nils-herrmann/clean_scirepeval huggingface dataset:
Real-time Voice Activity Detector Using Gammatone Filter and Modified Long-Term Signal Variability

In this paper, a real-time robust voice activity detector (VAD) is proposed. The proposed VAD adopts the gammatone filter and modify the existing long-term signal variability (LTSV) measure, i.e. known as GMLTSV in short. The proposed VAD is an improved version of existing VAD which used gammatone filter and entropy. The LTSV measure is modified to adapt to the amplitude envelopes extracted using gammatone filter by swapping entropy and variance used in LTSV measure to reduce noise effect in the extracted temporal envelopes and improve discriminative power of the extracted feature. The proposed algorithm also implements an adaptive threshold that is computed using a nonlinear filter to track short-term trend of the extracted feature in real-time. The proposed VAD using GMLTSV feature is tested against clean speech signals from TIMIT test corpus which are degraded at SNR ranged from -10dB to 20dB by non-stationary noise, eg. airport noise, babble noise, exhibition noise from Aurora-2 database, and stationary noise, eg. additive white Gaussian noise. Based on the evaluation, it is proven that the proposed GMLTSV-based VAD is robust in speech and non-speech detection even at low signal-to-noise ratio (SNR) and outperformed other existing voice activity detectors which are compared in the evaluation. The proposed VAD achieved satisfactory accuracy when compared to the impractical single frequency filtering based VAD while implementing real-time scheme for practical application.
Online newspapers and ad banners: an eye tracking study on the effects of congruity

The purpose of this paper is to study the possible influence of congruity on the way ad banners are watched and remembered in online newspapers.  The effects of the thematic connection between banner subject and article content are examined at the level of gaze behaviour using eye tracking technology, while questionnaires are exploited to assess both free and helped recall. Two “kinds of reading” are also considered, namely “imposed” (where the tester has to read a specific article) and “free” (where the tester can choose the article’s topic from a set).  Results indicate that congruity matters for the way banners are looked at, although it seems to have a limited influence on memory. Moreover, the kind of reading is significant in a few cases.  The design of ad banners for web content is very critical, due to the so-called “banner blindness” phenomenon. At the same time, ad banners are crucial for online newspapers, which rely on them as the main source of revenues. Understanding the potential of congruity is therefore an essential point.  Results to date obtained from previous research about congruity between online content and ad banners are contrasting, and further investigations are thus necessary. Moreover, in the context of online newspapers, congruity has been considered in extremely few cases and, to the best of the authors’ knowledge, without using eye tracking technology. The outcomes of this study can be exploited to drive further research in the field.
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media

Social media is often viewed as a sensor into various societal events such as disease outbreaks, protests, and elections. We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our approach detects a broad range of cyber-attacks (e.g., distributed denial of service (DDoS) attacks, data breaches, and account hijacking) in a weakly supervised manner using just a small set of seed event triggers and requires no training or labeled samples. A new query expansion strategy based on convolution kernels and dependency parses helps model semantic structure and aids in identifying key event characteristics. Through a large-scale analysis over Twitter, we demonstrate that our approach consistently identifies and encodes events, outperforming existing methods.
Excitotoxicity effects of glutamate on human neuroblastoma SH-SY5Y cells via oxidative damage

To investigate the mechanisms of excitotoxic effects of glutamate on human neuroblastoma SH-SY5Y cells. SH-SY5Y cell viability was measured by MTT assay. Other damaged profile was detected by lactate dehydrogenase (LDH) release and by 4′, 6-diamidino-2-phenylindole (DAPI) staining. The cytosolic calcium concentration was tested by calcium influx assay. The glutamate-induced oxidative stress was analyzed by cytosolic glutathione assay, superoxide dismutase (SOD) assay and extracellular malondialdehyde (MDA) assay. Glutamate treatment caused damage in SHSY5Y cells, including the decrease of cell viability, the increase of LDH release and the alterations of morphological structures. Furthermore, the concentration of cytoplasmic calcium in SH-SY5Y cells was not changed within 20 min following glutamate treatment, while cytosolic calcium concentration significantly increased within 24 h after glutamate treatment, which could not be inhibited by MK801, an antagonist of NMDA receptors, or by LY341495, an antagonist of metabotropic glutamate receptors. On the other hand, oxidative damage was observed in SH-SY5Y cells treated with glutamate, including decreases in glutathione content and SOD activity, and elevation of MDA level, all of which could be alleviated by an antioxidant Tanshinone IIA (Tan IIA, a major active ingredient from a Chinese plant Salvia Miltiorrhiza Bge). Glutamate exerts toxicity in human neuroblastoma SH-SY5Y cells possibly through oxidative damage, not through calcium homeostasis destruction mediated by NMDA receptors. 探讨谷氨酸导致人神经母细胞瘤细胞(SH-SY5Y cells)兴奋性毒损伤的机制。 MTT法检测SH-SY5Y细胞存活率; 测定乳酸脱氢酶释放量观察细胞损伤程度; DAPI染色法观察细胞凋亡形态学特点; 钙流法检测胞浆钙离子浓度变化; 以胞内谷胱甘肽、 超氧化物歧化酶活性和胞外丙二醛含量检测谷氨酸引发SH-SY5Y细胞的氧化应激状态。 谷氨酸导致SH-SY5Y细胞受损, 包括存活率下降、 乳酸脱氢酶释放量增多及形态结构发生改变; 谷氨酸处理20 min 后, 胞浆钙离子浓度无显著改变, 而处理24 h 后, 胞浆钙离子大量增加, 且MK801 (NMDA受体拮抗剂)及LY341495 (代谢型谷氨酸受体拮抗剂)均不能抑制钙离子内流的增多; 谷氨酸可导致SH-SY5Y氧化损伤, 包括胞内谷胱甘肽含量减少、 超氧化物歧化酶活性降低、 胞外脂质过氧化产物丙二醛水平升高等, 而丹参酮IIA (一种抗氧化剂)可减轻这些氧化损伤。 谷氨酸导致SH-SY5Y细胞兴奋性毒损伤可能是通过氧化损伤产生的, 而不依赖于NMDA 受体介导的钙稳态的破坏。
Proactive Data Migration for Improved Storage Availability in Large-Scale Data Centers

In face of high partial and complete disk failure rates and untimely system crashes, the executions of low-priority background tasks become increasingly frequent in large-scale data centers. However, the existing algorithms are all reactive optimizations and only exploit the temporal locality of workloads to reduce the user I/O requests during the low-priority background tasks. To address the problem, this paper proposes Intelligent Data Outsourcing (IDO), a zone-based and proactive data migration optimization, to significantly improve the efficiency of the low-priority background tasks. The main idea of IDO is to proactively identify the hot data zones of RAID-structured storage systems in the normal operational state. By leveraging the prediction tools to identify the upcoming events, IDO proactively migrates the data blocks belonging to the hot data zones on the degraded device to a surrogate RAID set in the large-scale data centers. Upon a disk failure or crash reboot, most user I/O requests addressed to the degraded RAID set can be serviced directly by the surrogate RAID set rather than the much slower degraded RAID set. Consequently, the performance of the background tasks and user I/O performance during the background tasks are improved simultaneously. Our lightweight prototype implementation of IDO and extensive trace-driven experiments on two case studies demonstrate that, compared with the existing state-of-the-art approaches, IDO effectively improves the performance of the low-priority background tasks. Moreover, IDO is portable and can be easily incorporated into any existing algorithms for RAID-structured storage systems.
A low cost sensorless drive for hybrid stepper motors based on back-EMF observer and d-axis current injection for industrial labelling machines

Stepper motor drives exhibit advantages like open loop capability, high torque density and lower cost with respect to other brushless servo alternatives. However, the typical performances of conventional open loop stepper motor drives are limited, making them unsuitable where high speeds, fast dynamics and smooth motion is required. They are also easily prone to stall and usually produce loud audible noise. Recently, the increasing price of rare earth materials used in permanent magnets is making the use of high-quality PMSMs prohibitive when medium requirements are present. To achieve performances comparable to those of a servo drive, vector control is applied, since the hybrid stepper motor can be considered as a special case of the PMSM, characterized by the presence of two phases and a high pole count (usually 50 pole pairs). Removal of the position/speed sensor is therefore highly desirable to maintain low system costs. In this paper sensorless speed control is achieved by means of a simple yet reliable stationary reference frame back-EMF observer, that can be analytically tuned. The adoption of a standard three phase inverter contributes to the reduction of the system costs, while the injection of a small constant direct axis current leads to a strong reduction of the estimation noise effect, especially at low speed. Both a laboratory test bench and an actual industrial automation machine (i.e. high speed labeller) are considered for experiments, demonstrating the importance and effectiveness of the proposal.
ECT Evaluation by an Error Measurement System According to IEC 60044-8 and 61850-9-2

A high-accuracy error measurement system for calibration of digital-output equipped electronic current transformers (ECTs) is described. The system has been developed in conformity with the requirements for digital information networks in IEC 61850-9-2 and for accuracy class and error measurement procedures in IEC 60044-8. The system has also been designed to achieve simultaneous and synchronous samplings between an ECT/MU and a reference measuring unit, a procedure for capturing and filtering sampled value (SV) packets and data processing using the discrete Fourier transform with error compensations for an integrating analog-to-digital converter. The system employs a prototype commercial digital-output equipped ECT consisting of an optical current transformer (OCT) and a merging unit (MU). The error sources of the system are discussed for the error compensations and uncertainty estimations. Based on the principle of the system, the accuracy and temperature dependence of the prototype OCT/MU are evaluated and are confirmed to be within the limits of errors of accuracy class 0.2S.
A Method of Aircraft Detection Using Fully Convolutional Network

Traditional methods on aircraft detection in remote sensing images rely on handcrafted design, which is difficult to detect and recognize the target in complex scenes and multiscale conditions. In this paper, we tackle these two problems by proposing a method for aircraft detection based on the fully convolutional neural network(FCNN). FCNN can obtain the location of the aircraft quickly and directly by minimizing a multi-task loss. Through data augmentation and transfer learning, the classification accuracy of FCN is much improved. In order to recognize small targets, we combine the resolution information with priori knowledge of aircraft to construct image pyramid structure on the test images. The experimental results show that the method with less parameters has higher accuracy and the model is simple to train.
The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube

The use of deceptive techniques in user-generated video portals is ubiquitous. Unscrupulous uploaders deliberately mislabel video descriptors aiming at increasing their views and subsequently their ad revenue. This problem, usually referred to as "clickbait," may severely undermine user experience. In this work, we study the clickbait problem on YouTube by collecting metadata for 206k videos. To address it, we devise a deep learning model based on variational autoencoders that supports the diverse modalities of data that videos include. The proposed model relies on a limited amount of manually labeled data to classify a large corpus of unlabeled data. Our evaluation indicates that the proposed model offers improved performance when compared to other conventional models. Our analysis of the collected data indicates that YouTube recommendation engine does not take into account clickbait. Thus, it is susceptible to recommending misleading videos to users.
COMPARISON OF LEVODOPA WITH CARBIDOPA OR BENSERAZIDE IN PARKINSONISM

The therapeutic efficacy and side-effects of two preparations of levodopa with extracerebral decarboxylase inhibitors have been compared in 19 patients with idiopathic parkinsonism in a blind randomised crossover trial. The mean daily dose of levodopa was 658 +/- 64 mg/day (mean +/- S.E.M.) when given together with carbidopa 66 mg/day and 605 +/- 59 mg/day when levodopa was combined with benserazide 151 mg/day. There was no significant difference between the treatment regimens either in beneficial effects on parkinsonian symptoms and signs or in the adverse effects of levodopa assessed by a clinical observer unaware of the treatment given. Of the 19 patients studied, 9 preferred the carbidopa preparation, 8 preferred the benserazide preparation, and 2 had no preference. It is concluded that there is no significant difference in therapeutic effects or adverse reactions between the two commercially available decarboxylase inhibitor-containing preparations. Central-nervous-system actions and side-effects depend on the daily dose of levodopa, regardless of the different ratios of decarboxylase inhibitors to levodopa.
Segmentation of volumetric MRA images by using capillary active contour

Precise segmentation of three-dimensional (3D) magnetic resonance angiography (MRA) images can be a very useful computer aided diagnosis (CAD) tool for clinical routines. Level sets based evolution schemes, which have been shown to be effective and easy to implement for many segmentation applications, are being applied to MRA data sets. In this paper, we present a segmentation scheme for accurately extracting vasculature from MRA images. Our proposed algorithm models capillary action and derives a capillary active contour for segmentation of thin vessels. The algorithm is implemented using the level set method and has been applied successfully on real 3D MRA images. Compared with other state-of-the-art MRA segmentation algorithms, experiments show that our method facilitates more accurate segmentation of thin blood vessels.
Advantages of Graphene Biosensors for Human Stem Cell Therapy Potency Assays

Regenerative medicine is challenged by the need to conform to rigorous guidelines for establishing safe and effective development and translation of stem cell-based therapies. Counteracting widespread concerns regarding unproven cell therapies, stringent cell-based assays seek not only to avoid harm but also to enhance quality and efficacy. Potency indicates that the cells are functionally fit for purpose before they are administered to the patient. It is a paramount quantitative critical quality attribute serving as a decisive release criterion. Given a broad range of stem cell types and therapeutic contexts the potency assay often comprises one of the most demanding hurdles for release of a cell therapy medicinal product. With need for improved biomarker assessment and expedited measurement, recent advances in graphene-based biosensors suggest that they are poised to be valuable platforms for accelerating potency assay development. Among several potential advantages, they offer versatility for sensitive measurement of a broad range of potential biomarker types, cell biocompatibility for direct measurement, and small sample sufficiency, plus ease of use and point-of-care applicability.
A Basic Architecture of an Autonomous Adaptive System With Conscious-Like Function for a Humanoid Robot

In developing a humanoid robot, there are two major objectives. One is developing a physical robot having body, hands, and feet resembling those of human beings and being able to similarly control them. The other is to develop a control system that works similarly to our brain, to feel, think, act, and learn like ours. In this article, an architecture of a control systemwith a brain-oriented logical structure for the second objective is proposed. The proposed system autonomously adapts to the environment and implements a clearly defined “consciousness” function, through which both habitual behavior and goaldirected behavior are realized. Consciousness is regarded as a function for effective adaptation at the system-level, based on matching and organizing the individual results of the underlying parallel-processing units. This consciousness is assumed to correspond to how our mind is “aware” when making our moment to moment decisions in our daily life. The binding problem and the basic causes of delay in Libet’s experiment are also explained by capturing awareness in this manner. The goal is set as an image in the system, and efficient actions toward achieving this goal are selected in the goaldirected behavior process. The system is designed as an artificial neural network and aims at achieving consistent and efficient system behavior, through the interaction of highly independent neural nodes. The proposed architecture is based on a two-level design. The first level, which we call the “basic-system,” is an artificial neural network system that realizes consciousness, habitual behavior and explains the binding problem. The second level, which we call the “extended-system,” is an artificial neural network system that realizes goal-directed behavior.
CircleView : Scalable Visualization and Navigation of Citation Networks

CircleView is a citation network browser that uses circles around circles as its visualization method to show a focus paper and two levels of its citation network. This method scales to varying numbers of papers and references, and has a structured layout that makes the visualization more readable. Bibliographic metadata is available via mouseover for all displayed papers. General requirements are presented for citation network visualization and navigation tools. An important requirement is the ability to integrate with existing institutional digital libraries, satisfied by CircleView’s use of web browser facilities for its user interface. CircleView is shown to have good visualization and performance scalability characteristics.
Mescaline and lysergic acid diethylamide (LSD) as discriminative stimuli

The observation that a particular drug state may acquire the properties of a discriminative stimulus is explicable on the basis of drug-induced interoceptive cues. The present investigation sought to determine (a) whether the hallucinogens mescaline and LSD could serve as discriminative stimuli when either drug is paired with saline and (b) whether discriminative responding would occur when the paired stimuli are produced by equivalent doses of LSD and mescaline. In a standard two-lever operant test chamber, rats received a reinforcer (sweetened milk) for correct responses according to a variable interval schedule. All sessions were preceded by one of two treatments; following treatment A, only responses on lever A were reinforced and, in a similar fashion, lever B was correct following treatment B. No responses were reinforced during the first five minutes of a daily thirty-minute session. It was found that mescaline and LSD can serve as discriminative stimuli when either drug is paired with saline and that the degree of discrimination varies with drug dose. When equivalent doses of the two drugs were given to the same animal, no discriminated responding was observed. The latter finding suggests that mescaline and LSD produce qualitatively similar interoceptive cues in the rat.
Point-based approximations for fast POMDP solving

The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. Furthermore, until recently, the efficient approximations that were available offered few theoretical guarantees regarding their performance. This paper describes a new class of approximate POMDP algorithms, called Point-Based Value Iteration (PBVI), which feature both good empirical performance (in simulation and robotic tasks), as well as solid theoretical guarantees.
Holistic CNN Compression via Low-rank Decomposition with Knowledge Transfer.

Convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks, which are extremely powerful to deal with massive training data by using tens of millions of parameters. However, CNNs often cost significant memory and computation consumption, which prohibits their usage in resource-limited environments such as mobile or embedded devices. To address the above issues, the existing approaches typically focus on either accelerating the convolutional layers or compressing the fully-connected layers separatedly, without pursuing a joint optimum. In this paper, we overcome such a limitation by introducing a holistic CNN compression framework, termed LRDKT, which works throughout both convolutional and fully-connected layers. First, a low-rank decomposition (LRD) scheme is proposed to remove redundancies across both convolutional kernels and fullyconnected matrices, which has a novel closed-form solver to significantly improve the efficiency of the existing iterative optimization solvers. Second, a novel knowledge transfer (KT) based training scheme is introduced. To recover the accumulated accuracy loss and overcome the vanishing gradient, KT explicitly aligns outputs and intermediate responses from a teacher (original) network to its student (compressed) network. We have comprehensively analyzed and evaluated the compression and speedup ratios of the proposed model on MNIST and ILSVRC 2012 benchmarks. In both benchmarks, the proposed scheme has demonstrated superior performance gains over the state-of-the-art methods. We also demonstrate the proposed compression scheme for the task of transfer learning, including domain adaptation and object detection, which show exciting performance gains over the state-of-the-arts. Our source code and compressed models are available at https://github.com/ShaohuiLin/LRDKT.
ADAPTATION MODEL IN A CONTROLLED CLINICAL TRIAL INVOLVING FAMILY CAREGIVERS OF CHRONIC PATIENTS Leidy

to elaborate the conceptual and theoretical-empirical structure, based on the application of Roy’s Adaptation model, to guide the development of a controlled clinical trial aimed at assessing the effectiveness of a nursing intervention program to promote the adaptation of family caregivers with caregiver role strain.  theoretical study. The conceptual structure was developed in three phases: development of a comprehensive understanding of the conceptual model, literature review and construction of the conceptual and theoretical-empirical structure itself.  the application process demonstrated its consistency in the design of an intervention program for family caregivers of chronic patients, to be tested in a controlled clinical trial. The indicators of adaptation were the reduced score on the caregiver tension scale and the increased perception of wellbeing and quality of life.  Roy’s model serves as an important guide for nursing research intended to test nursing interventions that favor the wellbeing of family caregivers.  Chronic disease. MODELO DE ADAPTAÇÃO EM UM ENSAIO CLÍNICO CONTROLADO COM CUIDADORES FAMILIARES DE PESSOAS COM DOENÇAS CRÔNICAS