nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid Fuzzy Logic Controller-Firefly Algorithm (FLC-FA) based for MPPT Photovoltaic (PV) system in solar car

This paper propose Firefly Algorithm (FA) new method for tuning the membership function of fuzzy logic controller for Maximum Power Point Tracker (MPPT) system Photovoltaic solar car, that consist of hybrid Fuzzy Logic Controller - Firefly Algorithm (FLC-FA) for the parameter. There are many MPPT methods for photovoltaic (PV) system, Perturbation and Observation (PnO), fuzzy logic controller (FLC) standard, and hybrid Fuzzy logic controller firefly algorithm (FLC-FA) is compared in this paper. The proposed FLC-FA algorithm is to obtain the optimal solution for MPPT for photovoltaic (PV) systems for solar cars. The result Fuzzy logic controller firefly (FLC-FA) of the proposed method, the highest maximum strength and efficiency generated is PnO = 96.31%, Standard FLC = 99.88% and Proposed FLC-FA = 99.98%. better than PnO and Fuzzy logic controller standard method. The main advantage of the proposed FLC-FA is more efficient and accurate than the still fuzzy logic controller standard.
DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning

The accurate exposure is the key of capturing high-quality photos in computational photography, especially for mobile phones that are limited by sizes of camera modules. Inspired by luminosity masks usually applied by professional photographers, in this paper, we develop a novel algorithm for learning local exposures with deep reinforcement adversarial learning. To be specific, we segment an image into sub-images that can reflect variations of dynamic range exposures according to raw low-level features. Based on these sub-images, a local exposure for each sub-image is automatically learned by virtue of policy network sequentially while the reward of learning is globally designed for striking a balance of overall exposures. The aesthetic evaluation function is approximated by discriminator in generative adversarial networks. The reinforcement learning and the adversarial learning are trained collaboratively by asynchronous deterministic policy gradient and generative loss approximation. To further simply the algorithmic architecture, we also prove the feasibility of leveraging the discriminator as the value function. Further more, we employ each local exposure to retouch the raw input image respectively, thus delivering multiple retouched images under different exposures which are fused with exposure blending. The extensive experiments verify that our algorithms are superior to state-of-the-art methods in terms of quantitative accuracy and visual illustration.
Face liveness detection through face structure analysis

Liveness detection is a way to detect whether the person is live or not during submission of his/her biometric trait. It is mandatory in order to prevent face spoofing attacks. Therefore, in this paper, we proposed a robust face structure analysis mechanism to detect the liveness by exploiting face shape information. 3D structure/shape of the face is measured on the basis of disparity map between left and right image taken by stereoscopic vision. A gradient-based eight neighbour feature extraction technique has been proposed to extract unique features from these disparity images. It produces minimal computational cost by taking subset of the overall image. We have applied linear discriminant analysis (LDA), C-means algorithms on these features while principal component analysis (PCA) is applied on raw disparity images. We have achieved a recognition rate of 91.6%, 97.5% and 98.3% using PCA, LDA and C-means respectively, which strengthened the confidence of our proposed feature extraction technique.
Colour and lighting in hospital design

Little information or guidance has been available to assist the development of a hospital’s visual environment. A report on lighting and colour design schemes, accessible to non professionals with responsibility for refurbishment strategies, was required by NHS Estates. Firstly, 20 hospitals were audited to establish a picture of current practice and to identify key issues where colour design could broadly enhance the environment for patients, staff and visitors. Critical areas were outlined in this report, where colour design can be utilised and applied, for the benefit of all users, from ambience to essential legal requirements such as colour contrast for the visually impaired. Provision of staff relaxation rooms that are different in terms of colour and lux levels from immediate work spaces, or thoughtfully designed areas for patients awaiting intensive treatment, have been shown to have some beneficial effects on a sense of well being. Colour and design have not been established as a definite cure for sickness and ill health, but certainly monotony and poor conditions in premises that have not been refurbished with any care, have had a detrimental affect on recovery rates and staff morale. The realisation that a well balanced and attractive environment is of major importance to patients’ health is, in no way new; Florence Nightingale observed that ‘a variety of form and brilliance of colour in the objects presented to patients are an actual means of recovery’. r 2005
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas, deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently led to a large influx of contributions in this direction. This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.
Faster Dual-Key Stealth Address for Blockchain-Based Internet of Things Systems

Stealth address prevents public association of a blockchain transaction’s output with a recipient’s wallet address and hides the actual destination address of a transaction. While stealth address provides an effective privacy-enhancing technology for a cryptocurrency network, it requires blockchain nodes to actively monitor all the transactions and compute the purported destination addresses, which restricts its application for resource-constrained environments like Internet of Things (IoT). In this paper, we propose DKSAP-IoT, a faster dual-key stealth address protocol for blockchain-based IoT systems. DKSAP-IoT utilizes a technique similar to the TLS session resumption to improve the performance and reduce the transaction size at the same time between two communication peers. Our theoretical analysis as well as the extensive experiments on an embedded computing platform demonstrate that DKSAPIoT is able to reduce the computational overhead by at least 50% when compared to the state-of-the-art scheme, thereby paving the way for its application to blockchain-based IoT systems.
Data mining of gene expression changes in Alzheimer brain

Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons. Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.
Effect of Control-Loops Interactions on Power Stability Limits of VSC Integrated to AC System

This paper investigates the effect of control-loops interactions on power stability limits of the voltage-source converter (VSC) as connected to an ac system. The focus is put on the physical mechanism of the control-loops interactions in the VSC, revealing that interactions among the control loops result in the production of an additional loop. The open-loop gain of the additional loop is employed to quantify the severity of the control-loop interactions. Furthermore, the power current sensitivity, closely related to control-loops interactions, is applied to estimate the maximum transferrable power of the VSC connected to an ac grid. On that basis, stability analysis results show that interactions between dc-link voltage control and phase-locked loop restrict the power angle to about 51° for stable operation with no dynamic reactive power supported. Conversely, the system is capable of approaching the ac-side maximum power transfer limits with alternating voltage control included. Simulations in MATLAB/Simulink are conducted to validate the stability analysis.
Trends & issues in crime and criminal justice No . 400 October 2010 Cloud computing : Challenges and future directions

A computer’s operating system, applications and data are typically installed and stored in the ‘traditional’ computing environment. In a cloud computing environment, individuals and businesses work with applications and data stored and/or maintained on shared machines in a web-based environment rather than physically located in the home of a user or a corporate environment. Lew Tucker, Vice President and Chief Technology Officer of Cloud Computing at Sun Microsystems, explained that cloud computing is ‘the movement of application services onto the Internet and the increased use of the Internet to access a wide variety of services traditionally originating from within a company’s data center’ (Creeger 2009: 52). For example, web-based applications such as Google’s GmailTM can be accessed in real time from an Internet-connected machine anywhere in the world.
Analogy perception applied to seven tests of word comprehension

It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are often limited by the need for hand-coded high-level representations as input. An alternative approach is to use high-level perception, in which high-level representations are automatically generated from raw data. Analogy perception is the process of recognizing analogies using high-level perception. We present PairClass, an algorithm for analogy perception that recognizes lexical proportional analogies using representations that are automatically generated from a large corpus of raw textual data. A proportional analogy is an analogy of the form A:B::C:D, meaning " A is to B as C is to D ". A lexical proportional analogy is a proportional analogy with words, such as carpenter:wood::mason:stone. PairClass represents the semantic relations between two words using a high-dimensional feature vector, in which the elements are based on frequencies of patterns in the corpus. PairClass recognizes analogies by applying standard supervised machine learning techniques to the feature vectors. We show how seven different tests of word comprehension can be framed as problems of analogy perception and we then apply PairClass to the seven resulting sets of analogy perception problems. We achieve competitive results on all seven tests. This is the first time a uniform approach has handled such a range of tests of word comprehension.
Pravastatin in elderly individuals at risk of vascular disease (PROSPER): a randomised controlled trial

Although statins reduce coronary and cerebrovascular morbidity and mortality in middle-aged individuals, their efficacy and safety in elderly people is not fully established. Our aim was to test the benefits of pravastatin treatment in an elderly cohort of men and women with, or at high risk of developing, cardiovascular disease and stroke.



We did a randomised controlled trial in which we assigned 5804 men (n=2804) and women (n=3000) aged 70-82 years with a history of, or risk factors for, vascular disease to pravastatin (40 mg per day; n=2891) or placebo (n=2913). Baseline cholesterol concentrations ranged from 4.0 mmol/L to 9.0 mmol/L. Follow-up was 3.2 years on average and our primary endpoint was a composite of coronary death, non-fatal myocardial infarction, and fatal or non-fatal stroke. Analysis was by intention-to-treat.



Pravastatin lowered LDL cholesterol concentrations by 34% and reduced the incidence of the primary endpoint to 408 events compared with 473 on placebo (hazard ratio 0.85, 95% CI 0.74-0.97, p=0.014). Coronary heart disease death and non-fatal myocardial infarction risk was also reduced (0.81, 0.69-0.94, p=0.006). Stroke risk was unaffected (1.03, 0.81-1.31, p=0.8), but the hazard ratio for transient ischaemic attack was 0.75 (0.55-1.00, p=0.051). New cancer diagnoses were more frequent on pravastatin than on placebo (1.25, 1.04-1.51, p=0.020). However, incorporation of this finding in a meta-analysis of all pravastatin and all statin trials showed no overall increase in risk. Mortality from coronary disease fell by 24% (p=0.043) in the pravastatin group. Pravastatin had no significant effect on cognitive function or disability.



Pravastatin given for 3 years reduced the risk of coronary disease in elderly individuals. PROSPER therefore extends to elderly individuals the treatment strategy currently used in middle aged people.
Effect of brief mindfulness intervention on tolerance and distress of pain induced by cold-pressor task.

A number of studies have demonstrated that short-term meditation intervention can lead to greater tolerance and lower pain or distress ratings of experimentally induced pain. However, few attempts have been made to examine the effects of short-term mindfulness-based intervention on the tolerance and distress of pain, when delivered in a therapist-free form. The present research explored the effect of brief mindfulness intervention using pre-recorded instruction on pain experimentally induced by the cold-pressor task. The effects of the mindfulness strategy, the distraction strategy and spontaneous strategy, all through the instructions of pre-recorded voices, were compared. The subjects were drawn from healthy college students and randomly assigned to the aforementioned three groups. Our results showed that compared with using spontaneous strategies, the mindfulness intervention significantly improved the participants' pain tolerance and reduced their immersion distress. The distraction strategy also significantly improved the participants' pain tolerance. However, it did not have a significant effect on the participants' level of distress during the immersion period. Our results suggest that brief mindfulness intervention without a therapist's personal involvement is capable of helping people cope with pain induced by the cold-pressor task.
LSTM-Based NeuroCRFs for Named Entity Recognition

Although NeuroCRF, an augmented Conditional Random Fields (CRF) model whose feature function is parameterized as a Feed-Forward Neural Network (FF NN) on word embeddings, has soundly outperformed traditional linear-chain CRF on many sequence labeling tasks, it is held back by the fact that FF NNs have a fixed input length and therefore cannot take advantage of the full input sentence. We propose to address this issue by replacing the FF NN with a Long Short-Term Memory (LSTM) NN, which can summarize an input of arbitrary length into a fixed dimension representation. The resulting model obtains F1=89.28 on WikiNER dataset, a significant improvement over the NeuroCRF baseline’s F1=87.58, which is already a highly competitive result.
Signal Processing in Cognitive Radio

Cognitive radio allows for usage of licensed frequency bands by unlicensed users. However, these unlicensed (cognitive) users need to monitor the spectrum continuously to avoid possible interference with the licensed (primary) users. Apart from this, cognitive radio is expected to learn from its surroundings and perform functions that best serve its users. Such an adaptive technology naturally presents unique signal-processing challenges. In this paper, we describe the fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting.
Dynamical Approximation by Hierarchical Tucker and Tensor-Train Tensors

We extend results on the dynamical low-rank approximation for the treatment of time-dependent matrices and tensors (Koch and Lubich; see [SIAM J. Matrix Anal. Appl., 29 (2007), pp. 434–454],  (2010), ) to the recently proposed hierarchical Tucker (HT) tensor format (Hackbusch and Kühn; see [ (2009), pp. 706–722]) and the tensor train (TT) format (Oseledets; see [ (2011), ), which are closely related to tensor decomposition methods used in quantum physics and chemistry. In this dynamical approximation approach, the time derivative of the tensor to be approximated is projected onto the time-dependent tangent space of the approximation manifold along the solution trajectory. This approach can be used to approximate the solutions to tensor differential equations in the HT or TT format and to compute updates in optimization algorithms within these reduced tensor formats. By deriving and analyzing the tangent space projector for the manifold of HT/TT tensors of fixed rank, we obtain curvature estimates, which allow us to obtain quasi-best approximation properties for the dynamical approximation, showing that the prospects and limitations of the ansatz are similar to those of the dynamical low rank approximation for matrices. Our results are exemplified by numerical experiments.
Improving Multi-Document Summarization via Text Classification

Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just makes up for these deficiencies. In this paper, we propose a novel summarization system called TCSum, which leverages plentiful text classification data to improve the performance of multi-document summarization. TCSum projects documents onto distributed representations which act as a bridge between text classification and summarization. It also utilizes the classification results to produce summaries of different styles. Extensive experiments on DUC generic multidocument summarization datasets show that, TCSum can achieve the state-of-the-art performance without using any hand-crafted features and has the capability to catch the variations of summary styles with respect to different text categories.
28 GHz and 73 GHz millimeter-wave indoor propagation measurements and path loss models

This paper presents 28 GHz and 73 GHz millimeter-wave propagation measurements performed in a typical office environment using a 400 Megachip-per-second broadband sliding correlator channel sounder and highly directional steerable 15 dBi (30° beamwidth) and 20 dBi (15° beamwidth) horn antennas. Power delay profiles were acquired for 48 transmitter-receiver location combinations over distances ranging from 3.9 m to 45.9 m with maximum transmit powers of 24 dBm and 12.3 dBm at 28 GHz and 73 GHz, respectively. Directional and omnidirectional path loss models and RMS delay spread statistics are presented for line-of-sight and non-line-of-sight environments for both co- and cross-polarized antenna configurations. The LOS omnidirectional path loss exponents were 1.1 and 1.3 at 28 GHz and 73 GHz, and 2.7 and 3.2 in NLOS at 28 GHz and 73 GHz, respectively, for vertically-polarized antennas. The mean directional RMS delay spreads were 18.4 ns and 13.3 ns, with maximum values of 193 ns and 288 ns at 28 GHz and 73 GHz, respectively.
Out-of-domain FrameNet Semantic Role Labeling

Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research