nils-herrmann/clean_scirepeval huggingface dataset:
Evidence-based Health Informatics: How Do We Know What We Know?

Health IT is expected to have a positive impact on the quality and efficiency of health care. But reports on negative impact and patient harm continue to emerge. The obligation of health informatics is to make sure that health IT solutions provide as much benefit with as few negative side effects as possible. To achieve this, health informatics as a discipline must be able to learn, both from its successes as well as from its failures.



To present motivation, vision, and history of evidence-based health informatics, and to discuss achievements, challenges, and needs for action.



Reflections on scientific literature and on own experiences.



Eight challenges on the way towards evidence-based health informatics are identified and discussed: quality of studies; publication bias; reporting quality; availability of publications; systematic reviews and meta-analysis; training of health IT evaluation experts; translation of evidence into health practice; and post-market surveillance. Identified needs for action comprise: establish health IT study registers; increase the quality of publications; develop a taxonomy for health IT systems; improve indexing of published health IT evaluation papers; move from meta-analysis to meta-summaries; include health IT evaluation competencies in curricula; develop evidence-based implementation frameworks; and establish post-marketing surveillance for health IT.



There has been some progress, but evidence-based health informatics is still in its infancy. Building evidence in health informatics is our obligation if we consider medical informatics a scientific discipline.
Common and distinct networks underlying reward valence and processing stages: A meta-analysis of functional neuroimaging studies

To better understand the reward circuitry in human brain, we conducted activation likelihood estimation (ALE) and parametric voxel-based meta-analyses (PVM) on 142 neuroimaging studies that examined brain activation in reward-related tasks in healthy adults. We observed several core brain areas that participated in reward-related decision making, including the nucleus accumbens (NAcc), caudate, putamen, thalamus, orbitofrontal cortex (OFC), bilateral anterior insula, anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC), as well as cognitive control regions in the inferior parietal lobule and prefrontal cortex (PFC). The NAcc was commonly activated by both positive and negative rewards across various stages of reward processing (e.g., anticipation, outcome, and evaluation). In addition, the medial OFC and PCC preferentially responded to positive rewards, whereas the ACC, bilateral anterior insula, and lateral PFC selectively responded to negative rewards. Reward anticipation activated the ACC, bilateral anterior insula, and brain stem, whereas reward outcome more significantly activated the NAcc, medial OFC, and amygdala. Neurobiological theories of reward-related decision making should therefore take distributed and interrelated representations of reward valuation and valence assessment into account.
WHAT IS SAID AND PSYCHOLOGICAL REALITY; GRICE’S PROJECT AND RELEVANCE THEORISTS’ CRITICISMS One of the most important aspects of Grice’s theory of conversation

One of the most important aspects of Grice’s theory of conversation is the drawing of a borderline between what is said and what is implicated. Grice’s views concerning this borderline have been strongly and influentially criticised by relevance theorists. In particular, it has become increasingly widely accepted that Grice’s notion of what is said is too limited, and that pragmatics has a far larger role to play in determining what is said than Grice would have allowed.  In this paper, I argue that the rejection of Grice has moved too swiftly, as a key line of objection which has led to this rejection is flawed. The flaw, we will see, is that relevance theorists rely on a misunderstanding of Grice’s project in his theory of conversation. I am not arguing that Grice’s versions of saying and implicating are right in all details, but simply that certain widespread reasons for rejecting his theory are based on  Relevance theorists, I will suggest, systematically misunderstand Grice by taking him to be engaged in the same project that they are: making sense of the psychological processes by which we interpret utterances. Notions involved with this project will need to be ones that are relevant to the psychology of utterance interpretation. Thus, it is only reasonable that relevance theorists will require that what is said and what is implicated should be psychologically real to the audience. (We will see that this requirement plays a crucial role in their arguments against Grice.) Grice, I will argue, was not pursuing this project. Rather, I will suggest that he was trying to make sense of quite a different notion of what is said: one on which both speaker and audience may be wrong about what is said. On this sort of notion, psychological reality is not a requirement. So objections to Grice based on a requirement of psychological reality will fail.
Tensor graph convolutional neural network

In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and the other is cross-attribute graphs. Especially, we propose a graph preserving layer to memorize salient nodes of those factorized subgraphs, i.e. cross graph convolution and graph pooling. For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs. Taking this operation, then general graph convolution may be efficiently performed followed by the composition of small matrices, which thus reduces high memory and computational burden. Encapsuling sequence graphs into a recursive learning, the dynamics of graphs can be efficiently encoded as well as the spatial layout of graphs. To validate the proposed TGCNN, experiments are conducted on skeleton action datasets as well as matrix completion dataset. The experiment results demonstrate that our method can achieve more competitive performance with the state-of-the-art methods.
Business Model Innovation Paths and Tools

There is a multitude of tools available for Business Model Innovation (BMI). However, Business models (BM) and supporting tools are not yet widely known by micro, small and medium sized companies (SMEs). In this paper, we build on analysis of 61 cases to present typical BMI paths of European SMEs. Firstly, we constructed two paths for established companies that we named as 'I want to grow' and 'I want to make my business profitable'. We also found one path for start-ups: 'I want to start a new business'. Secondly, we suggest appropriate BM toolsets for the three paths. The identified paths and related tools contribute to BMI research and practise with an aim to boost BMI in SMEs.
Basic Properties of the Blockchain: (Invited Talk)

As the first decentralized cryptocurrency, Bitcoin  has ignited much excitement, not only for its novel realization of a central bank-free financial instrument, but also as an alternative approach to classical distributed computing problems, such as reaching agreement distributedly in the presence of misbehaving parties, as well as to numerous other applications-contracts, reputation systems, name services, etc. The soundness and security of these applications, however, hinges on the thorough understanding of the fundamental properties of its underlying blockchain data structure, which parties ("miners") maintain and try to extend by generating "proofs of work" (POW, aka "cryptographic puzzle"). In this talk we follow the approach introduced in , formulating such fundamental properties of the blockchain, and then showing how applications such as consensus and a robust public transaction ledger can be built ``on top'' of them. The properties are as follows, assuming the adversary's hashing power (our analysis holds against arbitrary attacks) is strictly less than ½ and high network synchrony:
 Common prefix: The blockchains maintained by the honest parties possess a large common prefix. More specifically, if two honest parties "prune" (i.e., cut off) k blocks from the end of their local chains, the probability that the resulting pruned chains will not be mutual prefixes of each other drops exponentially in the that parameter.
 Chain quality: We show a bound on the ratio of blocks in the chain of any honest party contributed by malicious parties. In particular, as the adversary's hashing power approaches ½, we show that blockchains are only guaranteed to have few, but still some, blocks contributed by honest parties.
 Chain growth: We quantify the number of blocks that are added to the blockchain during any given number of rounds during the execution of the protocol.  This property, which in  was proven and used directly in the form of a lemma, was explicitly introduced in . Identifying it as a separate property enables modular proofs of applications' properties.)
 The above properties hold assuming that all parties-honest and adversarial-"wake up" and start computing at the same time, or, alternatively, that they compute on a common random string (the "genesis" block) only made available at the exact time when the protocol execution is to begin. In this talk we also consider the question of whether such a trusted setup/behavioral assumption is necessary, answering it in the negative by presenting a Bitcoin-like blockchain protocol that is provably secure without trusted setup, and, further, overcomes such lack in a scalable way-i.e., with running time independent of the number of parties .
 A direct consequence of our construction above is that consensus can be solved directly by a blockchain protocol without trusted setup assuming an honest majority (in terms of computational power).
Building Information Modeling ( BIM ) : Benefits , Risks and Challenges

Building Information Modeling (BIM) has recently attained widespread attention in the Architectural, Engineering and Construction (AEC) industry. BIM represents the development and use of computer-generated n-dimensional (n-D) models to simulate the planning, design, construction and operation of a facility. It helps architects, engineers and constructors to visualize what is to be built in simulated environment and to identify potential design, construction or operational problems. In this paper, the benefits and possible risks of BIM and future challenges for the construction industry are discussed. First presented is the main concept of BIM with its advantages and possible applications in construction. Then the role of BIM in the construction industry and academia is discussed based on the results of three recent questionnaire surveys. After that, a case study of Hilton Aquarium project in Atlanta is presented to quantitatively illustrate the cost and time savings realized by developing and using a building information model. It is followed by data from 10 construction projects to determine the net BIM savings and BIM return on investment. At the end, BIM risks and future challenges for the construction industry are discussed.
Trends in satellite communications and the role of optical free-space communications

The communication needs of Earth observation satellites is steadily increasing. Within a few years, the data rate of such satellites will exceed 1 Gbps, the angular resolution of sensors will be less than 1 μrad, and the memory size of onboard data recorders will be beyond 1 Tbytes. Compared to radio frequency links, optical communications in space offer various advantages such as smaller and lighter equipment, higher data rates, limited risk of interference with other communications systems, and the effective use of frequency resources. This paper describes and compares the major features of radio and optical frequency communications systems in space and predicts the needs of future satellite communications.
Audio and face video emotion recognition in the wild using deep neural networks and small datasets

This paper presents the techniques used in our contribution to Emotion Recognition in the Wild 2016’s video based sub-challenge. The purpose of the sub-challenge is to classify the six basic emotions (angry, sad, happy, surprise, fear & disgust) and neutral. Compared to earlier years’ movie based datasets, this year’s test dataset introduced reality TV videos containing more spontaneous emotion. Our proposed solution is the fusion of facial expression recognition and audio emotion recognition subsystems at score level. For facial emotion recognition, starting from a network pre-trained on ImageNet training data, a deep Convolutional Neural Network is fine-tuned on FER2013 training data for feature extraction. The classifiers, i.e., kernel SVM, logistic regression and partial least squares are studied for comparison. An optimal fusion of classifiers learned from different kernels is carried out at the score level to improve system performance. For audio emotion recognition, a deep Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) is trained directly using the challenge dataset. Experimental results show that both subsystems individually and as a whole can achieve state-of-the art performance. The overall accuracy of the proposed approach on the challenge test dataset is 53.9%, which is better than the challenge baseline of 40.47% .
Towards an Ontology of Economic Value: a Preliminary Analysis

The notion of value and of value creation has raised interest over the last 30 years for both researchers and practitioners. Although several studies have been conducted in marketing, value remains and elusive and often ill-defined concept. A clear understanding of value and value determinants can increase the awareness in strategic decisions and pricing choices. Objective of this paper is to preliminary discuss the main kinds of entity that an ontology of economic value should deal with.
Sequence-to-Sequence Model with Attention for Time Series Classification

Encouraged by recent waves of successful applications of deep learning, some researchers have demonstrated the effectiveness of applying convolutional neural networks (CNN) to time series classification problems. However, CNN and other traditional methods require the input data to be of the same dimension which prevents its direct application on data of various lengths and multi-channel time series with different sampling rates across channels. Long short-term memory (LSTM), another tool in the deep learning arsenal and with its design nature, is more appropriate for problems involving time series such as speech recognition and language translation. In this paper, we propose a novel model incorporating a sequence-to-sequence model that consists two LSTMs, one encoder and one decoder. The encoder LSTM accepts input time series of arbitrary lengths, extracts information from the raw data and based on which the decoder LSTM constructs fixed length sequences that can be regarded as discriminatory features. For better utilization of the raw data, we also introduce the attention mechanism into our model so that the feature generation process can peek at the raw data and focus its attention on the part of the raw data that is most relevant to the feature under construction. We call our model S2SwA, as the short for Sequence-to-Sequence with Attention. We test S2SwA on both uni-channel and multi-channel time series datasets and show that our model is competitive with the state-of-the-art in real world tasks such as human activity recognition.
Default Recovery Rates and LGD in Credit Risk Modeling and Practice

Evidence from many countries in recent years suggests that collateral values and recovery rates on corporate defaults can be volatile and, moreover, that they tend to go down just when the number of defaults goes up in economic downturns. This link between recovery rates and default rates has traditionally been neglected by credit risk models, as most of them focused on default risk and adopted static loss assumptions, treating the recovery rate either as a constant parameter or as a stochastic variable independent from the probability of default. This traditional focus on default analysis has been partly reversed by the recent significant increase in the number of studies dedicated to the subject of recovery rate estimation and the relationship between default and recovery rates. This paper presents a detailed review of the way credit risk models, developed during the last thirty years, treat the recovery rate and, more specifically, its relationship with the probability of default of an obligor. We also review the efforts by rating agencies to formally incorporate recovery ratings into their assessment of corporate loan and bond credit risk and the recent efforts by the Basel Committee on Banking Supervision to consider “downturn LGD” in their suggested requirements under Basel II. Recent empirical evidence concerning these issues and the latest data on high-yield bond and leverage loan defaults is also presented and discussed.
A Flexible Real-Time Control System for Autonomous Vehicles

In this paper we present a framework for the real-time control of lightweight autonomous vehicles which comprehends a proposed hardand software design. The system can be used for many kinds of vehicles and offers high computing power and flexibility in respect of the control algorithms and additional application dependent tasks. It was originally developed to control a small quad-rotor UAV where stringent restrictions in weight and size of the hardware components exist, but has been transfered to a fixed-wing UAV and a ground vehicle for inand outdoor search and rescue missions. The modular structure and the use of a standard PC architecture at an early stage simplifies reuse of components and fast integration of new features.  Quadrotor UAV controlled by the proposed system
Effects of a music therapy group intervention on enhancing social skills in children with autism.

Research indicates that music therapy can improve social behaviors and joint attention in children with Autism Spectrum Disorder (ASD); however, more research on the use of music therapy interventions for social skills is needed to determine the impact of group music therapy.



To examine the effects of a music therapy group intervention on eye gaze, joint attention, and communication in children with ASD.



Seventeen children, ages 6 to 9, with a diagnosis of ASD were randomly assigned to the music therapy group (MTG) or the no-music social skills group (SSG). Children participated in ten 50-minute group sessions over a period of 5 weeks. All group sessions were designed to target social skills. The Social Responsiveness Scale (SRS), the Autism Treatment Evaluation Checklist (ATEC), and video analysis of sessions were used to evaluate changes in social behavior.



There were significant between-group differences for joint attention with peers and eye gaze towards persons, with participants in the MTG demonstrating greater gains. There were no significant between-group differences for initiation of communication, response to communication, or social withdraw/behaviors. There was a significant interaction between time and group for SRS scores, with improvements for the MTG but not the SSG. Scores on the ATEC did not differ over time between the MTG and SSG.



The results of this study support further research on the use of music therapy group interventions for social skills in children with ASD. Statistical results demonstrate initial support for the use of music therapy social groups to develop joint attention.
Implementing a fog/cloud architecture for supporting stream data management

The Internet of Mobile Things (IoMT) requires support for a data lifecycle process ranging from sorting, cleaning and monitoring data streams to more complex tasks such as querying, aggregation, and analytics. Current solutions for stream data management in IoMT have been focused on partial aspects of a data lifecycle process, with special emphasis on sensor networks. This paper aims to address this problem by developing an offline and real-time data lifecycle process that incorporates a layered, data-flow centric, and an edge/cloud computing approach that is needed for handling heterogeneous, streaming and geographicallydispersed IoMT data streams. We propose an end to end architecture to support an instant intra-layer communication that establishes a stream data flow in real-time to respond to immediate data lifecycle tasks at the edge layer of the system. Our architecture also provides offline functionalities for later analytics and visualization of IoMT data streams at the core layer of the system. Communication and process are thus the defining factors in the design of our stream data management solution for IoMT. We describe and evaluate our prototype implementation using real-time transit data feeds and a commercial edge-based platform. Preliminary results are showing the advantages of running data lifecycle tasks at the edge of the network for reducing the volume of data streams that are redundant and should not be transported to the cloud. stream data lifecycle, edge computing, cloud computing, Internet of Mobile Things, end to end architectures
Matrix completion by deep matrix factorization

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices.
Credit Card Fraud Detection through Parenclitic Network Analysis

1Department of Computer Science, Faculty of Science and Technology, Universidade Nova de Lisboa, Lisboa, Portugal 2Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain 3Data, Networks and Cybersecurity Research Institute,  Pozuelo de Alarcón, Madrid, Spain 6Cyber Security & Digital Trust, BBVA Group, 28050 Madrid, Spain