nils-herrmann/clean_scirepeval huggingface dataset:
SENTIMENT ANALYSIS OF TWITTER DATA USING EMOTICONS AND EMOJI IDEOGRAMS

Twitter is an online social networking service where worldwide users publish their opinions on a variety of topics, discuss current issues, complain, and express positive or negative sentiment for products they use in daily life. Therefore, Twitter is a rich source of data for opinion mining and sentiment analysis. However, sentiment analysis for Twitter messages (tweets) is regarded as a challenging problem because tweets are short and informal. This paper focuses on this problem by the analyzing of symbols called emotion tokens, including emotion symbols (e.g. emoticons and emoji ideograms). According to observation, these emotion tokens are commonly used. They directly express one’s emotions regardless of his/her language, hence they have become a useful signal for sentiment analysis on multilingual tweets. The paper describes the approach to performing sentiment analysis, that is able to determine positive, negative and neutral sentiments for a tested topic.
A tandem-lens epifluorescence macroscope: Hundred-fold brightness advantage for wide-field imaging

The design of a macroscope constructed with photography lenses is described and several applications are demonstrated. The macroscope incorporates epi-illumination, a 0.4 numerical aperture, and a 40 mm working distance for imaging wide fields in the range of 1.5-20 mm in diameter. At magnifications of 1X to 2.5X, fluorescence images acquired with the macroscope were 100-700 times brighter than those obtained with commercial microscope objectives at similar magnifications. In several biological applications, the improved light collection efficiency (20-fold, typical) not only minimized bleaching effects, but, in concert with improved illumination throughput (15-fold, typical), significantly enhanced object visibility as well. Reduced phototoxicity and increased signal-to-noise ratios were observed in the in vivo real-time optical imaging of cortical activity using voltage-sensitive dyes. Furthermore, the macroscope has a depth of field which is 5-10 times thinner than that of a conventional low-power microscope. This shallow depth of field has facilitated the imaging of cortical architecture based on activity-dependent intrinsic cortical signals in the living primate brain. In these reflection measurements large artifacts from the surface blood vessels, which were observed with conventional lenses, were eliminated with the macroscope.
Anytime Neural Prediction via Slicing Networks Vertically

The pioneer deep neural networks (DNNs) have emerged to be deeper or wider for improving their accuracy in various applications of artificial intelligence. However, DNNs are often too heavy to deploy in practice, and it is often required to control their architectures dynamically given computing resource budget, i.e., anytime prediction. While most existing approaches have focused on training multiple shallow sub-networks jointly, we study training thin sub-networks instead. To this end, we first build many inclusive thin sub-networks (of the same depth) under a minor modification of existing multi-branch DNNs, and found that they can significantly outperform the state-of-art dense architecture for anytime prediction. This is remarkable due to their simplicity and effectiveness, but training many thin subnetworks jointly faces a new challenge on training complexity. To address the issue, we also propose a novel DNN architecture by forcing a certain sparsity pattern on multi-branch network parameters, making them train efficiently for the purpose of anytime prediction. In our experiments on the ImageNet dataset, its sub-networks have up to 43.3% smaller sizes (FLOPs) compared to those of the state-of-art anytime model with respect to the same accuracy. Finally, we also propose an alternative task under the proposed architecture using a hierarchical taxonomy, which brings a new angle for anytime prediction.
A 40 Gb/s clock and data recovery circuit in 0.18 μm CMOS technology

A phase-locked clock and data recovery circuit incorporates a multiphase LC oscillator and a quarter-rate bang-bang phase detector. The oscillator is based on differential excitation of a closed-loop transmission line at evenly spaced points, providing half-quadrature phases. The phase detector employs eight flip-flops to sample the input every 12.5 ps, detecting data transitions while retiming and demultiplexing the data into four 10-Gb/s outputs. Fabricated in 0.18m CMOS technology, the circuit produces a clock jitter of 0.9 psrms and 9.67 pspp with a PRBS of2 1 while consuming 144 mW from a 2-V supply.
An actor-focused model of justice rule adherence and violation: the role of managerial motives and discretion.

Research on organizational justice has focused primarily on the receivers of just and unjust treatment. Little is known about why managers adhere to or violate rules of justice in the first place. The authors introduce a model for understanding justice rule adherence and violation. They identify both cognitive motives and affective motives that explain why managers adhere to and violate justice rules. They also draw distinctions among the justice rules by specifying which rules offer managers more or less discretion in their execution. They then describe how motives and discretion interact to influence justice-relevant actions. Finally, the authors incorporate managers' emotional reactions to consider how their actions may change over time. Implications of the model for theory, research, and practice are discussed.
Transcranial Doppler Ultrasound: A Review of the Physical Principles and Major Applications in Critical Care

Transcranial Doppler (TCD) is a noninvasive ultrasound (US) study used to measure cerebral blood flow velocity (CBF-V) in the major intracranial arteries. It involves use of low-frequency (≤2 MHz) US waves to insonate the basal cerebral arteries through relatively thin bone windows. TCD allows dynamic monitoring of CBF-V and vessel pulsatility, with a high temporal resolution. It is relatively inexpensive, repeatable, and portable. However, the performance of TCD is highly operator dependent and can be difficult, with approximately 10-20% of patients having inadequate transtemporal acoustic windows. Current applications of TCD include vasospasm in sickle cell disease, subarachnoid haemorrhage (SAH), and intra- and extracranial arterial stenosis and occlusion. TCD is also used in brain stem death, head injury, raised intracranial pressure (ICP), intraoperative monitoring, cerebral microembolism, and autoregulatory testing.
Facial expression recognition using dual dictionary learning

In this paper, a novel method is proposed for Facial Expression Recognition (FER) using dictionary learning to learn both identity and expression dictionaries simultaneously. Accordingly, an automatic and comprehensive feature extraction method is proposed. The proposed method accommodates real-valued scores to a probability of what percent of the given Facial Expression (FE) is present in the input image. To this end, a dual dictionary learning method is proposed to learn both regression and feature dictionaries for FER. Then, two regression classification methods are proposed using a regression model formulated based on dictionary learning and two known classification methods including Sparse Representation Classification (SRC) and Collaborative Representation Classification (CRC). Convincing results are acquired for FER on the CK+, CK, MMI and JAFFE image databases compared to several state-of-the-arts. Also, promising results are obtained from evaluating the proposed method for generalization on other databases. The proposed method not only demonstrates excellent performance by obtaining high accuracy on all four databases but also outperforms other state-of-the-art approaches.
Research challenges in nextgen service orchestration

Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world.
Anatomy of Drive-by Download Attack

Drive-by download attacks where web browsers are subverted by malicious content delivered by web servers have become a common attack vector in recent years. Several methods for the detection of malicious content on web pages using data mining techniques to classify web pages as malicious or benign have been proposed in the literature. However, each proposed method uses different content features in order to do the classification and there is a lack of a high-level frameworks for comparing these methods based upon their choice of detection features. The lack of a framework makes it problematic to develop experiments to compare the effectiveness of methods based upon different selections of features. This paper presents such a framework derived from an analysis of of drive-by download attacks that focus upon potential state changes seen when Internet browsers render HTML documents. This framework can be used to identify potential features that have not yet been exploited and to reason about the challenges for using those features in detection drive-by download attack.
Extending GIS-based visual analysis: the concept of visualscapes

A Geographical Information System (GIS) is used to retrieve and explore the spatial properties of the visual structure inherent in space. The first section of the article aims to gather, compare and contrast existing approaches used to study visual space and found in disciplines such as landscape architecture, urbanism, geography and landscape archaeology. The concept of a visualscape is introduced in the following section as a tentative unifying concept to describe all possible ways in which the structure of visual space may be defined, broken down and represented within GIS independently of the context in which it is applied. Previous visibility studies in GIS are reviewed and further explored under this new concept. The last section presents the derivation of new visual parameters and introduces a new data structure (i.e. a vector field) to describe the visual exposure of a terrain.
Celtic Elements in Northwestern Spain in Pre-Roman times

The aim of this article is to present a synthetic overview of the state of knowledge regarding the Celtic cultures in the northwestern Iberian Peninsula. It reviews the difficulties linked to the fact that linguists and archaeologists do not agree on this subject, and that the hegemonic view rejects the possibility that these populations can be considered Celtic. On the other hand, the examination of a range of direct sources of evidence, including literary and epigraphic texts, and the application of the method of historical anthropology to the available data, demonstrate the validity of the consideration of Celtic culture in this region, which can be described as a protohistorical society of the Late Iron Age, exhibiting a hierarchical organization based on ritually chosen chiefs whose power was based in part on economic redistribution of resources, together with a priestly elite more or less of the druidic type. However, the method applied cannot on its own answer the questions of when and how this Celtic cultural dimension of the proto-history of the northwestern Iberian Peninsula developed.
Path Following Control of Mobile Robot Using Lyapunov Techniques and PID Cntroller

Path following of the mobile robot is one research hot for the mobile robot navigation. For the control system of the wheeled mobile robot(WMR) being in nonhonolomic system and the complex relations among the control parameters, it is difficult to solve the problem based on traditional mathematics model. In this paper, we presents a simple and effective way of implementing an adaptive following controller based on the PID for mobile robot path following. The method uses a non-linear model of mobile robot kinematics and thus allows an accurate prediction of the future trajectories. The proposed controller has a parallel structure that consists of PID controller with a fixed gain. The control law is constructed on the basis of Lyapunov stability theory. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity and orientation tracking control of the nonholonomic WMR. The simulation results of wheel type mobile robot platform are given to show the effectiveness of the proposed algorithm.
Product Lifecycle Management : Closing the Knowledge Loops

Competitive success of manufacturing firms is by and large determined by the success of the products they introduce to the market. This is why companies continuously try to improve the efficacy of their product realization process. Product Lifecycle Management (PLM) is a business solution which aims to streamline the flow of information about the product and related processes throughout the product’s lifecycle such that the right information in the right context at the right time can be made available. Yet, few organizations are positioned to reap the true benefits of PLM. One major reason for this is a lack of clear understanding of what PLM is, its core features and functions, and its relationship to the myriad of current software tools. This paper aims to do that and also elaborates on the role of PLM as a knowledge management system.
QGAN: Quantized Generative Adversarial Networks

The intensive computation and memory requirements of generative adversarial neural networks (GANs) hinder its real-world deployment on edge devices such as smartphones. Despite the success in model reduction of CNNs, neural network quantization methods have not yet been studied on GANs, which are mainly faced with the issues of both the effectiveness of quantization algorithms and the instability of training GAN models. In this paper, we start with an extensive study on applying existing successful methods to quantize GANs. Our observation reveals that none of them generates samples with reasonable quality because of the underrepresentation of quantized values in model weights, and the generator and discriminator networks show different sensitivities upon quantization methods. Motivated by these observations, we develop a novel quantization method for GANs based on EM algorithms, named as QGAN. We also propose a multi-precision algorithm to help find the optimal number of bits of quantized GAN models in conjunction with corresponding result qualities. Experiments on CIFAR10 and CelebA show that QGAN can quantize GANs to even 1-bit or 2-bit representations with results of quality comparable to original models.
History of cannabis as a medicine: a review.

Cannabis as a medicine was used before the Christian era in Asia, mainly in India. The introduction of cannabis in the Western medicine occurred in the midst of the 19th century, reaching the climax in the last decade of that century, with the availability and usage of cannabis extracts or tinctures. In the first decades of the 20th century, the Western medical use of cannabis significantly decreased largely due to difficulties to obtain consistent results from batches of plant material of different potencies. The identification of the chemical structure of cannabis components and the possibility of obtaining its pure constituents were related to a significant increase in scientific interest in such plant, since 1965. This interest was renewed in the 1990's with the description of cannabinoid receptors and the identification of an endogenous cannabinoid system in the brain. A new and more consistent cycle of the use of cannabis derivatives as medication begins, since treatment effectiveness and safety started to be scientifically proven.
Facial Recognition ( A Review )

A facial recognition system is an application which is used for identifying or verifying a person from a digital image or a video frame. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is generally used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Areas such as network security, content indexing and retrieval, and video compression benefit from face recognition technology since people themselves are the main source of interest. Network access control via face recognition not only makes hackers virtually impossible to steal one's "password", but also increases the user friendliness in human-computer interaction. Although humans have always had the innate ability to recognize and distinguish between faces, yet computers only recently have shown the same ability. In the mid 1960s, scientists began work on using the computer to recognize human faces. Since then, facial recognition software has come a long way. In this article, I have explored the reasons behind using facial recognition, the products developed to implement this biometrics technique and also the criticisms and advantages that are bounded with it.
Baromagnetic Effect in the Hexagonal Mn3Sn System

The magnetic behaviors influenced by the hydrostatic pressure in the polycrystalline hexagonal Mn<sub>3</sub>Sn intermetallic compound are briefly reported. The crystalline structure of Mn<sub>3</sub>Sn is determined to be hexagonal with lattice parameters <inline-formula> <tex-math notation="LaTeX">$a = b = 5.674$ </tex-math></inline-formula>(6) Å and <inline-formula> <tex-math notation="LaTeX">$c = 4.533$ </tex-math></inline-formula>(6) Å by means of the powder X-ray diffraction and the subsequent Rietveld refinement. Two distinct transitions can be observed from the M(T) curves in the range of 150-450 K. During the cooling process, Mn<sub>3</sub>Sn sample first changes into a triangular antiferromagnetic (AFM) state with weak ferromagnetism at the Néel temperature of ~412 K, and then, it further transforms to a spiral AFM spin arrangement over a broad temperature range from 260 to 180 K at ambient pressure. With the application or the increase of hydrostatic pressures, this triangular to spiral AFM transition occurs at higher temperatures, while the magnetization of triangular AFM phase increases significantly. Such a phenomenon can be explained by the fact that the hydrostatic pressure compresses the sample and shortens the Mn–Mn separation, and thus, the moments of Mn will deviate from their ideal 120° configuration to their easy axis in the basal plane. The variations of transition temperature and maximum magnetization with applied hydrostatic pressure confirm the incremental instability of the triangular AFM arrangement under pressure.
The Delphi technique : a method for testing complex and multifaceted topics

The purpose of this paper is to provide project management (PM) students with an example of the Delphi research approach that was applied to a recent doctoral research thesis. The objective is to provide both a description of the approach and an explanation of how it was successfully applied so that researchers in PM may become more aware and expand their perceptions of methodological options available to them.  The paper sets out a literature review of the Delphi approach and explains this research method as it was experienced as a research tool. The paper also provides reflection on the experience of being a researcher undertaking a doctoral study.  The Delphi approach is one of many that may be selected for researching PM issues and problems. It is appropriate for researching complex issues where larger scale quantitative “hard data” fails to unearth richness in tacit knowledge to help the research understand subtle expert opinion. It does not offer the rigor of clinical testing or quantitative analysis, but it provides a scientific methodology that is well suited to issues that require the insights of subject matter experts. The use of Delphi in this context was successful in that the thesis was passed and so its use in this context may be now considered as proved through rigorous examination.  The paper provides a solid literature review that may be used or referred to by researchers wishing to adopt this research approach. It also describes the protocols and processes adopted in the doctoral study. Thus, this paper provides the opportunity for PM researchers to expand their repertoire of research tools. Practitioners may also benefit from this paper as it provides a useful approach to assess and validate expert knowledge that could be contestable in a range of practice situations.
A Recommendation Model Based on Deep Neural Network

In recent years, recommendation systems have been widely used in various commercial platforms to provide recommendations for users. Collaborative filtering algorithms are one of the main algorithms used in recommendation systems. Such algorithms are simple and efficient; however, the sparsity of the data and the scalability of the method limit the performance of these algorithms, and it is difficult to further improve the quality of the recommendation results. Therefore, a model combining a collaborative filtering recommendation algorithm with deep learning technology is proposed, therein consisting of two parts. First, the model uses a feature representation method based on a quadric polynomial regression model, which obtains the latent features more accurately by improving upon the traditional matrix factorization algorithm. Then, these latent features are regarded as the input data of the deep neural network model, which is the second part of the proposed model and is used to predict the rating scores. Finally, by comparing with other recommendation algorithms on three public datasets, it is verified that the recommendation performance can be effectively improved by our model.
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2 . Modeling and identification

Battery management systems in hybrid electric vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. In order to use EKF to estimate the desired quantities, we first require a mathematical model that can accurately capture the dynamics of a cell. In this paper we “evolve” a suitable model from one that is very primitive to one that is more advanced and works well in practice. The final model includes terms that describe the dynamic contributions due to open-circuit voltage, ohmic loss, polarization time constants, electro-chemical hysteresis, and the effects of temperature. We also give a means, based on EKF, whereby the constant model parameters may be determined from cell test data. Results are presented that demonstrate it is possible to achieve root-mean-squared modeling error smaller than the level of quantization error expected in an implementation.