nils-herrmann/clean_scirepeval huggingface dataset:
Smart wearable body sensors for patient self-assessment and monitoring

Innovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited. The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors.



We reviewed the literature for connected device, sensor, trackers, telemonitoring, wireless technology and real time home tracking devices and their application for clinicians.



Smart wearable sensors are effective and reliable for preventative methods in many different facets of medicine such as, cardiopulmonary, vascular, endocrine, neurological function and rehabilitation medicine. These sensors have also been shown to be accurate and useful for perioperative monitoring and rehabilitation medicine.



Although these devices have been shown to be accurate and have clinical utility, they continue to be underutilized in the healthcare industry. Incorporating smart wearable sensors into routine care of patients could augment physician-patient relationships, increase the autonomy and involvement of patients in regards to their healthcare and will provide for novel remote monitoring techniques which will revolutionize healthcare management and spending.
Joint Training for Pivot-based Neural Machine Translation

While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs. Although this problem can be alleviated by exploiting a pivot language to bridge the source and target languages, the source-to-pivot and pivot-to-target translation models are usually independently trained. In this work, we introduce a joint training algorithm for pivot-based neural machine translation. We propose three methods to connect the two models and enable them to interact with each other during training. Experiments on Europarl and WMT corpora show that joint training of source-to-pivot and pivot-to-target models leads to significant improvements over independent training across various languages.
Deep networks with probabilistic gates

We investigate learning to probabilistically bypass computations in a network architecture. Our approach is motivated by AIG [44], where layers are conditionally executed depending on their inputs, and the network is trained against a target bypass rate using a per-layer loss. We propose a per-batch loss function, and describe strategies for handling probabilistic bypass during inference as well as training. Per-batch loss allows the network additional flexibility. In particular, a form of mode collapse becomes plausible, where some layers are nearly always bypassed and some almost never; such a configuration is strongly discouraged by AIG’s per-layer loss. We explore several inference-time strategies, including the natural MAP approach. With data-dependent bypass, we demonstrate improved performance over AIG. With data-independent bypass, as in stochastic depth [18], we observe mode collapse and effectively prune layers. We demonstrate our techniques on ResNet-50 and ResNet-101 [11] for ImageNet , where our techniques produce improved accuracy (.15–.41% in precision@1) with substantially less computation (bypassing 25–40% of the layers).
Dynamic analysis of Clavel's delta parallel robot

Some iterative matrix relations for the geometric, kinematic and dynamic analysis of a Delta parallel robot are established in this paper. The prototype of this manipulator is a three degree of freedom spatial mechanism, which consists of a system of parallel chains. Supposing that the position and the translation motion of the platform are known, an inverse dynamic problem is solved using the virtual powers method. Finally, some recursive matrix relations and some graphs for the moments and the powers of the three active couples are determined.
Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks

Recent advances in convolutional neural networks have shown promising results in 3D shape completion. But due to GPU memory limitations, these methods can only produce low-resolution outputs. To inpaint 3D models with semantic plausibility and contextual details, we introduce a hybrid framework that combines a 3D Encoder-Decoder Generative Adversarial Network (3D-ED-GAN) and a Longterm Recurrent Convolutional Network (LRCN). The 3DED- GAN is a 3D convolutional neural network trained with a generative adversarial paradigm to fill missing 3D data in low-resolution. LRCN adopts a recurrent neural network architecture to minimize GPU memory usage and incorporates an Encoder-Decoder pair into a Long Shortterm Memory Network. By handling the 3D model as a sequence of 2D slices, LRCN transforms a coarse 3D shape into a more complete and higher resolution volume. While 3D-ED-GAN captures global contextual structure of the 3D shape, LRCN localizes the fine-grained details. Experimental results on both real-world and synthetic data show reconstructions from corrupted models result in complete and high-resolution 3D objects.
State of the Art of Fingerprint Indexing Algorithms

Due to the large size that fingerprint databases generally have, the reduction of the search space is indispensable. In the resolution of this problem, indexing algorithms have a fundamental role. In the literature, there are several proposals that make use of different features to characterize fingerprints. In addition, a wide variety of recovery methods are reported. This paper concisely describes the indexing algorithms that have reported better results so far and makes a comparison between these, based on experiments in well known databases. Finally, a classification of the indexing algorithms is proposed, based on some general characteristics.
Learning Answer-Entailing Structures for Machine Comprehension

Understanding open-domain text is one of the primary challenges in NLP. Machine comprehension evaluates the system’s ability to understand text through a series of question-answering tasks on short pieces of text such that the correct answer can be found only in the given text. For this task, we posit that there is a hidden (latent) structure that explains the relation between the question, correct answer, and text. We call this the answer-entailing structure; given the structure, the correctness of the answer is evident. Since the structure is latent, it must be inferred. We present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs), and uses what it learns to answer machine comprehension questions on novel texts. We extend this framework to incorporate multi-task learning on the different subtasks that are required to perform machine comprehension. Evaluation on a publicly available dataset shows that our framework outperforms various IR and neuralnetwork baselines, achieving an overall accuracy of 67.8% (vs. 59.9%, the best previously-published result.
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection

Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturallyoccurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5% F0.5 score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 F1 score, indicating that our model generates mostly human-like instances.
Hungry like the wolf : A word-pattern analysis of the language of psychopaths

Two text analysis tools were used to examine the crime narratives of 14 psychopathic and 38 non-psychopathic homicide offenders. Psychopathy was determined using the Psychopathy Checklist-Revised (PCL-R). The Wmatrix linguistic analysis tool (Rayson, 2008) was used to examine parts of speech and semantic content while the Dictionary of Affect and Language (DAL) tool (Whissell & Dewson, 1986) was used to examine the emotional characteristics of the narratives.
Key Dimensions for Cloud Data Governance

Forward thinking organizations recognize that data management solutions on their own are becoming very expensive and failing cope with reality. They need to solve the data problem in a different way, through the implementation of an effective Data Governance. Data Governance needs to take a policy-centric approach to data models, data quality standards, data security and lifecycle management, and processes for defining, implementing and enforcing these policies. Until recently, data governance has largely been informal, in siloes around specific enterprise repositories, lacking structure and the wider support of the organization. In many government departments, data governance exists as a set of very ambiguous and generic regulations. The area of data governance is still under-researched, despite its importance. With the emergence of Cloud computing, and its increased adoption by businesses, public organisations and governments, as much as the potential gains from adopting the technology, businesses face new and more complex challenges. Such emphasize the need for effective data governance strategy and programs, which can ensure best returns for cloud adoption. This paper is one of very few published research, which tackles this subject domain, and attempts to lay its foundations.
Internet of Things and LoRa™ low-power wide-area networks challenges

Nowadays there is a lot of effort on the study, analysis and finding of new solutions related to high density sensor networks used as part of the IoT (Internet of Things) concept. LoRa (Long Range) is a modulation technique that enables the long-range transfer of information with a low transfer rate. This paper presents a review of the challenges and the obstacles of IoT concept with emphasis on the LoRa technology. A LoRaWAN network (Long Range Network Protocol) is of the Low Power Wide Area Network (LPWAN) type and encompasses battery powered devices that ensure bidirectional communication. The main contribution of the paper is the evaluation of the LoRa technology considering the requirements of IoT. In conclusion LoRa can be considered a suitable candidate in addressing the IoT challenges.
Instructional quality of Massive Open Online Courses (MOOCs)

We present an analysis of instructional design quality of 76 randomly selected Massive Open Online Courses (MOOCs). The quality of MOOCs was determined from first principles of instruction, using a course survey instrument. Two types of MOOCs (xMOOCs and cMOOCs) were analysed and their instructional design quality was assessed and compared. We found that the majority of MOOCs scored poorly on most instructional design principles. However, most MOOCs scored highly on organisation and presentation of course material. The results indicate that although most MOOCs are well-packaged, their instructional design quality is low. We outline implications for practice and ideas for future research.
Practical Gauss-Newton Optimisation for Deep Learning

We present an efficient block-diagonal approximation to the Gauss-Newton matrix for feedforward neural networks. Our resulting algorithm is competitive against state-of-the-art first-order optimisation methods, with sometimes significant improvement in optimisation performance. Unlike first-order methods, for which hyperparameter tuning of the optimisation parameters is often a laborious process, our approach can provide good performance even when used with default settings. A side result of our work is that for piecewise linear transfer functions, the network objective function can have no differentiable local maxima, which may partially explain why such transfer functions facilitate effective optimisation.
INTEGRATING MODELS OF DIFFUSION OF INNOVATIONS : A Conceptual Framework

■ Abstract This chapter provides a conceptual framework for integrating the array of variables defined in diffusion research to explicate their influence on an actor’s decision to adopt an innovation. The framework groups the variables into three major components. The first component includes characteristics of the innovation itself, within which two sets of variables are defined concerning public versus private consequences and benefits versus costs of adoption. A second component involves the characteristics of innovators (actors) that influence the probability of adoption of an innovation. Within this component six sets of variables concern societal entity of innovators (either people, organizations, states, etc.), familiarity with the innovation, status characteristics, socioeconomic characteristics, position in social networks, and personal qualities. The third component involves characteristics of the environmental context that modulate diffusion via structural characteristics of the modern world. These latter characteristics incorporate four sets of variables: geographical settings, societal culture, political conditions, and global uniformity. The concluding analysis highlights the need in diffusion research to incorporate more fully ( a) the interactive character of diffusion variables, ( b) the gating function of diffusion variables, and ( c) effects of an actor’s characteristics on the temporal rate of diffusion.
Do We Have a Free Will ?

I have taken an experimental approach to this question. Freely voluntary acts are pre ceded by a specific electrical change in the brain (the ‘readiness potential’, RP) that begins 550 ms before the act. Human subjects became aware of intention to act 350–400 ms after RP starts, but 200 ms. before the motor act. The volitional process is therefore initiated unconsciously. But the conscious function could still control the outcome; it can veto the act. Free will is therefore not excluded. These findings put constraints on views of how free will may operate; it would not initiate a voluntary act but it could control performance of the act. The findings also affect views of guilt and responsibility. But the deeper question still remains: Are freely voluntary acts subject to macro deterministic laws or can they appear without such constraints, non-determined by natural laws and ‘truly free’? I shall present an experimentalist view about these fundamental philosophical opposites.
How Organizational Identity influences Information Technology (IT) Outsourcing Success

Corporate Information Technology (IT) functions are under increasing pressure to succeed in their IT outsourcing (ITO) arrangements. Studies of ITO success have in the past mainly explored its operational and financial aspects. At the same time there is a dearth of research on broader organizational antecedents and outcomes of ITO. This study examines the effect of organizational identity on outsourcing success. Specifically, we ask: does the strength of an organization’s identityi.e. how unique it views its role in relation to other corporate functionsinfluence ITO success? We conduct an empirical study among 312 IT leaders engaged in outsourcing. We find that organizational identity strength mediates the effect of one pivotal outsourcing antecedent – effective knowledge sharing – on ITO Success. We thereby expose the role of identity strength as a potential determinant of ITO success and subsequently surmise that it’s likely to have a material impact on the firm.
Concept of fuzzy planar graphs

Fuzzy graph is now a very important research area due to its wide application. Fuzzy multigraph and fuzzy planar graphs are two subclasses of fuzzy graph theory. In this paper, we define both of these graphs and studied a lot of properties. A very close association of fuzzy planar graph is fuzzy dual graph. This is also defined and studied several properties. The relation between fuzzy planar graph and fuzzy dual graph is also established.
Quad-Tilting Rotor Convertible MAV: Modeling and Real-Time Hover Flight Control

This paper describes the modeling, control and hardware implementation of an experimental tilt-rotor aircraft. This vehicle combines the high-speed cruise capabilities of a conventional airplane with the hovering capabilities of a helicopter by tilting their four rotors. Changing between cruise and hover flight modes in midair is referred to transition. Dynamic model of the vehicle is derived both for vertical and horizontal flight modes using Newtonian approach. Two nonlinear control strategies are presented and evaluated at simulation level to control, the vertical and horizontal flight dynamics of the vehicle in the longitudinal plane. An experimental prototype named Quad-plane was developed to perform the vertical flight. A low-cost DSP-based Embedded Flight Control System (EFCS) was deThis work was partially supported by the Institute for Science & Technology of Mexico City (ICyTDF) and the French National Centre for Scientific Research (CNRS). G. R. Flores (B) · J. Escareño · R. Lozano Heudiasyc Laboratory, University of Technology of Compiègne, CNRS 6599, 60205 Compiègne, France e-mail: gfloresc@hds.utc.fr S. Salazar French-Mexican Laboratory on Computer Science and Control, CINVESTAV, Mexico City, Mexico signed and built to achieve autonomous attitudestabilized flight.
From mindless to mindful practice--cognitive bias and clinical decision making.

n engl j med 368;26 nejm.org june 27, 2013 2445 correct diagnoses as often as we think: the diagnostic failure rate is estimated to be 10 to 15%. The rate is highest among specialties in which patients are diagnostically undifferentiated, such as emergency medicine, family medicine, and internal medicine. Error in the visual specialties, such as radiology and pathology, is considerably lower, probably around 2%.1 Diagnostic error has multiple causes, but principal among them are cognitive errors. Usually, it’s not a lack of knowledge that leads to failure, but problems with the clinician’s thinking. Esoteric diagnoses are occasionally missed, but common illnesses are commonly misdiagnosed. For example, physicians know the pathophysiology of pulmonary embolus in excruciating detail, yet because its signs and symptoms are notoriously variable and overlap with those of numerous other diseases, this important diagnosis was missed a staggering 55% of the time in a series of fatal cases.2 Over the past 40 years, work by cognitive psychologists and others has pointed to the human mind’s vulnerability to cognitive biases, logical fallacies, false assumptions, and other reasoning failures. It seems that much of our everyday thinking is f lawed, and clinicians are not immune to the problem (see box). More than 100 biases affecting clinical decision making have been described, and many medical disciplines now acknowledge their pervasive influence on our thinking. Cognitive failures are best understood in the context of how our brains manage and process information. The two principal modes, automatic and controlled, are colloquially referred to as “intuitive” and “analytic”; psychologists know them as Type 1 and Type 2 processes. Various conceptualizations of the reasoning process have been proposed, but most can be incorporated into this dual-process system. This system is more than a model: it is accepted that the two processes involve different cortical mechanisms with associated neurophysiologic and neuroanatomical From Mindless to Mindful Practice — Cognitive Bias and Clinical Decision Making
An Analysis of Cyber Security Attack Taxonomies

Taxonomies have been developed as a mechanism for cyber attack categorisation. However, when one considers the recent and rapid evolution of attacker techniques and targets, the applicability and effectiveness of these taxonomies should be questioned. This paper applies two approaches to the evaluation of seven taxonomies. The first employs a criteria set, derived through analysis of existing works in which critical components to the creation of taxonomies are defined. The second applies historical attack data to each taxonomy under review, more specifically, attacks in which industrial control systems have been targeted. This combined approach allows for a more in-depth understanding of existing taxonomies to be developed, from both a theoretical and practical perspective.
Challenges with Variational Autoencoders for Text

We study variational autoencoders for text data to build a generative model that can be used to conditionally generate text. We introduce a mutual information criterion to encourage the model to put semantic information into the latent representation, and compare its efficacy with other tricks explored in literature such as KL divergence cost annealing and word dropout. We compare the log-likelihood lowerbounds on held-out data using variational autoencoders with the log-likelihoods on an unconditional language model. We notice our models quickly learn to exploit the grammatical redundancies in our dataset, but it is more challenging to encode semantic information in the latent representation.
PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds.

Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.
The Toronto Mindfulness Scale: development and validation.

In this study, the authors both developed and validated a self-report mindfulness measure, the Toronto Mindfulness Scale (TMS). In Study 1, participants were individuals with and without meditation experience. Results showed good internal consistency and two factors, Curiosity and Decentering. Most of the expected relationships with other constructs were as expected. The TMS scores increased with increasing mindfulness meditation experience. In Study 2, criterion and incremental validity of the TMS were investigated on a group of individuals participating in 8-week mindfulness-based stress reduction programs. Results showed that TMS scores increased following treatment, and Decentering scores predicted improvements in clinical outcome. Thus, the TMS is a promising measure of the mindfulness state with good psychometric properties and predictive of treatment outcome.