nils-herrmann/clean_scirepeval huggingface dataset:
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines

After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging on a shared view of the computational foundations of intelligence that promotes valuable cross-disciplinary exchanges on questions, methods, and results. We chart advances over the past several decades that address challenges of perception and action under uncertainty through the lens of computation. Advances include the development of representations and inferential procedures for large-scale probabilistic inference and machinery for enabling reflection and decisions about tradeoffs in effort, precision, and timeliness of computations. These tools are deployed toward the goal of computational rationality: identifying decisions with highest expected utility, while taking into consideration the costs of computation in complex real-world problems in which most relevant calculations can only be approximated. We highlight key concepts with examples that show the potential for interchange between computer science, cognitive science, and neuroscience.
Joint Discriminative Learning of Deep Dynamic Textures for 3D Mask Face Anti-Spoofing

Three-dimensional mask spoofing attacks have been one of the main challenges in face recognition. Compared with a 3D mask, a real face displays different facial motion patterns that are reflected by different facial dynamic textures. However, a large portion of these facial motion differences is subtle. We find that the subtle facial motion can be fully captured by multiple deep dynamic textures from a convolutional layer of a convolutional neural network, but not all deep dynamic textures from different spatial regions and different channels of a convolutional layer are useful for differentiation of subtle motions between real faces and 3D masks. In this paper, we propose a novel feature learning model to learn discriminative deep dynamic textures for 3D mask face anti-spoofing. A novel joint discriminative learning strategy is further incorporated in the learning model to jointly learn the spatial- and channel-discriminability of the deep dynamic textures. The proposed joint discriminative learning strategy can be used to adaptively weight the discriminability of the learned feature from different spatial regions or channels, which ensures that more discriminative deep dynamic textures play more important roles in face/mask classification. Experiments on several publicly available data sets validate that the proposed method achieves promising results in intra- and cross-data set scenarios.
Precocious puberty and statural growth.

Precocious puberty results mostly from the precocious activation of the gonadotropic axis. Although the age limits have recently been discussed, most physicians consider that onset of pubertal development before the age of 8 years in a girl or 9 years in a boy warrants at least a clinical and bone age evaluation by a paediatric endocrinologist. The major concern in precocious puberty is the underlying condition, and central nervous system or gonadal neoplasm have to be formally excluded as a first step in the diagnosis. A secondary concern is height, since precocious puberty leads to accelerated growth, accelerated bone maturation and ultimately reduced stature. Precocious puberty is heterogeneous and strict criteria should be used to define it, both in terms of age and in terms of potential for progression. Depot forms of GnRH agonists are now the standard treatment for progressive central precocious puberty and aim at alleviating the clinical symptoms of early pubertal development, their psychological consequences and the effects on growth. Here, we review the consequences of both central and gonadotropin-independent precocious puberty on adult stature and the information available on outcomes using the therapeutic regimens currently available. In girls with progressive precocious puberty, all published evidence indicates a gain of adult height over height predicted before treatment or over untreated historical controls. However, the apparent height gain (derived from the comparison of predicted and actual heights) is very variable, in large part due to the inaccuracy of height prediction methods. In girls with onset of puberty at the lower half of the normal age (8-10 years) distribution, trials using GnRH agonists have given negative results (no benefit of treatment). In boys, precocious puberty is rare and fewer results are available but point in the same direction. The most appropriate time for interrupting the treatment is still controversial. In conclusion, GnRH agonists restore adult height in children when it is compromised by precocious puberty.
Visualising the structure of architectural open spaces based on shape analysis

Visualisation and interpretation of architectural spaces are intellectually challenging and multifaceted exercises with fairly wide ranging applications and implications. In the context of urban planning, they are most commonly undertaken to evaluate the usage of the architectural space to ensure efficient navigation and accessibility . These exercises clearly assume a certain influence of the built structures on the human cognition. However, what aspect of architectural space affects the human behaviour still remains an open debate. In this respect, it is closely similar to an exercise to identify the unknown visual variables in information visualization. Since a quantitative analysis of the architectural geometric structure on a large scale will be a daunting computational task, the open space bounded by the built structures is studied instead . The study of architectural open spaces essentially involves the computation of the visibility polygon or isovist (space visible all around a viewpoint, see Figure 1a) from a viewpoint and calculating various shape measures of the visibility polygon. The isovist computation involves drawing rays
Temporal dynamics of brain activation during a working memory task

Working memory is responsible for the short-term storage and online manipulation of information necessary for higher cognitive functions, such as language, planning and problem-solving1,2. Traditionally, working memory has been divided into two types of processes: executive control (governing the encoding manipulation and retrieval of information in working memory) and active maintenance (keeping information available 'online'). It has also been proposed that these two types of processes may be subserved by distinct cortical structures, with the prefrontal cortex housing the executive control processes, and more posterior regions housing the content-specific buffers (for example verbal versus visuospatial) responsible for active maintenance3,4. However, studies in non-human primates suggest that dorsolateral regions of the prefrontal cortex may also be involved in active maintenance5–8. We have used functional magnetic resonance imaging to examine brain activation in human subjects during performance of a working memory task. We used the temporal resolution of this technique to examine the dynamics of regional activation, and to show that prefrontal cortex along with parietal cortex appears to play a role in active maintenance.
Demand Side Management in Smart Grids

Smart grids are considered to be the next generation electric grids. Drivers for this development are among others: an increased electricity demand, an ageing network infrastructure and an increased share of renewable energy sources. Sustainability and the European 20/20/20 climate and energy targets are also main drivers for this development. Some of the important aspects of smart grids are that they allow two-way communication between the actors in the grid and enable optimisation of electricity use and production and customer participation. Demand side management is associated to smart grids and means adapting the electricity demand to the electricity production and the available electricity in the grid. It is considered that smart grids and demand side management hold potential to facilitate an increased share of renewable energy sources and to reduce the need for the current power reserve, which is based on fossil energy sources. The aim of this study was to compile and analyse some of the work that has been done and is being implemented around the world concerning demand side management in smart grids with a focus on market rules and business models in relation to Swedish conditions. The study includes a review of selected research and demonstration projects. Success factors and knowledge gaps have been identified through an analysis of the project findings and recommendations. A description of the current Nordic market conditions is also given. The conclusions regarding the conditions on the Swedish electricity market is that it appears to be relatively well adjusted to demand side management with the possibility for hourly meter readings and a full deployment of smart meters. The Nord Pool electricity market allows both day-ahead trading and intra-day trading on the elspot and the elbas markets. The review of the projects show that there is a potential for achieving flexible load from private customers. Hourly metering is seen as a prerequisite for demand side management and active customers is also considered crucial. Visualisation of electricity consumption and current costs is shown to be an important tool for encouraging customers to take an active part in their electricity consumption. Some of the other methods for achieving flexibility are for example: different types of pricing models, a signal for renewable energy sources and direct and indirect control of smart appliances and heating systems. The aggregation concept is an example of direct control that have been used and show potential for achieving flexibility in the grid. The concept means that a so called aggregator, which for example could be the supplier or the distribution system operator, compiles and controls the electrical load for several customers. A key challenge that has been identified is the issue of standardisation. Exchange of experiences from various research and demonstration projects is also considered desirable to support the development of smart grid and demand response.
Forecasting the behavior of multivariate time series using neural networks

This pal~er presents a neural network approach to multivariate time-series anal.lwis. Real world observations ~?/flottr /.'ices in three cities have been used as a benchmark in our ev~eriments, l"eed/orward connectionist networks have bt,t,tl ~k,.signed to model flottr l , ices over the period/iom ..lltgttsl 1972 to Novt,mher 1980./or the cities ~?[Blt[]ith~. :lliltneapoli.s, and Kansas Cit): Remarkable sttccess has heen achieved ill training the lletworks to leatvt the price CllrlV [i," each t?/these cities and in m a k i l l g acc l t ra le price predictions. Our r¢,~lt/l.s show that the it¢ltral network al~proat'h is a leading contender with the statistical modeling apl,oaches.
A new adaptive frequency oscillator for gait assistance

To control exoskeletons for walking gait assistance, it is of primary importance to control them to act synchronously with the gaits of users. To effectively estimate the gait cycle (or the phase within a stride) of users, we propose a new adaptive frequency oscillator (AFO). While previous AFOs successfully estimated the walking frequency from joint angles as inputs, the new AFO, called particularly-shaped adaptive oscillator (PSAO) can estimate gait cycle from the same inputs, which would have required foot contact sensors in previous approaches. To predict the effects of PSAO-based gait assistance on human walking, it has been tested with neuromuscular walking simulation. In the simulation, the gait assistance system reduced the metabolic cost of walking for some assistance patterns. The walk ratio (step length per step rate) also changed as assistance patterns shifted in phase, which is meaningful because metabolic cost of walking in general is minimal at specific walk ratio. For a prototype exoskeleton we developed, the effect of gait assistance was experimented on a human subject walking on level ground and inclining slopes to verify the predictions from the simulation: (1) physiological cost index computed from heart rate significantly decreased indicating reduction in metabolic energy expenditure; (2) walk ratio was in fact controllable to an extent.
Dairy products and colorectal cancer risk: a systematic review and meta-analysis of cohort studies.

Previous studies of the association between intake of dairy products and colorectal cancer risk have indicated an inverse association with milk, however, the evidence for cheese or other dairy products is inconsistent.



We conducted a systematic review and meta-analysis to clarify the shape of the dose-response relationship between dairy products and colorectal cancer risk. We searched the PubMed database for prospective studies published up to May 2010. Summary relative risks (RRs) were estimated using a random effects model.



Nineteen cohort studies were included. The summary RR was 0.83 (95% CI [confidence interval]: 0.78-0.88, I2=25%) per 400 g/day of total dairy products, 0.91 (95% CI: 0.85-0.94, I2=0%) per 200 g/day of milk intake and 0.96 (95% CI: 0.83-1.12, I2=28%) per 50 g/day of cheese. Inverse associations were observed in both men and women but were restricted to colon cancer. There was evidence of a nonlinear association between milk and total dairy products and colorectal cancer risk, P<0.001, and the inverse associations appeared to be the strongest at the higher range of intake.



This meta-analysis shows that milk and total dairy products, but not cheese or other dairy products, are associated with a reduction in colorectal cancer risk.
The trouble with overconfidence.

The authors present a reconciliation of 3 distinct ways in which the research literature has defined overconfidence: (a) overestimation of one's actual performance, (b) overplacement of one's performance relative to others, and (c) excessive precision in one's beliefs. Experimental evidence shows that reversals of the first 2 (apparent underconfidence), when they occur, tend to be on different types of tasks. On difficult tasks, people overestimate their actual performances but also mistakenly believe that they are worse than others; on easy tasks, people underestimate their actual performances but mistakenly believe they are better than others. The authors offer a straightforward theory that can explain these inconsistencies. Overprecision appears to be more persistent than either of the other 2 types of overconfidence, but its presence reduces the magnitude of both overestimation and overplacement.
SDN-based solutions for Moving Target Defense network protection

Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.
Study of Chronic Wound Image Segmentation: Impact of Tissue Type and Color Data Augmentation

Chronic wound segmentation is an essential task for evaluating wound and its recovery progress. A physician usually measures a wound area to choose proper treatment according to wound conditions. However, precise measurement needs accurate image-region segmentation. With the advent of deep learning for semantic image segmentation, accuracy of region segmentation is dramatically higher than traditional methods. Unfortunately, semantic segmentation in prior work did not produce satisfactory outputs in wound image segmentation, even with a large training dataset. This work, therefore, rethinks about the challenge and aims at not only improving segmentation accuracy, but also studying the impact of wound tissue types and color on accuracy. Since an end-to-end approach of semantic segmentation in prior work performed relatively poorly, the proposed method employs both image processing and deep learning techniques. The experiments indicated that slough was the most challenging tissue to be segmented. Also, properly increasing color variety of wound images significantly improved segmentation performance. The accuracy of the proposed method was 72%, 40%, and 53% in terms of intersection over union for granulation, necrosis, and slough wound tissue types, respectively. The proposed method outperformed a prior end-to-end approach, even though this method employed particularly simpler neural network models and much smaller number of training images.
Authentication Algorithm for Intrusion Detection in Wireless Networks

Security has been a major issue in wireless networks. In a wireless network, wireless devices are prone to be unauthorized accessing data or resources. Hence it becomes necessary to consider issues of security such as : 1. Authentication, 2. Access Control. Traditional methods of Authentication has been to assign user names and passwords. This is extremely vulnerable to be accessed and misused. Therefore, to overcome these problems, This paper proposes a method by which the Username and Password are stored in an Hashed format, with the help of Neural Network learning the hashes instead of storing them in the tables. Thus, reducing the risk of being accessed. Therefore, a combination of authentication and access control could make a good tool for intrusion detection.
Learning Dynamic Memory Networks for Object Tracking

Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the target object’s appearance, making their tracking accuracy still far from state-ofthe-art. In this paper, we propose a dynamic memory network to adapt the template to the target’s appearance variations during tracking. An LSTM is used as a memory controller, where the input is the search feature map and the outputs are the control signals for the reading and writing process of the memory block. As the location of the target is at first unknown in the search feature map, an attention mechanism is applied to concentrate the LSTM input on the potential target. To prevent aggressive model adaptivity, we apply gated residual template learning to control the amount of retrieved memory that is used to combine with the initial template. Unlike tracking-by-detection methods where the object’s information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target’s appearance changes by updating the external memory. Moreover, unlike other tracking methods where the model capacity is fixed after offline training – the capacity of our tracker can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information. Extensive experiments on OTB and VOT demonstrates that our tracker MemTrack performs favorably against state-of-the-art tracking methods while retaining real-time speed of 50 fps.
Sensor relocation in mobile sensor networks

Recently there has been a great deal of research on using mobility in sensor networks to assist in the initial deployment of nodes. Mobile sensors are useful in this environment because they can move to locations that meet sensing coverage requirements. This paper explores the motion capability to relocate sensors to deal with sensor failure or respond to new events. We define the problem of sensor relocation and propose a two-phase sensor relocation solution: redundant sensors are first identified and then relocated to the target location. We propose a Grid-Quorum solution to quickly locate the closest redundant sensor with low message complexity, and propose to use cascaded movement to relocate the redundant sensor in a timely, efficient and balanced way. Simulation results verify that the proposed solution outperforms others in terms of relocation time, total energy consumption, and minimum remaining energy.
The Semantic Web - ISWC 2003

The Semantic Network, a component of the Unified Medical Language System® (UMLS), describes core biomedical knowledge consisting of semantic types and relationships. It is a well established, semi-formal ontology in widespread use for over a decade. We expected to “publish” this ontology on the Semantic Web, using OWL, with relatively little effort. However, we ran into a number of problems concerning alternative interpretations of the SN notation and the inability to express some of the interpretations in OWL. We detail these problems, as a cautionary tale to others planning to publish pre-existing ontologies on the Semantic Web, as a list of issues to consider when describing formally concepts in any ontology, and as a collection of criteria for evaluating alternative representations, which could form part of a methodology of ontology development.
Coordinate Noun Phrase Disambiguation in a Generative Parsing Model

In this paper we present methods for improving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the dependency between conjunct lexical heads. Our changes to the baseline model result in an increase in NP coordination dependency f-score from 69.9% to 73.8%, which represents a relative reduction in f-score error of 13%.
On the Generation of Skeletons from Discrete Euclidean Distance Maps

The skeleton is an important representation for shape analysis. A common approach for generating discrete skeletons takes three steps: 1) computing the distance map, 2) detecting maximal disks from the distance map, and 3) linking the centers of maximal disks (CMDs) into a connected skeleton. Algorithms using approximate distance metrics are abundant and their theory has been well established. However, the resulting skeletons may be inaccurate and sensitive to rotation. In this paper, we study methods for generating skeletons based on the exact Euclidean metric. We first show that no previous algorithms identifies the exact set of discrete maximal disks under the Euclidean metric. We then propose new algorithms and show that they are correct. To link CMDs into connected skeletons, we examine two prevalent approaches: connected thinning and steepest ascent. We point out that the connected thinning approach does not work properly for Euclidean distance maps. Only the steepest ascent algorithm produces skeletons that are truly medially placed. The resulting skeletons have all the desirable properties: they have the same simple connectivity as the figure, they are well-centered, they are insensitive to rotation, and they allow exact reconstruction. The effectiveness of our algorithms is demonstrated with numerous examples.