nils-herrmann/clean_scirepeval huggingface dataset:
Expressive, Efficient, and Revocable Data Access Control for Multi-Authority Cloud Storage

Data access control is an effective way to ensure the data security in the cloud. Due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Ciphertext-Policy Attribute-based Encryption (CP-ABE) is regarded as one of the most suitable technologies for data access control in cloud storage, because it gives data owners more direct control on access policies. However, it is difficult to directly apply existing CP-ABE schemes to data access control for cloud storage systems because of the attribute revocation problem. In this paper, we design an expressive, efficient and revocable data access control scheme for multi-authority cloud storage systems, where there are multiple authorities co-exist and each authority is able to issue attributes independently. Specifically, we propose a revocable multi-authority CP-ABE scheme, and apply it as the underlying techniques to design the data access control scheme. Our attribute revocation method can efficiently achieve both forward security and backward security. The analysis and simulation results show that our proposed data access control scheme is secure in the random oracle model and is more efficient than previous works.
100 MHz, 20 V, 90% efficient synchronous buck converter with integrated gate driver

This paper describes a synchronous buck converter based on a GaN-on-SiC integrated circuit, which includes a halfbridge power stage, as well as a modified active pull-up gate driver stage. The integrated modified active pull-up driver takes advantage of depletion-mode device characteristics to achieve fast switching with low power consumption. Design principles and results are presented for a synchronous buck converter prototype operating at 100 MHz switching frequency, delivering up to 7 W from 20 V input voltage. Measured power-stage efficiency peaks above 91%, and remains above 85% over a wide range of operating conditions. Experimental results show that the converter has the ability to accurately track a 20 MHz bandwidth LTE envelope signal with 83.7% efficiency.
The neuroscience of mammalian associative learning.

Mammalian associative learning is organized into separate anatomically defined functional systems. We illustrate the organization of two of these systems, Pavlovian fear conditioning and Pavlovian eyeblink conditioning, by describing studies using mutant mice, brain stimulation and recording, brain lesions and direct pharmacological manipulations of specific brain regions. The amygdala serves as the neuroanatomical hub of the former, whereas the cerebellum is the hub of the latter. Pathways that carry information about signals for biologically important events arrive at these hubs by circuitry that depends on stimulus modality and complexity. Within the amygdala and cerebellum, neural plasticity occurs because of convergence of these stimuli and the biologically important information they predict. This neural plasticity is the physical basis of associative memory formation, and although the intracellular mechanisms of plasticity within these structures share some similarities, they differ significantly. The last Annual Review of Psychology article to specifically tackle the question of mammalian associative learning ( Lavond et al. 1993 ) persuasively argued that identifiable "essential" circuits encode memories formed during associative learning. The next dozen years saw breathtaking progress not only in detailing those essential circuits but also in identifying the essential processes occurring at the synapses (e.g., Bi & Poo 2001, Martinez & Derrick 1996 ) and within the neurons (e.g., Malinow & Malenka 2002, Murthy & De Camilli 2003 ) that make up those circuits. In this chapter, we describe the orientation that the neuroscience of learning has taken and review some of the progress made within that orientation.
An Array of Half-Width Microstrip Leaky-Wave Antennas Radiating on Boresight

An array of four uniform half-width microstrip leaky-wave antennas (MLWAs) was designed and tested to obtain maximum radiation in the boresight direction. To achieve this, uniform MLWAs are placed at 90 ° and fed by a single probe at the center. Four beams from four individual branches combine to form the resultant directive beam. The measured matched bandwidth of the array is 300 MHz (3.8-4.1 GHz). Its beam toward boresight occurs over a relatively wide 6.4% (3.8-4.05 GHz) band. The peak measured boresight gain of the array is 10.1 dBi, and its variation within the 250-MHz boresight radiation band is only 1.7 dB.
Asymmetrical effects of positive and negative events: the mobilization-minimization hypothesis.

Negative (adverse or threatening) events evoke strong and rapid physiological, cognitive, emotional, and social responses. This mobilization of the organism is followed by physiological, cognitive, and behavioral responses that damp down, minimize, and even erase the impact of that event. This pattern of mobilization-minimization appears to be greater for negative events than for neutral or positive events. Theoretical accounts of this response pattern are reviewed. It is concluded that no single theoretical mechanism can explain the mobilization-minimization pattern, but that a family of integrated process models, encompassing different classes of responses, may account for this pattern of parallel but disparately caused effects.
Design of Circularly Polarized Annular-Ring Slot Antennas Fed by a Double-Bent Microstripline

A novel design of a circularly polarized annular-ring slot antenna is discussed. The circular polarization is attained through a newly proposed double-bent microstripline that feeds the antenna at two different positions. Several structural parameters were experimentally studied with care to establish a design procedure, which was subsequently drawn into a design flow chart. Validation was carried out using the antennas designed at 3.5 and 1.59 GHz. The measured 3-dB axial-ratio bandwidth (ARBW) for the former is 10.5% and for the latter, 10.0%, which is larger than the 8.5% 3-dB ARBW required by an Inmarsat application.
SQLMR : A Scalable Database Management System for Cloud Computing

As the size of data set in cloud increases rapidly, how to process large amount of data efficiently has become a critical issue. MapReduce provides a framework for large data processing and is shown to be scalable and fault-tolerant on commondity machines. However, it has higher learning curve than SQL-like language and the codes are hard to maintain and reuse. On the other hand, traditional SQL-based data processing is familiar to user but is limited in scalability. In this paper, we propose a hybrid approach to fill the gap between SQL-based and MapReduce data processing. We develop a data management system for cloud, named SQLMR. SQLMR complies SQL-like queries to a sequence of MapReduce jobs. Existing SQL-based applications are compatible seamlessly with SQLMR and users can manage Tera to PataByte scale of data with SQL-like queries instead of writing MapReduce codes. We also devise a number of optimization techniques to improve the performance of SQLMR. The experiment results demonstrate both performance and scalability advantage of SQLMR compared to MySQL and two NoSQL data processing systems, Hive and HadoopDB.
Natural doubling of the apparent switching frequency using three-level ANPC converter

The 3L-NPC (three-level neutral-point-clamped) is the most popular multilevel converter used in high-power medium-voltage applications. An important disadvantage of this structure is the unequal distribution of losses among the switches. The performances of 3L-NPC structure were improved by developing the 3L-ANPC (Active-NPC) converter which has more degrees of freedom. In this paper the switching states and the loss distribution problem are studied for different PWM strategies. A new PWM strategy is also proposed in the paper. It has numerous advantages: (a) natural doubling of the apparent switching frequency without using the flying-capacitor concept, (b) dead times do not influence the operating mode at 50% of the duty cycle, (c) operating at both high and small switching frequencies without structural modifications and (d) better balancing of loss distribution in switches. The PSIM simulation results are shown in order to validate the proposed PWM strategy and the analysis of the switching states.
Development of a Broadband Coplanar Waveguide-to-Microstrip Transition With Vias

A transition from coplanar waveguide (CPW)-to-microstrip with vias is often used in wafer-probe measurements. This paper shows how field and impedance matching are used to develop a wideband transition. This paper demonstrates how the presence and placement of the vias affect the bandwidth and alters the impedance of the transition. The measured results on a transition show that a wideband transition with return loss better than 10 dB and an insertion loss less than 1.5 dB up to 36.64 GHz is obtained. The measurements show excellent agreement with simulation. The work presented in this paper provides a better understanding about a CPW-to-microstrip transition with vias as well as design procedures and principles that can be utilized to facilitate the realization of a broadband CPW-to-microstrip transition with vias.
A Continuous Occlusion Model for Road Scene Understanding

We present a physically interpretable, continuous threedimensional (3D) model for handling occlusions with applications to road scene understanding. We probabilistically assign each point in space to an object with a theoretical modeling of the reflection and transmission probabilities for the corresponding camera ray. Our modeling is unified in handling occlusions across a variety of scenarios, such as associating structure from motion (SFM) point tracks with potentially occluding objects or modeling object detection scores in applications such as 3D localization. For point track association, our model uniformly handles static and dynamic objects, which is an advantage over motion segmentation approaches traditionally used in multibody SFM. Detailed experiments on the KITTI raw dataset show the superiority of the proposed method over both state-of-the-art motion segmentation and a baseline that heuristically uses detection bounding boxes for resolving occlusions. We also demonstrate how our continuous occlusion model may be applied to the task of 3D localization in road scenes.
Using Software-Defined Networking for Ransomware Mitigation: The Case of CryptoWall

Currently, different forms of ransomware are increasingly threatening Internet users. Modern ransomware encrypts important user data, and it is only possible to recover it once a ransom has been paid. In this article we show how software-defined networking can be utilized to improve ransomware mitigation. In more detail, we analyze the behavior of popular ransomware - CryptoWall - and, based on this knowledge, propose two real-time mitigation methods. Then we describe the design of an SDN-based system, implemented using OpenFlow, that facilitates a timely reaction to this threat, and is a crucial factor in the case of crypto ransomware. What is important is that such a design does not significantly affect overall network performance. Experimental results confirm that the proposed approach is feasible and efficient.
SherLog: error diagnosis by connecting clues from run-time logs

Computer systems often fail due to many factors such as software bugs or administrator errors. Diagnosing such production run failures is an important but challenging task since it is difficult to reproduce them in house due to various reasons: (1) unavailability of users' inputs and file content due to privacy concerns; (2) difficulty in building the exact same execution environment; and (3) non-determinism of concurrent executions on multi-processors.
 Therefore, programmers often have to diagnose a production run failure based on logs collected back from customers and the corresponding source code. Such diagnosis requires expert knowledge and is also too time-consuming, tedious to narrow down root causes. To address this problem, we propose a tool, called SherLog, that analyzes source code by leveraging information provided by run-time logs to infer what must or may have happened during the failed production run. It requires neither re-execution of the program nor knowledge on the log's semantics. It infers both control and data value information regarding to the failed execution.
 We evaluate SherLog with 8 representative real world software failures (6 software bugs and 2 configuration errors) from 7 applications including 3 servers. Information inferred by SherLog are very useful for programmers to diagnose these evaluated failures. Our results also show that SherLog can analyze large server applications such as Apache with thousands of logging messages within only 40 minutes.
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.
Multi-script Text Extraction from Natural Scenes

Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages.
Multiple classifier application to credit risk assessment

Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how such accuracy could be improved by using classifier ensembles. Benchmarking results on four credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.
A Highly Backdrivable, Lightweight Knee Actuator for Investigating Gait in Stroke

Many of those who survive a stroke develop a gait disability known as stiff-knee gait (SKG). Characterized by reduced knee flexion angle during swing, people with SKG walk with poor energy efficiency and asymmetry due to the compensatory mechanisms required to clear the foot. Previous modeling studies have shown that knee flexion activity directly before the foot leaves the ground, and this should result in improved knee flexion angle during swing. The goal of this research is to physically test this hypothesis using robotic intervention. We developed a device that is capable of assisting knee flexion torque before swing but feels imperceptible (transparent) for the rest of the gait cycle. This device uses sheathed Bowden cable to control the deflection of a compliant torsional spring in a configuration known as a series elastic remote knee actuator (SERKA). In this investigation, we describe the design and evaluation of SERKA, which includes a pilot experiment on stroke subjects. SERKA could supply a substantial torque (12 N middot m) in less than 20 ms, with a maximum torque of 41 N middot m. The device resisted knee flexion imperceptibly when desired, at less than 1 N middot m rms torque during normal gait. With the remote location of the actuator, the user experiences a mass of only 1.2 kg on the knee. We found that the device was capable of increasing both peak knee flexion angle and velocity during gait in stroke subjects. Thus, the SERKA is a valid experimental device that selectively alters knee kinetics and kinematics in gait after stroke.
Estimating body shape of dressed humans

The paper presents a method to estimate the detailed 3D body shape of a person even if heavy or loose clothing is worn. The approach is based on a space of human shapes, learned from a large database of registered body scans. Together with this database we use as input a 3D scan or model of the person wearing clothes and apply a fitting method, based on ICP (iterated closest point) registration and Laplacian mesh deformation. The statistical model of human body shapes enforces that the model stays within the space of human shapes. The method therefore allows us to compute the most likely shape and pose of the subject, even if it is heavily occluded or body parts are not visible. Several experiments demonstrate the applicability and accuracy of our approach to recover occluded or missing body parts from 3D laser scans.
Stock Price Forecasting by Combining News Mining and Time Series Analysis

Stock price forecasting has aroused great concern in research of economy, machine learning and other fields. Time series analysis methods are usually utilized to deal with this task. In this paper, we propose to combine news mining and time series analysis to forecast inter-day stock prices. News reports are automatically analyzed with text mining techniques, and then the mining results are used to improve the accuracy of time series analysis algorithms. The experimental result on a half year Chinese stock market data indicates that the proposed algorithm can help to improve the performance of normal time series analysis in stock price forecasting significantly. Moreover, the proposed algorithm also performs well in stock price trend forecasting.
Techniques for sentiment analysis of Twitter data: A comprehensive survey

The World Wide Web has intensely evolved a novel way for people to express their views and opinions about different topics, trends and issues. The user-generated content present on different mediums such as internet forums, discussion groups, and blogs serves a concrete and substantial base for decision making in various fields such as advertising, political polls, scientific surveys, market prediction and business intelligence. Sentiment analysis relates to the problem of mining the sentiments from online available data and categorizing the opinion expressed by an author towards a particular entity into at most three preset categories: positive, negative and neutral. In this paper, firstly we present the sentiment analysis process to classify highly unstructured data on Twitter. Secondly, we discuss various techniques to carryout sentiment analysis on Twitter data in detail. Moreover, we present the parametric comparison of the discussed techniques based on our identified parameters.
Augmented Chemistry: Interactive Education System

Current knowledge delivery methods in education should move away from memory based learning to more motivated and creative education. This paper will emphasize on the advantages tangible interaction can bring to education. Augmented Chemistry provides an efficient way for designing and interacting with the molecules to understand the spatial relations between molecules. For Students it is very informative to see actual molecules representation 3D environment, inspect molecules from multiple viewpoints and control the interaction of molecules. We present in this paper an Augmented Reality system for teaching spatial relationships and chemical-reaction problem-solving skills to school-level students based on the VSEPR theory. Our system is based on inexpensive webcams and open-source software. We hope this willgenerate more ideas for educators and researcher to explore Augmented Reality
From rule-based to data-driven lexical entrainment models in spoken dialog systems

From rule-based to data-driven lexical entrainment models in spoken dialog systems José Lopes a,b,∗, Maxine Eskenazi c, Isabel Trancoso a,b a Spoken Language Laboratory, INESC-ID Lisboa, Rua Alves Redol 9, 1000-029 Lisboa, Portugal b Instituto Superior Técnico, Avenida Rovisco Pais 1, 1049-001 Lisboa, Portugal c Language Technologies Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
The Anatomy of Big Data Computing

Advances in information technology and its widespread growth in several areas of business, engineering, medical, and scientific studies are resulting in information/data explosion. Knowledge discovery and decision-making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing, a new paradigm that combines large-scale compute, new data-intensive techniques, and mathematical models to build data analytics. Big data computing demands a huge storage and computing for data curation and processing that could be delivered from on-premise or clouds infrastructures. This paper discusses the evolution of big data computing, differences between traditional data warehousing and big data, taxonomy of big data computing and underpinning technologies, integrated platform of big data and clouds known as big data clouds, layered architecture and components of big data cloud, and finally open-technical challenges and future directions.