gauge. Group data showed that the power grip protocol, used in this study, provided a valid and reliable estimate of wrist-hand strength-endurance. Force fatigue curves showed both linear and curvilinear effects among the study groups. An endurance index based on force decrement during repetitive power grip was shown to differentiate between uninjured, injured, and insincere groups.
Towards a more precise characterisation of creativity in AI

I summarise and attempt to clarify some concepts presented in and arising from Margaret Boden’s (1990) descriptive hierarchy of creativity, by formalising the ideas she proposes. I exemplify their use by broadly describing the development of art music from the 10th to the 20th century in these more formal terms. I suggest that Boden’s descriptive framework, once elaborated, is more powerful than it first appears.
Convergence and Hybrid Information Technology

Recently mobile communication systems such as 3GPP LTE, IEEE802.16e have been growing interest in the femto-cell systems for increase of data rates and enhanced call quality. In a femto-cell system, frequent handover among femto-cells may happen. Also, the probability of handover failure may increase during the frequent handover. In this paper, we propose a load-balanced handover with adaptive hysteresis in a femto-cell system considering channel allocation status of target base station. The simulation results show that the proposed schemes reduce the ping-pong rate and improve the MS's handover-related performance in terms of handover failure probability compared with the conventional handover method in a femto-cell system.
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer

An unprecedented booming has been witnessed in the research area of artistic style transfer ever since Gatys et al. introduced the neural method. One of the remaining challenges is to balance a trade-off among three critical aspects—speed, flexibility, and quality: (i) the vanilla optimization-based algorithm produces impressive results for arbitrary styles, but is unsatisfyingly slow due to its iterative nature, (ii) the fast approximation methods based on feed-forward neural networks generate satisfactory artistic effects but bound to only a limited number of styles, and (iii) feature-matching methods like AdaIN achieve arbitrary style transfer in a real-time manner but at a cost of the compromised quality. We find it considerably difficult to balance the trade-off well merely using a single feed-forward step and ask, instead, whether there exists an algorithm that could adapt quickly to any style, while the adapted model maintains high efficiency and good image quality. Motivated by this idea, we propose a novel method, coined MetaStyle, which formulates the neural style transfer as a bilevel optimization problem and combines learning with only a few post-processing update steps to adapt to a fast approximation model with satisfying artistic effects, comparable to the optimization-based methods for an arbitrary style. The qualitative and quantitative analysis in the experiments demonstrates that the proposed approach achieves high-quality arbitrary artistic style transfer effectively, with a good trade-off among speed, flexibility, and quality.