nils-herrmann/clean_scirepeval huggingface dataset:
Outline for a Logical Theory of Adaptive Systems

The purpose of this paper is to outline a theory of automata appropriate to the properties, requirements and questions of adaptation. The conditions that such a theory should satisfy come from not one but several fields: It should be possible to formulate, at least in an abstract version, some of the key hypotheses and problems from relevant parts of biology, particularly the areas concerned with molecular control and neurophysiology. The work in theoretical genetics initiated by R. A. Fisher  and Sewall Wright [24] should find a natural place in the theory. At the same time the rigorous methods of automata theory should be brought to bear (particularly those parts concerned with growing automata [1, 2, 3, 7, 8, 12, 15, 18, 23]). Finally the theory should include among its models abstract counterparts of artificial adaptive systems currently being studied, systems such as Newell-Shaw-Simon's "General Problem Solver" , Selfridge's "Pandemonium" , von Neumann's self-reproducing automata [22] and Turing's morphogenetic systems [19, 20]. The theory outlined here (which is intended as a theory and not the theory) is presented in four main parts. Section 2 discusses the study of adaptation via generation procedures and generated populations. Section 3 defines a continuum of generation procedures realizable in a reasonably direct fashion. Section 4 discusses the realization of generation procedures as populations of interacting programs in an iterative circuit computer. Section 5 discusses the process of adaptation in the context of the earlier sections. The paper concludes with a discussion of the nature of the theorems of this theory. Before entering upon the detailed discussion, one general feature of the theory should be noted. The interpretations or models of the theory divide into two broad categories: "complete" models and "incomplete" models. The "complete" models comprise the artificial systems--systems with properties and specifications completely delimited at the outset (cf. the rules of a game). One set of "complete" models for the theory consists of various programmed parallel computers. The "incomplete" models encompass natural systems. Any natural system involves an unlimited number of factors and, inevitably, the theory can handle only a selected few of these. Because there will always be variables which do not have explicit counterparts in the theory, the derived statements must be approximate relative to natural systems. For this reason it helps greatly that
Multi-Topic E-mail Authorship Attribution Forensics

In this paper we describe an investigation of forensic authorship identification or categorisation undertaken on multitopic e-mail documents. We use an extended set of e-mail document features such as structural characteristics and linguistic patterns together with a Support Vector Machine learning algorithm. Experiments on a number of e-mail documents generated by different authors on a set of topics gave promising results for both interand intra-topic author categorisation.
A Computational Model for the Dynamical Learning of Event Taxonomies

We present a computational model that can learn event taxonomies online from the continuous sensorimotor information flow perceived by an agent while interacting with its environment. Our model implements two fundamental learning biases. First, it learns probabilistic event models as temporal sensorimotor forward models and event transition models, which predict event model transitions given particular perceptual circumstances. Second, learning is based on the principle of minimizing free energy, which is further biased towards the detection of free energy transients. As a result, the algorithm forms conceptual structures that encode events and event boundaries. We show that event taxonomies can emerge when the algorithm is run on multiple levels of precision. Moreover, we show that generally any type of forward model can be used, as long as it learns sufficiently fast. Finally, we show that the developed structures can be used to hierarchically plan goaldirected behavior by means of active inference.
Split learning for health: Distributed deep learning without sharing raw patient data

Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.
Cross-language transfer learning for deep neural network based speech enhancement

In this paper, we propose a transfer learning approach to adapt a well-trained model obtained with high-resource materials of one language to another target language using a small amount of adaptation data for speech enhancement based on deep neural networks (DNNs). We investigate the performance degradation issues of enhancing noisy Mandarin speech data using DNN models already trained with only English speech materials, and vice versa. By assuming that the hidden layers of the well-trained DNN regression model as a cascade of feature extractors, we hypothesize that the first several layers should be transferable between languages. Our experimental results indicate that even with only about 1 minute of adaptation data from the resource-limited language we can achieve a considerable performance improvement over the DNN model without cross-language transfer learning.
A Compact, Wideband Circularly Polarized Co-designed Filtering Antenna and Its Application for Wearable Devices With Low SAR

A compact circularly polarized (CP) co-designed filtering antenna is reported. The device is based on a patch radiator seamlessly integrated with a bandpass filter composed of coupled stripline open-loop resonators, which are designed together as a system. In the proposed design, the patch functions simultaneously as the radiator and the last stage resonator of the filter, resulting in a low-profile integrated radiating and filtering module with a small overall form factor of 0.53λ0 × 0.53λ0 × 0.07λ0. It is shown that the filtering circuit not only ensures frequency selectivity but also provides impedance matching functionality, which serves to broaden both the impedance and axial ratio bandwidths. The designed filtering antenna was fabricated and measured, experimentally achieving an S11 <; -13.5 dB, an axial ratio of less than 3 dB and a gain higher than 5.2 dBi over a bandwidth from 3.77 to 4.26 GHz, i.e., around 12.2%, which makes it an excellent candidate for integration into a variety of wireless systems. A linearly polarized version of the integrated filtering antenna was also demonstrated. In addition, further full-wave simulations and experiments were carried out to verify that the designed CP filtering antenna maintains its properties even when mounted on different positions of the human body with various body gestures. The stable impedance and radiation properties also make it a suitable candidate as a wearable antenna for off-body wireless communications.
Controlling Robot Morphology From Incomplete Measurements

Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions. Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body-mounted robotic arm gathers the missing data.
Aircraft in Your Head : How Air Traffic Controllers Mentally Organize Air Traffic

The StayCentered Project at Technische Universität Chemnitz aims for assisting air traffic controllers in stressful traffic situations. Therefore we are seeking to comprehend air traffic controllers’ principles of operation within the dyadic team structure. First exploratory research revealed insights into air traffic controllers’ practices, their information processing (mental models), potential stressors, and related emotional effects. This paper discusses the results and the implications for air traffic controllers’ work in general and the StayCentered project in particular. Air traffic control; HCI; Decision Support; Mental Models
Minimally supervised question classification on fine-grained taxonomies

This article presents a minimally supervised approach to question classification on fine-grained taxonomies. We have defined an algorithm that automatically obtains lists of weighted terms for each class in the taxonomy, thus identifying which terms are highly related to the classes and are highly discriminative between them. These lists have then been applied to the task of question classification. Our approach is based on the divergence of probability distributions of terms in plain text retrieved from the Web. A corpus of questions with which to train the classifier is not therefore necessary. As the system is based purely on statistical information, it does not require additional linguistic resources or tools. The experiments were performed on English questions and their Spanish translations. The results reveal that our system surpasses current supervised approaches in this task, obtaining a significant improvement in the experiments carried out.
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries

Among the various types of spatio-temporal queries, the most common ones involve window queries in time. In particular, timestamp (or timeslice) queries retrieve all objects that intersect a window at a specific timestamp. Interval queries include multiple (usually consecutive) timestamps. Although several indexes have been developed for either type, currently there does not exist a structure that can efficiently process both query types. This is a significant problem due to the fundamental importance of these queries in any spatio-temporal system that deals with historical information retrieval. Our paper addresses the problem by proposing the MV3R-tree, a structure that utilizes the concepts of multi-version B-trees and 3D-Rtrees. Extensive experimentation proves that MV3R-trees compare favorably with specialized structures aimed at timestamp and interval window queries, both in terms of time and space requirements.
Get in Step With Synchronization

This paper presents a review of power-system synchronization. When two sources are paralleled, it is crucial to close the interconnecting circuit breaker when both sources are in voltage, frequency, and phase coincidence. Operators can synchronize manually, or use the latest, state-of-the-art autosynchronizers (device 25A) and sync-check relays (ANSI/IEEE device 25) to automate closing. Generator and bus synchronization share most principles, with some important differences for each type of synchronization. For generation plants, closing manually or applying an automatic synchronizer depends a lot on the plant configuration and operating mode. For bus-line applications, synchronizing depends on power-system stiffness, motor loads, and whether a wye-delta transformer is between the line and the bus. Methods for attaining proper synch-check and fast synchronization are discussed.
Peeking Behind the Curtains of Serverless Platforms

Serverless computing is an emerging paradigm in which an application’s resource provisioning and scaling are managed by third-party services. Examples include AWS Lambda, Azure Functions, and Google Cloud Functions. Behind these services’ easy-to-use APIs are opaque, complex infrastructure and management ecosystems. Taking on the viewpoint of a serverless customer, we conduct the largest measurement study to date, launching more than 50,000 function instances across these three services, in order to characterize their architectures, performance, and resource management efficiency. We explain how the platforms isolate the functions of different accounts, using either virtual machines or containers, which has important security implications. We characterize performance in terms of scalability, coldstart latency, and resource efficiency, with highlights including that AWS Lambda adopts a bin-packing-like strategy to maximize VM memory utilization, that severe contention between functions can arise in AWS and Azure, and that Google had bugs that allow customers to use resources for free.
Using Search Queries to Understand Health Information Needs in Africa

e lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health information needs of people in these nations. Without understanding people’s everyday needs, concerns, and misconceptions, health organizations and policymakers lack the ability to eectively target education and programming eorts. In this paper, we propose a boom-up approach that uses search data from individuals to uncover and gain insight into health information needs in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common search themes or topics, revealing a wide-spread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the dierent paerns that emerge in health information needs by demographic groups (age and sex) and country. We also uncover discrepancies in the quality of content returned by search engines to users by topic. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education eorts both onand oine.
JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis

Thanks to improvements in machine learning techniques including deep learning, a free large-scale speech corpus that can be shared between academic institutions and commercial companies has an important role. However, such a corpus for Japanese speech synthesis does not exist. In this paper, we designed a novel Japanese speech corpus, named the"JSUT corpus,"that is aimed at achieving end-to-end speech synthesis. The corpus consists of 10 hours of reading-style speech data and its transcription and covers all of the main pronunciations of daily-use Japanese characters. In this paper, we describe how we designed and analyzed the corpus. The corpus is freely available online.
Strip-map Phase Gradient Autofocus

This paper describes a generalisation of the Phase Gradient Autofocus (PGA) algorithm that allows strip-map operation. A standard autofocus technique, PGA, uses prominent points within the target scene to estimate the point spread function of the system. PGA was developed for tomographic mode spotlight synthetic aperture radar (SAR) but has limited applicability for side-scan synthetic aperture operation. We show how it can be generalised to work with stripmap geometries and relate our new method to the previous PGA extension to strip-map systems. Strip-map PGA (SPGA) works by employing the (image domain) 2D phase correction technique. Using the scale transform of the imaging system, the 2D phase errors are related to aperture position errors. Robust phase error estimates are obtained by utilising range redundancies. We also present a modified Phase Curvature Autofocus (PCA) algorithm applicable to strip-map operation that also uses the scale transform. Preliminary results of the two algorithms are presented showing a similar performance, and a discussion of the limitations of the algorithms is given.
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.
Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees

We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms featurebased joint model by Li and Ji (2014). We also compare our model with an end-toend tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1% on entity mentions and 2% on relations. Our finegrained analysis also shows that our model performs significantly better on AGENTARTIFACT relations, while SPTree performs better on PHYSICAL and PARTWHOLE relations.
An evolutionary artificial neural networks approach for breast cancer diagnosis

This paper presents an evolutionary artificial neural network (EANN) approach based on the pareto-differential evolution (PDE) algorithm augmented with local search for the prediction of breast cancer. The approach is named memetic pareto artificial neural network (MPANN). Artificial neural networks (ANNs) could be used to improve the work of medical practitioners in the diagnosis of breast cancer. Their abilities to approximate nonlinear functions and capture complex relationships in the data are instrumental abilities which could support the medical domain. We compare our results against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost.
Priming and human memory systems.

Priming is a nonconscious form of human memory, which is concerned with perceptual identification of words and objects and which has only recently been recognized as separate from other forms of memory or memory systems. It is currently under intense experimental scrutiny. Evidence is converging for the proposition that priming is an expression of a perceptual representation system that operates at a pre-semantic level; it emerges early in development, and access to it lacks the kind of flexibility characteristic of other cognitive memory systems. Conceptual priming, however, seems to be based on the operations of semantic memory.
A flexible exoskeleton for hip assistance

This paper proposes a new concept called “a flexible exoskeleton” and presents the design of a wearable walking assistance device to provide physical gait assistance for the elderly. To overcome some limitations of previous wearable walking assistance devices, 3 novel mechanisms are proposed: 1) flexible thigh frames that withstand the designated load direction, 2) an adjustable flexible hip brace surrounding the wearer's body, and 3) slim and backdrivable actuators that minimize the maximum height and resistance. The proposed walking assistance device has reduced metabolic energy consumption by 13.2±4% compared with a free condition.
Survey of Pedestrian Detection for Advanced Driver Assistance Systems

Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.
Offline and Online calibration of Mobile Robot and SLAM Device for Navigation

Robot navigation technology is required to accomplish difficult tasks in various environments. In navigation, it is necessary to know the information of the external environments and the state of the robot under the environment. On the other hand, various studies have been done on SLAM technology, which is also used for navigation, but also applied to devices for Mixed Reality and the like. In this paper, we propose a robot-device calibration method for navigation with a device using SLAM technology on a robot. The calibration is performed by using the position and orientation information given by the robot and the device. In the calibration, the most efficient way of movement is clarified according to the restriction of the robot movement. Furthermore, we also show a method to dynamically correct the position and orientation of the robot so that the information of the external environment and the shape information of the robot maintain consistency in order to reduce the dynamic error occurring during navigation. Our method can be easily used for various kinds of robots and localization with sufficient precision for navigation is possible with offline calibration and online position correction. In the experiments, we confirm the parameters obtained by two types of offline calibration according to the degree of freedom of robot movement and validate the effectiveness of online correction method by plotting localized position error during robot’s intense movement. Finally, we show the demonstration of navigation using SLAM device.