nils-herrmann/clean_scirepeval huggingface dataset:
Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs.
Semantic Pooling for Complex Event Analysis in Untrimmed Videos

Pooling plays an important role in generating a discriminative video representation. In this paper, we propose a new semantic pooling approach for challenging event analysis tasks (e.g., event detection, recognition, and recounting) in long untrimmed Internet videos, especially when only a few shots/segments are relevant to the event of interest while many other shots are irrelevant or even misleading. The commonly adopted pooling strategies aggregate the shots indifferently in one way or another, resulting in a great loss of information. Instead, in this work we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event analysis. Next, we propose a new isotonic regularizer that is able to exploit the constructed semantic ordering information. The resulting nearly-isotonic support vector machine classifier exhibits higher discriminative power in event analysis tasks. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new and closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and achieve promising improvements.
Future Edge Cloud and Edge Computing for Internet of Things Applications

The Internet is evolving rapidly toward the future Internet of Things (IoT) which will potentially connect billions or even trillions of edge devices which could generate huge amount of data at a very high speed and some of the applications may require very low latency. The traditional cloud infrastructure will run into a series of difficulties due to centralized computation, storage, and networking in a small number of datacenters, and due to the relative long distance between the edge devices and the remote datacenters. To tackle this challenge, edge cloud and edge computing seem to be a promising possibility which provides resources closer to the resource-poor edge IoT devices and potentially can nurture a new IoT innovation ecosystem. Such prospect is enabled by a series of emerging technologies, including network function virtualization and software defined networking. In this survey paper, we investigate the key rationale, the state-of-the-art efforts, the key enabling technologies and research topics, and typical IoT applications benefiting from edge cloud. We aim to draw an overall picture of both ongoing research efforts and future possible research directions through comprehensive discussions.
Online Peer-to-Peer Lending: A Lenders' Perspective

Online Peer-to-Peer lending platforms claim to be beneficial for both borrowers and lenders by eliminating expensive intermediaries and reducing transaction costs. However, are the often inexperienced lenders who operate in a pseudonymous online environment with potentially significant information asymmetries really able to obtain an attractive return on their investment? This paper discusses the question by presenting profitability data from the US platform Prosper.com. Although the overall investment performance has not been satisfactory for most rating categories, it is shown that following some simple investment rules improves profitability of a portfolio and leads to acceptable returns for all credit rating categories with exception of the high-risk one.
Linking employees' e-learning system use to their overall job outcomes: An empirical study based on the IS success model

e-Learning systems are increasingly being adopted by modern enterprises, because of their cost-down effect. However, the extant literature provides little insight into their beneficial consequences. It is expected that a transfer of acquired knowledge, skills and/or work attitudes to jobs will occur through elearning systems use. This makes e-learning systems a useful tool for improving job outcomes, although this is not well documented. The IS success model, which captures both the human and technological elements of information systems, provides a theoretical basis for linking system use to system task. The current study then examines the relation between e-learning systems use and overall job outcomes based on the model. The data collected from one hundred and ninety-three e-learning system users were analyzed with partial least square (PLS). The results indicate that e-learning systems are perceived as useful and satisfying by employees, and employees’ e-learning systems use is significantly associated with overall job outcomes. In practice, these results provide a basis for establishing a link between an organization’s investment in e-learning and human capital management. They also lend empirical support to the IS success model.
Regression modelling strategies for improved prognostic prediction.

Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.
Rethinking schizophrenia

How will we view schizophrenia in 2030? Schizophrenia today is a chronic, frequently disabling mental disorder that affects about one per cent of the world’s population. After a century of studying schizophrenia, the cause of the disorder remains unknown. Treatments, especially pharmacological treatments, have been in wide use for nearly half a century, yet there is little evidence that these treatments have substantially improved outcomes for most people with schizophrenia. These current unsatisfactory outcomes may change as we approach schizophrenia as a neurodevelopmental disorder with psychosis as a late, potentially preventable stage of the illness. This ‘rethinking’ of schizophrenia as a neurodevelopmental disorder, which is profoundly different from the way we have seen this illness for the past century, yields new hope for prevention and cure over the next two decades.
Morphogenetic Engineering: Reconciling Self-Organization and Architecture

Generally, phenomena of spontaneous pattern formation are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both architectured and self-organized. Can we understand their precise self-formation capabilities and integrate them with technological planning? Can physical systems be endowed with information, or informational systems be embedded in physics, to create autonomous morphologies and functions? This book is the first initiative of its kind toward establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of “self-architecturing” systems. Particular emphasis is set on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science—while, conversely, the benefits of selforganization are often underappreciated in engineering methodologies.
InfoVAE: Information Maximizing Variational Autoencoders

A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. We find that existing training objectives for variational autoencoders can lead to inaccurate amortized inference distributions and, in some cases, improving the objective provably degrades the inference quality. In addition, it has been observed that variational autoencoders tend to ignore the latent variables when combined with a decoding distribution that is too flexible. We again identify the cause in existing training criteria and propose a new class of objectives (InfoVAE) that mitigate these problems. We show that our model can significantly improve the quality of the variational posterior and can make effective use of the latent features regardless of the flexibility of the decoding distribution. Through extensive qualitative and quantitative analyses, we demonstrate that our models outperform competing approaches on multiple performance metrics.
KB-Enabled Query Recommendation for Long-Tail Queries

In recent years, query recommendation algorithms have been designed to provide related queries for search engine users. Most of these solutions, which perform extensive analysis of users' search history (or query logs), are largely insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study a new solution, which makes use of a knowledge base (or KB), such as YAGO and Freebase. A KB is a rich information source that describes how real-world entities are connected. We extract entities from a query, and use these entities to explore new ones in the KB. Those discovered entities are then used to suggest new queries to the user. As shown in our experiments, our approach provides better recommendation results for long-tail queries than existing solutions.
Sudarshan Kriya yogic breathing in the treatment of stress, anxiety, and depression: part I-neurophysiologic model.

Mind-body interventions are beneficial in stress-related mental and physical disorders. Current research is finding associations between emotional disorders and vagal tone as indicated by heart rate variability. A neurophysiologic model of yogic breathing proposes to integrate research on yoga with polyvagal theory, vagal stimulation, hyperventilation, and clinical observations. Yogic breathing is a unique method for balancing the autonomic nervous system and influencing psychologic and stress-related disorders. Many studies demonstrate effects of yogic breathing on brain function and physiologic parameters, but the mechanisms have not been clarified. Sudarshan Kriya yoga (SKY), a sequence of specific breathing techniques (ujjayi, bhastrika, and Sudarshan Kriya) can alleviate anxiety, depression, everyday stress, post-traumatic stress, and stress-related medical illnesses. Mechanisms contributing to a state of calm alertness include increased parasympathetic drive, calming of stress response systems, neuroendocrine release of hormones, and thalamic generators. This model has heuristic value, research implications, and clinical applications.
IOT Based Monitoring System in Smart Agriculture

Internet of Things (IoT) plays a crucial role in smart agriculture. Smart farming is an emerging concept, because IoT sensors capable of providing information about their agriculture fields. The paper aims making use of evolving technology i.e. IoT and smart agriculture using automation. Monitoring environmental factors is the major factor to improve the yield of the efficient crops. The feature of this paper includes monitoring temperature and humidity in agricultural field through sensors using CC3200 single chip. Camera is interfaced with CC3200 to capture images and send that pictures through MMS to farmers mobile using Wi-Fi.
A Traceability Attack against e-Passports

Since 2004, many nations have started issuing “e-passports” containing an RFID tag that, when powered, broadcast information. It is claimed that these passports are more secure and that our data will be protected from any possible unauthorised attempts to read it. In this paper we show that there is a flaw in one of the passport’s protocols that makes it possible to trace the movements of a particular passport, without having to break the passport’s cryptographic key. All an attacker has to do is to record one session between the passport and a legitimate reader, then by replaying a particular message, the attacker can distinguish that passport from any other. We have implemented our attack and tested it successfully against passports issued by a range of nations.
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees

Optimization problems with rank constraints arise in many applications, including matrix regression, structured PCA, matrix completion and matrix decomposition problems. An attractive heuristic for solving such problems is to factorize the low-rank matrix, and to run projected gradient descent on the nonconvex factorized optimization problem. The goal of this problem is to provide a general theoretical framework for understanding when such methods work well, and to characterize the nature of the resulting fixed point. We provide a simple set of conditions under which projected gradient descent, when given a suitable initialization, converges geometrically to a statistically useful solution. Our results are applicable even when the initial solution is outside any region of local convexity, and even when the problem is globally concave. Working in a non-asymptotic framework, we show that our conditions are satisfied for a wide range of concrete models, including matrix regression, structured PCA, matrix completion with real and quantized observations, matrix decomposition, and graph clustering problems. Simulation results show excellent agreement with the theoretical predictions.
Semantic Object Parsing with Graph LSTM

By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multidimensional data to general graph-structured data. Particularly, instead of evenly and fixedly dividing an image to pixels or patches in existing multi-dimensional LSTM structures (e.g., Row, Grid and Diagonal LSTMs [1][2]), we take each arbitrary-shaped superpixel as a semantically consistent node, and adaptively construct an undirected graph for each image, where the spatial relations of the superpixels are naturally used as edges. Constructed on such an adaptive graph topology, the Graph LSTM is more naturally aligned with the visual patterns in the image (e.g., object boundaries or appearance similarities) and provides a more economical information propagation route. Furthermore, for each optimization step over Graph LSTM, we propose to use a confidence-driven scheme to update the hidden and memory states of nodes progressively till all nodes are updated. In addition, for each node, the forgets gates are adaptively learned to capture different degrees of semantic correlation with neighboring nodes. Comprehensive evaluations on four diverse semantic object parsing datasets well demonstrate the significant superiority of our Graph LSTM over other state-of-the-art solutions.
Efficient deterministic multithreading without global barriers

Multithreaded programs execute nondeterministically on conventional architectures and operating systems. This complicates many tasks, including debugging and testing. Deterministic multithreading (DMT) makes the output of a multithreaded program depend on its inputs only, which can totally solve the above problem. However, current DMT implementations suffer from a common inefficiency: they use frequent global barriers to enforce a deterministic ordering on memory accesses. In this paper, we eliminate that inefficiency using an execution model we call deterministic lazy release consistency (DLRC). Our execution model uses the Kendo algorithm to enforce a deterministic ordering on synchronization, and it uses a deterministic version of the lazy release consistency memory model to propagate memory updates across threads. Our approach guarantees that programs execute deterministically even when they contain data races. We implemented a DMT system based on these ideas (RFDet) and evaluated it using 16 parallel applications. Our implementation targets C/C++ programs that use POSIX threads. Results show that RFDet gains nearly 2x speedup compared with DThreads-a start-of-the-art DMT system.
Mobile Edge Computing Empowers Internet of Things

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in realtime. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations. key words: Internet of Things, mobile edge computing, cloudlet, semantics, social network, green energy.
Multi-level comparison of data deduplication in a backup scenario

Data deduplication systems detect redundancies between data blocks to either reduce storage needs or to reduce network traffic. A class of deduplication systems splits the data stream into data blocks (chunks) and then finds exact duplicates of these blocks.
 This paper compares the influence of different chunking approaches on multiple levels. On a macroscopic level, we compare the chunking approaches based on real-life user data in a weekly full backup scenario, both at a single point in time as well as over several weeks.
 In addition, we analyze how small changes affect the deduplication ratio for different file types on a microscopic level for chunking approaches and delta encoding. An intuitive assumption is that small semantic changes on documents cause only small modifications in the binary representation of files, which would imply a high ratio of deduplication. We will show that this assumption is not valid for many important file types and that application-specific chunking can help to further decrease storage capacity demands.
Culture, gender, and the self: variations and impact of social comparison processes.

Psychological differences between women and men, far from being invariant as a biological explanation would suggest, fluctuate in magnitude across cultures. Moreover, contrary to the implications of some theoretical perspectives, gender differences in personality, values, and emotions are not smaller, but larger, in American and European cultures, in which greater progress has been made toward gender equality. This research on gender differences in self-construals involving 950 participants from 5 nations/cultures (France, Belgium, the Netherlands, the United States, and Malaysia) illustrates how variations in social comparison processes across cultures can explain why gender differences are stronger in Western cultures. Gender differences in the self are a product of self-stereotyping, which occurs when between-gender social comparisons are made. These social comparisons are more likely, and exert a greater impact, in Western nations. Both correlational and experimental evidence supports this explanation.
Predicting player disengagement and first purchase with event-frequency based data representation

In the game industry, especially for free to play games, player retention and purchases are important issues. There have been several approaches investigated towards predicting them by players' behaviours during game sessions. However, most current methods are only available for specific games because the data representations utilised are usually game specific. This work intends to use frequency of game events as data representations to predict both players' disengagement from game and the decisions of their first purchases. This method is able to provide better generality because events exist in every game and no knowledge of any event but their frequency is needed. In addition, this event frequency based method will also be compared with a recent work by Runge et al.  in terms of disengagement prediction.
Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques

Extraction of map objects such roads, rivers and buildings from high resolution satellite imagery is an important task in many civilian and military applications. We present a semi-automatic approach for road detection that achieves high accuracy and efficiency. This method exploits the properties of road segments to develop customized operators to accurately derive the road segments. The customized operators include directional morphological enhancement, directional segmentation and thinning. We have systematically evaluated the algorithm on a variety of images from IKONOS, QuickBird, CARTOSAT-2A satellites and carefully compared it with the techniques presented in literature. The results demonstrate that the algorithm proposed is both accurate and efficient.
Saliency ratio optimization in an IPM machine with fractional-slot concentrated windings

The implementation of concentrated windings in interior permanent magnet (IPM) machines has numerous advantages over distributed windings, with the disadvantage being mainly the decrease in saliency ratio. This paper presents a proposed finite element (FE) method in which the d- and q-axis inductances (Ld and Lq) of the IPM machine with fractional-slot concentrated windings can be accurately determined. This method is used to determine Ld and Lq of various winding configurations and to determine the optimum saliency ratio for a 12-slot 14-pole model with fractional-slot concentrated windings. FE testing were carried out by the use of Flux2D.