nils-herrmann/clean_scirepeval huggingface dataset:
Clinical practice guidelines for diagnosis of autism spectrum disorder in adults and children in the UK: a narrative review

Research suggests that diagnostic procedures for Autism Spectrum Disorder are not consistent across practice and that diagnostic rates can be affected by contextual and social drivers. The purpose of this review was to consider how the content of clinical practice guidelines shapes diagnoses of Autism Spectrum Disorder in the UK; and investigate where, within those guidelines, social factors and influences are considered.



We electronically searched multiple databases (NICE Evidence Base; TRIP; Social Policy and Practice; US National Guidelines Clearinghouse; HMIC; The Cochrane Library; Embase; Global health; Ovid; PsychARTICLES; PsychINFO) and relevant web sources (government, professional and regional NHS websites) for clinical practice guidelines. We extracted details of key diagnostic elements such as assessment process and diagnostic tools. A qualitative narrative analysis was conducted to identify social factors and influences.



Twenty-one documents were found and analysed. Guidelines varied in recommendations for use of diagnostic tools and assessment procedures. Although multidisciplinary assessment was identified as the 'ideal' assessment, some guidelines suggested in practice one experienced healthcare professional was sufficient. Social factors in operational, interactional and contextual areas added complexity to guidelines but there were few concrete recommendations as to how these factors should be operationalized for best diagnostic outcomes.



Although individual guidelines appeared to present a coherent and systematic assessment process, they varied enough in their recommendations to make the choices available to healthcare professionals particularly complex and confusing. We recommend a more explicit acknowledgement of social factors in clinical practice guidelines with advice about how they should be managed and operationalised to enable more consistency of practice and transparency for those coming for diagnosis.
Blockstack: Design and Implementation of a Global Naming System with Blockchains

Cryptocurrency blockchains like Bitcoin and Namecoin and their respective P2P networks have seen significant adoption in the past few years, and show promise as naming systems with no trusted parties. Users can register human-readable names and securely associate data with them; only the owner of a particular private-key can write or update the name/value pair. In theory, many decentralized systems can be built using these cryptocurrency networks, such as new, decentralized versions of DNS or PKI. As the technology is relatively new and evolving rapidly, however, little production data or experience is available to guide design tradeoffs. In this paper, we describe our experience operating a large, real-world deployment of a decentralized PKI service built on top of the Namecoin blockchain. We present various challenges (network reliability, throughput, security issues) that we needed to overcome while registering/updating over 33,000 entries on the blockchain which involved over 200,000 transactions on the Namecoin network. Further, we discuss how our experience informed the design of a new blockchain-based naming system, Blockstack. We detail why we changed from Namecoin to the Bitcoin network for the new system, as well as operational lessons from this migration. Blockstack is released as open source and currently powers a production PKI system for 40,000 users.
DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model

The Android platform is becoming increasingly popular and various organizations have developed a variety of applications (App) to cater to market trends. Due to the characteristics of the Android platform, such as supporting the unofficial App stores, open source policy and the great tolerance for App verification, it is inevitable that it faces serious problems of malicious software intrusion. In order to protect the users from the serious damages caused by Android malware, we propose a low-cost and high-efficient method to extract permissions, sensitive APIs, monitoring system events and permission-rate as key features, and employ the ensemble Rotation Forest (RF) to construct a model to detect whether an Android App is malicious or not. Specifically, a dataset containing 2,130 samples is used to verify the performance of the proposed method. The experimental results show that the proposed method achieves an high accuracy of 88.26% with 88.40% sensitivity at the precision of 88.16%. To further evaluate the performance of the proposed model, we also compare it with the state-of-the-art Support Vector Machine (SVM) model under the same experimental conditions, and the comparison results demonstrate that the proposed method improves the accuracy by 3.33% compared to SVM. The experimental results show that the proposed model is extremely promising and could provide a cost-effective alternative for Android malware detection.
Learning Conversational Systems that Interleave Task and Non-Task Content

Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems’ capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave nontask content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.
Nonintrusive Load Monitoring and Diagnostics in Power Systems

This paper describes a transient event classification scheme, system identification techniques, and implementation for use in nonintrusive load monitoring. Together, these techniques form a system that can determine the operating schedule and find parameters of physical models of loads that are connected to an AC or DC power distribution system. The monitoring system requires only off-the-shelf hardware and recognizes individual transients by disaggregating the signal from a minimal number of sensors that are installed at a central location in the distribution system. Implementation details and field tests for AC and DC systems are presented.
A 4-bit CMOS Phase Shifter Using Distributed Active Switches

This paper presents a novel 4-bit phase shifter using distributed active switches in 0.18-mum RF CMOS technology. The relative phase shift, which varies from 0deg to 360deg in steps of 22.5deg, is achieved with a 3-bit distributed phase shifter and a 180deg high-pass/low-pass phase shifter. The distributed phase shifter is implemented using distributed active switches that consist of a periodic placement of series inductors and cascode transistors, thereby obtaining linear phase shift versus frequency with a digital control. The design guideline of the distributed phase shifter is presented. The 4-bit phase shifter achieves 3.5 plusmn 0.5 dB of gain, with an rms phase error of 2.6deg at a center frequency of 12.1 GHz. The input and output return losses are less than -15 dB at all conditions. The chip size is 1880 mum times 915 mum including the probing pads.
ZnO nanowire UV photodetectors with high internal gain.

ZnO nanowire (NW) visible-blind UV photodetectors with internal photoconductive gain as high as G approximately 108 have been fabricated and characterized. The photoconduction mechanism in these devices has been elucidated by means of time-resolved measurements spanning a wide temporal domain, from 10-9 to 102 s, revealing the coexistence of fast (tau approximately 20 ns) and slow (tau approximately 10 s) components of the carrier relaxation dynamics. The extremely high photoconductive gain is attributed to the presence of oxygen-related hole-trap states at the NW surface, which prevents charge-carrier recombination and prolongs the photocarrier lifetime, as evidenced by the sensitivity of the photocurrrent to ambient conditions. Surprisingly, this mechanism appears to be effective even at the shortest time scale investigated of t < 1 ns. Despite the slow relaxation time, the extremely high internal gain of ZnO NW photodetectors results in gain-bandwidth products (GB) higher than approximately 10 GHz. The high gain and low power consumption of NW photodetectors promise a new generation of phototransistors for applications such as sensing, imaging, and intrachip optical interconnects.
High Gain Wide-Band U-Shaped Patch Antennas With Modified Ground Planes

A high gain wideband U-shaped patch antenna with two equal arms on poly tetra fluoro ethylene (PTFE) substrate is presented. An inverted U-shaped slot is introduced on the circular or square shaped ground plane just under the U-shaped patch. In this communication the effect of size and shape of the ground plane on impedance bandwidth is studied. Maximum impedance bandwidth of 86.79% (4.5–11.4 GHz) is obtained with circular shaped ground plane with diameter 36 mm. The highest gain achieved is 4.1 dBi. The simulated results are confirmed experimentally. The proposed antenna is simple in structure compared to the regular stacked or coplanar parasitic patch antennas. It is highly suitable for wireless communications.
Space Telerobotics : Unique Challenges to Human – Robot Collaboration in Space

In this chapter, we survey the current state of the art in space telerobots. We begin by defining relevant terms and describing applications. We then examine the design issues for space telerobotics, including common requirements, operational constraints, and design elements. A discussion follows of the reasons space telerobotics presents unique challenges beyond terrestrial systems. We then present case studies of several different space telerobots, examining key aspects of design and human–robot interaction. Next, we describe telerobots and concepts of operations for future space exploration missions. Finally, we discuss the various ways in which space telerobots can be evaluated in order to characterize and improve performance.
Numba: a LLVM-based Python JIT compiler

Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1].
Model Compression with Generative Adversarial Networks

More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPUor memory-constrained devices. Model compression (also known as distillation) alleviates this burden by training a less expensive student model to mimic the expensive teacher model while maintaining most of the original accuracy. However, when fresh data is unavailable for the compression task, the teacher’s training data is typically reused, leading to suboptimal compression. In this work, we propose to augment the compression dataset with synthetic data from a generative adversarial network (GAN) designed to approximate the training data distribution. Our GAN-assisted model compression (GAN-MC) significantly improves student accuracy for expensive models such as large random forests and deep neural networks on both tabular and image datasets. Building on these results, we propose a comprehensive metric—the Compression Score—to evaluate the quality of synthetic datasets based on their induced model compression performance. The Compression Score captures both data diversity and discriminability, and we illustrate its benefits over the popular Inception Score in the context of image classification.
Ten Mistakes To Avoid When Injecting Botulinum Toxin.

Injection of botulinum toxin is currently the most common cosmetic procedure in the United States, and in recent years it has become-together with dermal fillers-the mainstay of therapy for the prevention and treatment of facial aging. However, in some cases the treatment may lead to a somewhat unnatural appearance, usually caused by loss of facial expression or other telltale signs. In the present article, we review the 10 mistakes that should be avoided when injecting botulinum toxin. We also reflect on how treatment with botulinum toxin influences us through our facial expressions, both in terms of how we feel and what others perceive.
Narrative Visualization: Sharing Insights into Complex Data

This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation.
LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK

In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves stateof-art results in terms of the false positive rate at a 95% recall rate on standard benchmark datasets.  Corresponding author
A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm. & 2011 Elsevier B.V. All rights reserved.
Early detection of radiographic knee osteoarthritis using computer-aided analysis.

To determine whether computer-based analysis can detect features predictive of osteoarthritis (OA) development in radiographically normal knees.



A systematic computer-aided image analysis method weighted neighbor distances using a compound hierarchy of algorithms representing morphology (WND-CHARM) was used to analyze pairs of weight-bearing knee X-rays. Initial X-rays were all scored as normal Kellgren-Lawrence (KL) grade 0, and on follow-up approximately 20 years later either developed OA (defined as KL grade=2) or remained normal.



The computer-aided method predicted whether a knee would change from KL grade 0 to grade 3 with 72% accuracy (P<0.00001), and to grade 2 with 62% accuracy (P<0.01). Although a large part of the predictive signal comes from the image tiles that contained the joint, the region adjacent to the tibial spines provided the strongest predictive signal.



Radiographic features detectable using a computer-aided image analysis method can predict the future development of radiographic knee OA.
NPCs as People, Too: The Extreme AI Personality Engine

PK Dick once asked " Do Androids Dream of Electric Sheep? " In video games, a similar question could be asked of non-player characters: Do NPCs have dreams? Can they live and change as humans do? Can NPCs have personalities , and can these develop through interactions with players , other NPCs, and the world around them? Despite advances in personality AI for games, most NPCs are still undeveloped and undeveloping, reacting with flat affect and predictable routines that make them far less than human— in fact, they become little more than bits of the scenery that give out parcels of information. This need not be the case. Extreme AI, a psychology-based personality engine, creates adaptive NPC personalities. Originally developed as part of the thesis " NPCs as People: Using Databases and Behaviour Trees to Give Non-Player Characters Personality, " Extreme AI is now a fully functioning personality engine using all thirty facets of the Five Factor model of personality and an AI system that is live throughout gameplay. This paper discusses the research leading to Extreme AI; develops the ideas found in that thesis; discusses the development of other personality engines; and provides examples of Extreme AI's use in two game demos.
Exploring actor-object relationships for query-focused multi-document summarization

Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users’ preferences. We note that, different users can generate somewhat different summaries on the basis of the same source data and query. This paper presents our study on how to exploit the information regards how users summarized their texts. Models of different users can be used either separately, or in an ensemble-like fashion. Machine learning methods are explored in the construction of the individual models. However, we explore yet another hypothesis. We believe that the sentences selected into the summary should be coherent and supplement each other in their meaning. One method to model this relationship between sentences is by detecting actor–object relationship (AOR). The sentences that satisfy this relationship have their importance value enhanced. This paper combines ensemble summarizing system and AOR to generate summaries. We have evaluated this method on DUC 2006 and DUC 2007 using ROUGE measure. Experimental results show the supervised method that exploits the ensemble summarizing system combined with AOR outperforms previous models when considering performance in query-based multidocument summarization tasks. Keyword User-based summarization · Actor–object relationship · Multi-document summarization · Ensemble summarizing system · Training data construction Communicated by V. Loia. M. Valizadeh (B) · P. Brazdil LIAAD INESC Tec, University of Porto, Porto, Portugal e-mail: valizadehmr@gmail.com P. Brazdil FEP, University of Porto, Porto, Portugal e-mail:
A Lightened CNN for Deep Face Representation

Convolution neural network (CNN) has significantly pushed forward the development of face recognition techniques. To achieve ultimate accuracy, CNN models tend to be deeper or multiple local facial patch ensemble, which result in a waste of time and space. To alleviate this issue, this paper studies a lightened CNN framework to learn a compact embedding for face representation. First, we introduce the concept of maxout in the fully connected layer to the convolution layer, which leads to a new activation function, named Max-Feature-Map (MFM). Compared with widely used ReLU, MFM can simultaneously capture compact representation and competitive information. Then, one shallow CNN model is constructed by 4 convolution layers and totally contains about 4M parameters; and the other is constructed by reducing the kernel size of convolution layers and adding Network in Network (NIN) layers between convolution layers based on the previous one. These models are trained on the CASIA-WebFace dataset and evaluated on the LFW and YTF datasets. Experimental results show that the proposed models achieve state-of-the-art results. At the same time, a reduction of computational cost is reached by over 9 times in comparison with the released VGG model.
Omnidirectional Circularly Polarized Antenna Utilizing Zeroth-Order Resonance of Epsilon Negative Transmission Line

The omnidirectional circularly polarized (CP) antenna using a circular mushroom structure with curved branches is proposed. The antenna is based on the zeroth-order resonance (ZOR) mode of epsilon negative (ENG) transmission line (TL) to obtain a vertical polarization and an omnidirectional radiation pattern. Also, the horizontal polarization is obtained by the curved branches. The 90° phase difference between two orthogonal polarizations is inherently provided by the zeroth-order resonator. Therefore, the antenna has an omnidirectional CP radiation pattern in the azimuthal plane. In addition, this antenna is planar type and simply designed without a dual feeding structure and 90° phase shifter. The measured average axial ratio and left-hand (LH) CP gain are 2.03 dB and - 0.40 dBic, respectively, in the azimuthal plane.
A model-free mapless navigation method for mobile robot using reinforcement learning

In this paper, a simple and efficient method is proposed using one of the basic reinforcement learning tool Q-learning. This method is to achieve the aim of mobile robot maples navigation with collision-free. Thus, an end-to-end navigation model is built, which uses lidar sensor massage as input and moving command of mobile robot as output, so as to simplify the process of environmental perception and decision making on action. Through simulation and experiments, its effectiveness is proved. After trained by this method, mobile robot can safely reach the navigation target in an unknown environment without any prior demonstration. In addition, an extensive quantitative and qualitative evaluation of this method is presented by the comparison with traditional path planning method based on foregone global environment map.