nils-herrmann/clean_scirepeval huggingface dataset:
Abdominal obesity and metabolic syndrome: exercise as medicine?

Metabolic syndrome is defined as a cluster of at least three out of five clinical risk factors: abdominal (visceral) obesity, hypertension, elevated serum triglycerides, low serum high-density lipoprotein (HDL) and insulin resistance. It is estimated to affect over 20% of the global adult population. Abdominal (visceral) obesity is thought to be the predominant risk factor for metabolic syndrome and as predictions estimate that 50% of adults will be classified as obese by 2030 it is likely that metabolic syndrome will be a significant problem for health services and a drain on health economies.Evidence shows that regular and consistent exercise reduces abdominal obesity and results in favourable changes in body composition. It has therefore been suggested that exercise is a medicine in its own right and should be prescribed as such.



This review provides a summary of the current evidence on the pathophysiology of dysfunctional adipose tissue (adiposopathy). It describes the relationship of adiposopathy to metabolic syndrome and how exercise may mediate these processes, and evaluates current evidence on the clinical efficacy of exercise in the management of abdominal obesity. The review also discusses the type and dose of exercise needed for optimal improvements in health status in relation to the available evidence and considers the difficulty in achieving adherence to exercise programmes.



There is moderate evidence supporting the use of programmes of exercise to reverse metabolic syndrome although at present the optimal dose and type of exercise is unknown. The main challenge for health care professionals is how to motivate individuals to participate and adherence to programmes of exercise used prophylactically and as a treatment for metabolic syndrome.
The impact of concomitant fibromyalgia on visual analogue scales of pain, fatigue and function in patients with various rheumatic disorders.

To evaluate the impact of concomitant fibromyalgia on the rating of pain, fatigue, and dysfunction, in patients with various rheumatic disorders.



A cross-sectional study was carried out in a hospital-based rheumatology unit. Standard clinical and laboratory data were obtained and all patients completed questionnaires on pain, fatigue, and daily function. The rate of concomitant fibromyalgia was estimated using the 1990 American College of Rheumatology (ACR) classification criteria for fibromyalgia and the analysis concentrated on visual analogue scales (VAS).



Six hundred and eighteen visits of 383 patients with inflammatory as well as non-inflammatory rheumatic disorders were analyzed. Concomitant fibromyalgia was noted in 74 patients (23% of the cohort). Patients with rheumatic diseases and concomitant fibromyalgia had significantly higher mean VAS scores for pain, fatigue, and function (79±17, 81±18, 80±18, respectively) as compared to patients who had no features of fibromyalgia (47±28, 50±29, 44±30 respectively; all p values <0.001). The scores reported by patients with rheumatic diseases and concomitant fibromyalgia were similar to the scores obtained from patients with primary FM.



Concomitant FM is common both among patients with inflammatory and patients with non inflammatory rheumatic disorders. Concomitant FM has a remarkable impact on the severity of symptoms and, moreover, patients with concomitant FM exhibit extreme and significantly distinct levels of pain and fatigue which is as severe as that reported by patients with primary FM. It seems that fibromyalgic features dominate and become the main cause of morbidity in rheumatological patients with concomitant FM.
Multi-class Generalized Binary Search for Active Inverse Reinforcement Learning

This paper addresses the problem of learning a task from demonstration. We adopt the framework of inverse reinforcement learning, where tasks are represented in the form of a reward function. Our contribution is a novel active learning algorithm that enables the learning agent to query the expert for more informative demonstrations, thus leading to more sampleefficient learning. For this novel algorithm (Generalized Binary Search for Inverse Reinforcement Learning, or GBS-IRL), we provide a theoretical bound on sample complexity and illustrate its applicability on several different tasks. To our knowledge, GBS-IRL is the first active IRL algorithm with provable sample complexity bounds. We also discuss our method in light of other existing methods in the literature and its general applicability in multi-class classification problems. Finally, motivated by recent work on learning from demonstration in robots, we also discuss how different forms of human feedback can be integrated in a transparent manner in our learning framework.
Reduced-Order Transfer Matrices From RLC Network Descriptor Models of Electric Power Grids

This paper compares the computational performances of four model order reduction methods applied to large-scale electric power RLC networks transfer functions with many resonant peaks. Two of these methods require the state-space or descriptor model of the system, while the third requires only its frequency response data. The fourth method is proposed in this paper, being a combination of two of the previous methods. The methods were assessed for their ability to reduce eight test systems, either of the single-input single-output (SISO) or multiple-input multiple-output (MIMO) type. The results indicate that the reduced models obtained, of much smaller dimension, reproduce the dynamic behaviors of the original test systems over an ample range of frequencies with high accuracy.
Aspect-Oriented Instrumentation with GCC

We present the InterAspect instrumentation framework for GCC, a widely used compiler infrastructure. The addition of plugin support in the latest release of GCC makes it an attractive platform for runtime instrumentation, as GCC plug-ins can directly add instrumentation by transforming the compiler’s intermediate representation. Such transformations, however, require expert knowledge of GCC internals. InterAspect addresses this situation by allowing instrumentation plug-ins to be developed using the familiar vocabulary of AspectOriented Programming pointcuts, join points, and advice functions. InterAspect also supports powerful customized instrumentation, where specific information about each join point in a pointcut, as well as results of static analysis, can be used to customize the inserted instrumentation. We introduce the InterAspect API and present several examples that illustrate how it can be applied to useful runtime verification problems.
AirBag: Boosting Smartphone Resistance to Malware Infection

Recent years have experienced explosive growth of smartphone sales. Inevitably, the rise in the popularity of smartphones also makes them an attractive target for attacks. In light of these threats, current mobile platform providers have developed various server-side vetting processes to block malicious applications (“apps”). While helpful, they are still far from ideal in achieving their goals. To make matters worse, the presence of alternative (less-regulated) mobile marketplaces also opens up new attack vectors, which necessitate client-side solutions (e.g., mobile anti-virus software) to run on mobile devices. However, existing client-side solutions still exhibit limitations in their capability or deployability. In this paper, we present AirBag, a lightweight OS-level virtualization approach to enhance the popular Android platform and boost our defense capability against mobile malware infection. Assuming a trusted smartphone OS kernel and the fact that untrusted apps will be eventually installed onto users’ phones, AirBag is designed to isolate and prevent them from infecting our normal systems (e.g., corrupting the phone firmware) or stealthily leaking private information. More specifically, by dynamically creating an isolated runtime environment with its own dedicated namespace and virtualized system resources, AirBag not only allows for transparent execution of untrusted apps, but also effectively mediates their access to various system resources or phone functionalities (e.g., SMSs or phone calls). We have implemented a proof-of-concept prototype on three representative mobile devices, i.e., Google Nexus One, Nexus 7, and Samsung Galaxy S III. The evaluation results with a number of untrusted apps, including real-world mobile malware, demonstrate its practicality and effectiveness.
IMU-Aided 3D Reconstruction Based on Multiple Virtual Planes

This paper proposes a novel approach for fast 3D reconstruction of an object inside a scene by using Inertial Measurement Unit (IMU) data. A network of cameras is used to observe the scene. For each camera within the network, a virtual camera is considered by using the concept of \emph{infinite homography}. Such a virtual camera is downward and has optical axis parallel to the gravity vector. Then a set of virtual horizontal 3D planes are considered for the aim of 3D reconstruction. The intersection of these virtual parallel 3D planes with the object is computed using the concept of homography and by applying a 2D Bayesian occupancy grid for each plane. The experimental results validate both feasibility and effectiveness of the proposed method.
Mapping customers' service experience for operations improvement

The growing importance of the service sector in almost every economy in the world has created a significant amount of interest in service operations. In practice, many service sectors have sought and made use of various enhancement programs to improve their operations and performance in an attempt to hold competitive success. As most researchers recognize, service operations link with customers. The customers as participants act in the service operations system driven by the goal of sufficing his/her added values. This is one of the distinctive features of service production and consumption. In the paper, first, we propose the idea of service operations improvement by mapping objectively the service experience of customers from the view of customer journey. Second, a portraying scheme of service experience of customers based on the IDEF3 technique is proposed, and last, some implications on service operations improvement are
Neurofeedback in children with ADHD: validation and challenges.

Neurofeedback (NF), a type of neurobehavioral training, has gained increasing attention in recent years, especially concerning the treatment of children with ADHD. Promising results have emerged from recent randomized controlled studies, and thus, NF is on its way to becoming a valuable addition to the multimodal treatment of ADHD. In this review, we summarize the randomized controlled trials in children with ADHD that have been published within the last 5 years and discuss issues such as the efficacy and specificity of effects, treatment fidelity and problems inherent in placebo-controlled trials of NF. Directions for future NF research are outlined, which should further address specificity and help to determine moderators and mediators to optimize and individualize NF training. Furthermore, we describe methodological (tomographic NF) and technical ('tele-NF') developments that may also contribute to further improvements in treatment outcome.
Frequency diverse array radars

This paper presents a generalized structure for a frequency diverse array radar. In its simplest form, the frequency diverse array applies a linear phase progression across the aperture. This linear phase progression induces an electronic beam scan, as in a conventional phased array. When an additional linear frequency shift is applied across the elements, a new term is generated which results in a scan angle that varies with range in the far-field. This provides more flexible beam scan options, as well as providing resistance to point interference such as multipath. More general implementations provide greater degrees of freedom for space-time-frequency-phase-polarization control, permitting novel concepts for simultaneous multi-mission operation, such as performing synthetic aperture radar and ground moving target indication at the same time.
Bayesian Optimization with Inequality Constraints

Bayesian optimization is a powerful framework for minimizing expensive objective functions while using very few function evaluations. It has been successfully applied to a variety of problems, including hyperparameter tuning and experimental design. However, this framework has not been extended to the inequality-constrained optimization setting, particularly the setting in which evaluating feasibility is just as expensive as evaluating the objective. Here we present constrained Bayesian optimization, which places a prior distribution on both the objective and the constraint functions. We evaluate our method on simulated and real data, demonstrating that constrained Bayesian optimization can quickly find optimal and feasible points, even when small feasible regions cause standard methods to fail.
Automated Security Analysis of Dynamic Web Applications through Symbolic Code Execution

The automatic identification of security vulnerabilities is a critical issue in the development of web-based applications. We present a methodology and tool for vulnerability identification based on symbolic code execution exploiting Static Taint Analysis to improve the efficiency of the analysis. The tool targets PHP web applications, and demonstrates the effectiveness of our approach in identifying cross-site scripting and SQL injection vulnerabilities on both NIST synthetic benchmarks and real world applications. It proves to be faster and more effective than its main competitors, both open source and commercial.
The inverse kinematics solutions of a 7 DOF robotic arm using Fuzzy Logic

This paper focuses on modeling resolving and simulations of the inverse kinematics of an anthropomorphic redundant robotic structure with seven degrees of freedom and a workspace similar to human arm. Also the kinematical model and the kinematics equations of the robotic arm are presented. A method of resolving the redundancy of seven degrees of freedom robotic arm is presented using Fuzzy Logic toolbox from MATLAB®.
A comparison of the anthropometric, strength, endurance and flexibility characteristics of female elite and recreational climbers and non-climbers.

There is limited information on the anthropometry, strength, endurance and flexibility of female rock climbers. The aim of this study was to compare these characteristics in three groups of females: Group 1 comprised 10 elite climbers aged 31.3 +/- 5.0 years (mean +/- s) who had led to a standard of 'hard very severe'; Group 2 consisted of 10 recreational climbers aged 24.1 +/- 4.0 years who had led to a standard of 'severe'; and Group 3 comprised 10 physically active individuals aged 28.5 +/- 5.0 years who had not previously rock-climbed. The tests included finger strength (grip strength, finger strength measured on climbing-specific apparatus), flexibility, bent arm hang and pull-ups. Regression procedures (analysis of covariance) were used to examine the influence of body mass, leg length, height and age. For finger strength, the elite climbers recorded significantly higher values (P < 0.05) than the recreational climbers and non-climbers (four fingers, right hand: elite 321 +/- 18 N, recreational 251 +/- 14 N, non-climbers 256 +/- 15 N; four fingers, left hand: elite 307 +/- 14 N, recreational 248 +/- 12 N, non-climbers 243 +/- 11 N). For grip strength of the right hand, the elite climbers recorded significantly higher values than the recreational climbers only (elite 338 +/- 12 N, recreational 289 +/- 10 N, non-climbers 307 +/- 11 N). The results suggest that elite climbers have greater finger strength than recreational climbers and non-climbers.
An Error Detection and Correction Framework for Connectomics

We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image segmentation more generally. Both tasks take as input the raw image and a binary mask representing a candidate object. For the error detection task, the desired output is a map of split and merge errors in the object. For the error correction task, the desired output is the true object. We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object. We train multiscale 3D convolutional networks to perform both tasks. We find that the error-detecting net can achieve high accuracy. The accuracy of the error-correcting net is enhanced if its input object mask is “advice” (union of erroneous objects) from the error-detecting net.
Spontaneous evolution of linguistic structure-an iterated learning model of the emergence of regularity and irregularity

A computationally implemented model of the transmission of linguistic behavior over time is presented. In this model [the iterated learning model (ILM)], there is no biological evolution, natural selection, nor any measurement of the success of the agents at communicating (except for results-gathering purposes). Nevertheless, counter to intuition, significant evolution of linguistic behavior is observed. From an initially unstructured communication system (a protolanguage), a fully compositional syntactic meaning-string mapping emerges. Furthermore, given a nonuniform frequency distribution over a meaning space and a production mechanism that prefers short strings, a realistic distribution of string lengths and patterns of stable irregularity emerges, suggesting that the ILM is a good model for the evolution of some of the fundamental features of human language.
The Dafny Integrated Development Environment

In recent years, program verifiers and interactive theorem provers have become more powerful and more suitable for verifying large programs or proofs. This has demonstrated the need for improving the user experience of these tools to increase productivity and to make them more accessible to nonexperts. This paper presents an integrated development environment for Dafny—a programming language, verifier, and proof assistant—that addresses issues present in most state-of-the-art verifiers: low responsiveness and lack of support for understanding non-obvious verification failures. The paper demonstrates several new features that move the state-of-the-art closer towards a verification environment that can provide verification feedback as the user types and can present more helpful information about the program or failed verifications in a demand-driven and unobtrusive way.
Cebocephaly, alobar holoprosencephaly, spina bifida, and sirenomelia in a stillbirth.

Cebocephaly and sirenomelia are uncommon birth defects. Their association is extremely rare; however, the presence of spina bifida with both conditions is not unexpected. We report on a female still-birth with cebocephaly, alobar holoprosencephaly, cleft palate, lumbar spina bifida, sirenomelia, a single umbilical artery, and a 46,XX karyotype, but without maternal diabetes mellitus. Our case adds to the examples of overlapping cephalic and caudal defects, possibly related to vulnerability of the midline developmental field or axial mesodermal dysplasia spectrum.
When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging

This study presents a novel approach to the problem of system portability across different domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on WordNet. The paper explores the challenges of system portability across domains and text genres (movie reviews, news, blogs, and product reviews), highlights the factors affecting system performance on out-of-domain and smallset in-domain data, and presents a new system consisting of the ensemble of two classifiers with precision-based vote weighting, that provides significant gains in accuracy and recall over the corpus-based classifier and the lexicon-based system taken individually.
Vision-based Deep Reinforcement Learning

Recently, Google Deepmind showcased how Deep learning can be used in conjunction with existing Reinforcement Learning (RL) techniques to play Atari games[11], beat a world-class player [14] in the game of Go and solve complicated riddles . Deep learning has been shown to be successful in extracting useful, nonlinear features from high-dimensional media such as images, text, video and audio . For control tasks like playing games, researchers have traditionally used handcrafted-features which require a lot of human effort. Convolutional neural networks (CNN) can be used to extract useful visual features from the highdimensional input, such as a video game screen, to learn near-optimal value function in such control tasks. We chose the Arcade Learning environment as our testbed to examine how deep learning can be applied to a reinforcement learning setting and analyze performance of various modern algorithms. The learning is performed end-to-end with no game knowledge included in the architecture or during training. We also show how the architecture of the deep network used, can be modified to achieve superior performances and compare performances on the task of playing a complicated game Seaquest.
Semantic Annotation of Data Processing Pipelines in Scientific Publications

Data processing pipelines are a core object of interest for data scientist and practitioners operating in a variety of data-related application domains. To effectively capitalise on the experience gained in the creation and adoption of such pipelines, the need arises for mechanisms able to capture knowledge about datasets of interest, data processing methods designed to achieve a given goal, and the performance achieved when applying such methods to the considered datasets. However, due to its distributed and often unstructured nature, this knowledge is not easily accessible. In this paper, we use (scientific) publications as source of knowledge about Data Processing Pipelines. We describe a method designed to classify sentences according to the nature of the contained information (i.e. scientific objective, dataset, method, software, result), and to extract relevant named entities. The extracted information is then semantically annotated and published as linked data in open knowledge repositories according to the DMS ontology for data processing metadata. To demonstrate the effectiveness and performance of our approach, we present the results of a quantitative and qualitative analysis performed on four different conference series.