nils-herrmann/clean_scirepeval huggingface dataset:
A Machine Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery

Labor monitoring is crucial in modern health care, as it can be used to detect (and help avoid) significant problems with the fetus. In this paper we focus on hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to life-long disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are routinely monitored during labor. The key idea is to learn models of the fetal response to signals from its environment, using time series data recorded during labor. Then, we use the parameters of these models as attributes in a binary classification problem. A majority vote over several periods is taken to provide the current label for the fetus. We use a unique database of real clinical recordings, both from normal and pathological cases. Our approach classifies correctly more than half the pathological cases, 1.5 hours before delivery. These are cases that were missed by clinicians; early detection of this type would have allowed the physician to perform a Caesarean section, possibly avoiding the negative outcome.
A Discriminative Metric Learning Based Anomaly Detection Method

Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated.
Generic 2D/3D SLAM with NDT maps for lifelong application

In this paper, we present a new, generic approach for Simultaneous Localization and Mapping (SLAM). First of all, we propose an abstraction of the underlying sensor data using Normal Distribution Transform (NDT) maps that are suitable for making our approach independent from the used sensor and the dimension of the generated maps. We present some modifications for the original NDT mapping to handle free-space measurements explicitly and to enable its usage in dynamic environments with moving obstacles and persons. In the second part of this paper we describe our graph-based SLAM approach that is designed for lifelong usage. Therefore, the memory and computational complexity is limited by pruning the pose graph in an appropriate way.
GAIN: Missing Data Imputation using Generative Adversarial Nets

We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some components of a real data vector, imputes the missing components conditioned on what is actually observed, and outputs a completed vector. The discriminator (D) then takes a completed vector and attempts to determine which components were actually observed and which were imputed. To ensure that D forces G to learn the desired distribution, we provide D with some additional information in the form of a hint vector. The hint reveals to D partial information about the missingness of the original sample, which is used by D to focus its attention on the imputation quality of particular components. This hint ensures thatG does in fact learn to generate according to the true data distribution. We tested our method on various datasets and found that GAIN significantly outperforms state-of-the-art imputation methods.
Talent identification and development programmes in sport : current models and future directions.

Many children strive to attain excellence in sport. However, although talent identification and development programmes have gained popularity in recent decades, there remains a lack of consensus in relation to how talent should be defined or identified and there is no uniformly accepted theoretical framework to guide current practice. The success rates of talent identification and development programmes have rarely been assessed and the validity of the models applied remains highly debated. This article provides an overview of current knowledge in this area with special focus on problems associated with the identification of gifted adolescents. There is a growing agreement that traditional cross-sectional talent identification models are likely to exclude many, especially late maturing, 'promising' children from development programmes due to the dynamic and multidimensional nature of sport talent. A conceptual framework that acknowledges both genetic and environmental influences and considers the dynamic and multidimensional nature of sport talent is presented. The relevance of this model is highlighted and recommendations for future work provided. It is advocated that talent identification and development programmes should be dynamic and interconnected taking into consideration maturity status and the potential to develop rather than to exclude children at an early age. Finally, more representative real-world tasks should be developed and employed in a multidimensional design to increase the efficacy of talent identification and development programmes.
Polymorphic malware detection using sequence classification methods and ensembles

Identifying malicious software executables is made difficult by the constant adaptations introduced by miscreants in order to evade detection by antivirus software. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we apply methods designed for gene sequencing to detect malware in a manner robust to attacker adaptations. Whereas most gene classification tools are optimized for and restricted to an alphabet of four letters (nucleic acids), we have selected the Strand gene sequence classifier for malware classification. Strand’s design can easily accommodate unstructured data with any alphabet, including source code or compiled machine code. To demonstrate that gene sequence classification tools are suitable for classifying malware, we apply Strand to approximately 500 GB of malware data provided by the Kaggle Microsoft Malware Classification Challenge (BIG 2015) used for predicting nine classes of polymorphic malware. Experiments show that, with minimal adaptation, the method achieves accuracy levels well above 95% requiring only a fraction of the training times used by the winning team’s method.
Big Data Computing and Clouds: Challenges, Solutions, and Future Directions

This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.
Phidgets: easy development of physical interfaces through physical widgets

Physical widgets or phidgets are to physical user interfaces what widgets are to graphical user interfaces. Similar to widgets, phidgets abstract and package input and output devices: they hide implementation and construction details, they expose functionality through a well-defined API, and they have an (optional) on-screen interactive interface for displaying and controlling device state. Unlike widgets, phidgets also require: a connection manager to track how devices appear on-line; a way to link a software phidget with its physical counterpart; and a simulation mode to allow the programmer to develop, debug and test a physical interface even when no physical device is present. Our evaluation shows that everyday programmers using phidgets can rapidly develop physical interfaces.
On the relationship between the “default mode network” and the “social brain”

The default mode network (DMN) of the brain consists of areas that are typically more active during rest than during active task performance. Recently however, this network has been shown to be activated by certain types of tasks. Social cognition, particularly higher-order tasks such as attributing mental states to others, has been suggested to activate a network of areas at least partly overlapping with the DMN. Here, we explore this claim, drawing on evidence from meta-analyses of functional MRI data and recent studies investigating the structural and functional connectivity of the social brain. In addition, we discuss recent evidence for the existence of a DMN in non-human primates. We conclude by discussing some of the implications of these observations.
Evaluation of multiple features for violent scenes detection

Violent scenes detection (VSD) is a challenging problem because of the heterogeneous content, large variations in video quality, and complex semantic meanings of the concepts involved. In the last few years, combining multiple features from multi-modalities has proven to be an effective strategy for general multimedia event detection (MED), but the specific event detection like VSD has been comparatively less studied. Here, we evaluated the use of multiple features and their combination in a violent scenes detection system. We rigorously analyzed a set of low-level features and a deep learning feature that captures the appearance, color, texture, motion and audio in video. We also evaluated the utility of mid-level visual information obtained from detecting related violent concepts. Experiments were performed on the publicly available MediaEval VSD 2014 dataset. The results showed that visual and motion features are better than audio features. Moreover, the performance of the mid-level features was nearly as good as that of the low-level visual features. Experiments with a number of fusion methods showed that all single features are complementary and help to improve overall performance. This study also provides an empirical foundation for selecting feature sets that are capable of dealing with heterogeneous content comprising violent scenes in movies.
Variational Policy Search via Trajectory Optimization

In order to learn effective control policies for dynamical systems, policy search methods must be able to discover successful executions of the desired task. While random exploration can work well in simple domains, complex and highdimensional tasks present a serious challenge, particularly when combined with high-dimensional policies that make parameter-space exploration infeasible. We present a method that uses trajectory optimization as a powerful exploration strategy that guides the policy search. A variational decomposition of a maximum likelihood policy objective allows us to use standard trajectory optimization algorithms such as differential dynamic programming, interleaved with standard supervised learning for the policy itself. We demonstrate that the resulting algorithm can outperform prior methods on two challenging locomotion tasks.
Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry.

The brain activation of a group of high-functioning autistic participants was measured using functional magnetic resonance imaging during the performance of a Tower of London task, in comparison with a control group matched with respect to intelligent quotient, age, and gender. The 2 groups generally activated the same cortical areas to similar degrees. However, there were 3 indications of underconnectivity in the group with autism. First, the degree of synchronization (i.e., the functional connectivity or the correlation of the time series of the activation) between the frontal and parietal areas of activation was lower for the autistic than the control participants. Second, relevant parts of the corpus callosum, through which many of the bilaterally activated cortical areas communicate, were smaller in cross-sectional area in the autistic participants. Third, within the autism group but not within the control group, the size of the genu of the corpus callosum was correlated with frontal-parietal functional connectivity. These findings suggest that the neural basis of altered cognition in autism entails a lower degree of integration of information across certain cortical areas resulting from reduced intracortical connectivity. The results add support to a new theory of cortical underconnectivity in autism, which posits a deficit in integration of information at the neural and cognitive levels.
Trading off prediction accuracy and power consumption for context-aware wearable computing

Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the -wearable system's energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWaich sensing and notification platform. We improve power consumption techniques by providing competitive classification performance even in the low frequency region of 1-10 Hz and for the highly erratic wrist based sensing location. Furthermore, we propose and analyze a collection of selective sampling strategies in order to reduce the number of required sensor readings and the computation cycles even further. Our results indicate that optimized sampling schemes can increase the deployment lifetime of a wearable computing platform by a factor of four without a significant loss in prediction accuracy.
Attribute extraction and scoring: A probabilistic approach

Knowledge bases, which consist of concepts, entities, attributes and relations, are increasingly important in a wide range of applications. We argue that knowledge about attributes (of concepts or entities) plays a critical role in inferencing. In this paper, we propose methods to derive attributes for millions of concepts and we quantify the typicality of the attributes with regard to their corresponding concepts. We employ multiple data sources such as web documents, search logs, and existing knowledge bases, and we derive typicality scores for attributes by aggregating different distributions derived from different sources using different methods. To the best of our knowledge, ours is the first approach to integrate concept- and instance-based patterns into probabilistic typicality scores that scale to broad concept space. We have conducted extensive experiments to show the effectiveness of our approach.
Coloring Local Feature Extraction

Although color is commonly experienced as an indispensable quality in describing the world around us, state-of-the art local featurebased representations are mostly based on shape description, and ignore color information. The description of color is hampered by the large amount of variations which causes the measured color values to vary significantly. In this paper we aim to extend the description of local features with color information. To accomplish a wide applicability of the color descriptor, it should be robust to : 1. photometric changes commonly encountered in the real world, 2. varying image quality, from high quality images to snap-shot photo quality and compressed internet images. Based on these requirements we derive a set of color descriptors. The set of proposed descriptors are compared by extensive testing on multiple applications areas, namely, matching, retrieval and classification, and on a wide variety of image qualities. The results show that color descriptors remain reliable under photometric and geometrical changes, and with decreasing image quality. For all experiments a combination of color and shape outperforms a pure shape-based approach.
Unsupervised user intent modeling by feature-enriched matrix factorization

Spoken language interfaces are being incorporated into various devices such as smart phones and TVs. However, dialogue systems may fail to respond correctly when users' request functionality is not supported by currently installed apps. This paper proposes a feature-enriched matrix factorization (MF) approach to model open domain intents, which allows a system to dynamically add unexplored domains according to users' requests. First we leverage the structured knowledge from Wikipedia and Freebase to automatically acquire domain-related semantics to enrich features of input utterances, and then MF is applied to model automatically acquired knowledge, published app textual descriptions and users' spoken requests in a joint fashion; this generates latent feature vectors for utterances and user intents without need of prior annotations. Experiments show that the proposed MF models incorporated with rich features significantly improve intent prediction, achieving about 34% of mean average precision (MAP) for both ASR and manual transcripts.
Sensing-Throughput Tradeoff for Cognitive Radio Networks

In a cognitive radio network, the secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. To support this spectrum reuse functionality, the secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance in cognitive radio networks. There are two parameters associated with spectrum sensing: probability of detection and probability of false alarm. The higher the probability of detection, the better the primary users are protected. However, from the secondary users' perspective, the lower the probability of false alarm, the more chances the channel can be reused when it is available, thus the higher the achievable throughput for the secondary network. In this paper, we study the problem of designing the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected. We formulate the sensing-throughput tradeoff problem mathematically, and use energy detection sensing scheme to prove that the formulated problem indeed has one optimal sensing time which yields the highest throughput for the secondary network. Cooperative sensing using multiple mini-slots or multiple secondary users are also studied using the methodology proposed in this paper. Computer simulations have shown that for a 6 MHz channel, when the frame duration is 100 ms, and the signal-to-noise ratio of primary user at the secondary receiver is -20 dB, the optimal sensing time achieving the highest throughput while maintaining 90% detection probability is 14.2 ms. This optimal sensing time decreases when distributed spectrum sensing is applied.
Creating creativity: user interfaces for supporting innovation

A challenge for human-computer interaction researchers and user interf ace designers is to construct information technologies that support creativity. This ambitious goal can be attained by building on an adequate understanding of creative processes. This article offers a four-phase framework for creativity that might assist designers in providing effective tools for users: (1)Collect: learn from provious works stored in libraries, the Web, etc.; (2) Relate: consult with peers and mentors at early, middle, and late stages, (3)Create: explore, compose, evaluate possible solutions; and (4) Donate: disseminate the results and contribute to the libraries. Within this integrated framework, this article proposes eight activities that require human-computer interaction research and advanced user interface design. A scenario about an architect illustrates the process of creative work within such an environment.
The World report on ageing and health: a policy framework for healthy ageing

Although populations around the world are rapidly ageing, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. A coherent and focused public health response that spans multiple sectors and stakeholders is urgently needed. To guide this global response, WHO has released the first World report on ageing and health, reviewing current knowledge and gaps and providing a public health framework for action. The report is built around a redefinition of healthy ageing that centres on the notion of functional ability: the combination of the intrinsic capacity of the individual, relevant environmental characteristics, and the interactions between the individual and these characteristics. This Health Policy highlights key findings and recommendations from the report.
Classification of Household Devices by Electricity Usage Profiles

This paper investigates how to classify household items such as televisions, kettles and refrigerators based only on their electricity usage profile every 15 minutes over a fixed interval of time. We address this time series classification problem through deriving a set of features that characterise the pattern of usage and the amount of power used when a device is on. We evaluate a wide range of classifiers on both the raw data and the derived feature set using both a daily and weekly usage profile and demonstrate that whilst some devices can be identified with a high degree of accuracy, others are very hard to disambiguate with this granularity of data.
Universal Dependencies for Finnish

There has been substantial recent interest in annotation schemes that can be applied consistently to many languages. Building on several recent efforts to unify morphological and syntactic annotation, the Universal Dependencies (UD) project seeks to introduce a cross-linguistically applicable part-of-speech tagset, feature inventory, and set of dependency relations as well as a large number of uniformly annotated treebanks. We present Universal Dependencies for Finnish, one of the ten languages in the recent first release of UD project treebank data. We detail the mapping of previously introduced annotation to the UD standard, describing specific challenges and their resolution. We additionally present parsing experiments comparing the performance of a stateof-the-art parser trained on a languagespecific annotation schema to performance on the corresponding UD annotation. The results show improvement compared to the source annotation, indicating that the conversion is accurate and supporting the feasibility of UD as a parsing target. The introduced tools and resources are available under open licenses from http://bionlp.utu.fi/ud-finnish.html.