nils-herrmann/clean_scirepeval huggingface dataset:
Generalized Zero-Shot Learning with Deep Calibration Network

A technical challenge of deep learning is recognizing target classes without seen data. Zero-shot learning leverages semantic representations such as attributes or class prototypes to bridge source and target classes. Existing standard zero-shot learning methods may be prone to overfitting the seen data of source classes as they are blind to the semantic representations of target classes. In this paper, we study generalized zero-shot learning that assumes accessible to target classes for unseen data during training, and prediction on unseen data is made by searching on both source and target classes. We propose a novel Deep Calibration Network (DCN) approach towards this generalized zero-shot learning paradigm, which enables simultaneous calibration of deep networks on the confidence of source classes and uncertainty of target classes. Our approach maps visual features of images and semantic representations of class prototypes to a common embedding space such that the compatibility of seen data to both source and target classes are maximized. We show superior accuracy of our approach over the state of the art on benchmark datasets for generalized zero-shot learning, including AwA, CUB, SUN, and aPY.
Cuckoo++ hash tables: high-performance hash tables for networking applications

Hash tables are essential data-structures for networking applications (e.g., connection tracking, firewalls, network address translators). Among these, cuckoo hash tables provide excellent performance by processing lookups with very few memory accesses (2 to 3 per lookup). Yet, they remain memory bound and each memory access impacts performance. In this paper, we propose algorithmic improvements to cuckoo hash tables to eliminate unnecessary memory accesses, without altering the properties of the original cuckoo hash table so that all existing theoretical analysis remain applicable. We also present an implementation tailored to run efficiently on Intel Xeon processors, thus supporting NFV and softwarization trends and compare it to the optimized implementation of DPDK. On a single core, our implementation achieves 37M positive lookups per second (i.e., when the key looked up is present in the table), and 60M negative lookups per second, a 45% to 70% improvement over DPDK.
Understanding Internet Banking Adoption and Use Behavior: A Hong Kong Perspective

Hong Kong is an international financial center well known for its efficiency and its ability to adapt and keep up with the times. Recently, however, the Hong Kong banking industry has been losing competitive advantages in some areas, with the adoption of Internet Banking being one of them. Hong Kong banks have been slower than some other international banks in joining the e-commerce evolution, which first emerged in the United States in mid-90s. Financial institutions in the U.S. have introduced and promoted online banking to provide better customer services. Many property and stock investment firms in Hong Kong have also jumped on the bandwagon and adopted the Internet as a channel for providing better and more efficient services
Chemi-net: a graph convolutional network for accurate drug property prediction

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.
Recommending citations with translation model

Citation Recommendation is useful for an author to find out the papers or books that can support the materials she is writing about. It is a challengeable problem since the vocabulary used in the content of papers and in the citation contexts are usually quite different. To address this problem, we propose to use translation model, which can bridge the gap between two heterogeneous languages. We conduct an experiment and find the translation model can provide much better candidates of citations than the state-of-the-art methods.
Relationships between sprinting, agility, and jump ability in female athletes.

The aim of this study was to assess the relationships between various field tests in female athletes. Altogether, 83 high school soccer, 51 college soccer, and 79 college lacrosse athletes completed tests for linear sprinting, countermovement jump, and agility in a single session. Linear sprints (9.1, 18.3, 27.4, and 36.6 m) and agility tests (Illinois and pro-agility) were evaluated using infrared timing gates, while countermovement jump height was assessed using an electronic timing mat. Pearson's product-moment correlation coefficients (r) were used to determine the strength and directionality of the relationship between tests and coefficients of determination (r2) were used to examine the amount of explained variance between tests. All of the performance scores were statistically correlated with each other; however, the coefficients of determination were low, moderate, and high depending on the test pairing. Linear sprint split times were strongly correlated with each other (r= 0.775 to 0.991). The relationship between countermovement jump height and linear sprinting was stronger with the longer distances (27.4 and 36.6 m) than with the shorter distances (9.1 and 18.3 m), and showed a stronger relationship within the college athletes (r= -0.658 to -0.788) than high school soccer players (r= -0.491 to -0.580). The Illinois and pro-agility tests were correlated (r > or = 0.600) with each other as well as with linear sprint times. The results of this study indicate that linear sprinting, agility, and vertical jumping are independent locomotor skills and suggest a variety of tests ought to be included in an assessment protocol for high school and college female athletes.
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data. This requires a system that understands users’ questions and converts them to SQL queries automatically. In this paper we present a novel approach, TYPESQL, which views this problem as a slot filling task. Additionally, TYPESQL utilizes type information to better understand rare entities and numbers in natural language questions. We test this idea on the WikiSQL dataset and outperform the prior state-of-the-art by 5.5% in much less time. We also show that accessing the content of databases can significantly improve the performance when users’ queries are not wellformed. TYPESQL gets 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.
Seeing Small Faces from Robust Anchor's Perspective

This paper introduces a novel anchor design principle to support anchor-based face detection for superior scaleinvariant performance, especially on tiny faces. To achieve this, we explicitly address the problem that anchor-based detectors drop performance drastically on faces with tiny sizes, e.g. less than 16 Ã— 16 pixels. In this paper, we investigate why this is the case. We discover that current anchor design cannot guarantee high overlaps between tiny faces and anchor boxes, which increases the difficulty of training. The new Expected Max Overlapping (EMO) score is proposed which can theoretically explain the low overlapping issue and inspire several effective strategies of new anchor design leading to higher face overlaps, including anchor stride reduction with new network architectures, extra shifted anchors, and stochastic face shifting. Comprehensive experiments show that our proposed method significantly outperforms the baseline anchor-based detector, while consistently achieving state-of-the-art results on challenging face detection datasets with competitive runtime speed.
Text Detection and Character Extraction in Natural Scene Images

Detecting text regions in natural scene images has become an important area due to its varies applications. Text Information Extraction (TIE) System involves detecting text regions in a given image, localizing it, extracting the text part and recognizing text using OCR. This work basically concentrates on the detection and extraction of text in natural scene images. In this work, the test image will be preprocessed using RGB to Gray conversion, binarization, Edge Detection method and Geometric based Noise removal method. The features from the pre-processed image are extracted. The extracted features are used by the trained SVM classifier to detect the text regions. After detecting text regions, characters are extracted and finally displayed.  Text Information Extraction (TIE); Natural Scene Images; Text Detection; Text Extraction; Support Vector Machine (SVM).
Cloud Event Programming Paradigms: Applications and Analysis

Rapid expansion in cloud event technologies such as Amazon Web Service's Lambda, IBM Bluemix's OpenWhisk, Google Cloud Platform's Cloud Functions, and Microsoft Azure's Functions motivates study of software development in these services and their potential as a disruptive force in commercial cloud technologies. In addition to discussing the current state of cloud event services, this paper presents two real world applications utilizing these platforms: Lambdefy, a library designed to make traditional web application run effectively in AWS Lambda, and a performant media management service designed by Trek10, capable of resizing thousands of images per second. Furthermore, we discuss how cloud event technologies enable and/or limit these applications, motivate new software design paradigms in a cloud event environment, and highlight compelling use case scenarios and barriers to entry for cloud event services. AWS cloud technologies are exclusively used due to their maturity and the recent release of the other platforms, while Node.js and the Serverless Framework are utilized for deployment and application development.
Progress and Recent Realizations of Miniaturized Inductive Proximity Sensors for Automation

Noncontacting inductive proximity sensors (IPSs) are widely preferred for position detection in industrial applications. Advances in computer-aided design simulation tools, integration, and assembly techniques for the last ten years have made possible continued sensor-feature improvements in parallel to size reduction toward very small miniaturized versions. This paper refers to the progress in the past years, presenting recently gained knowledge in sensing principle, evaluation electronics, and manufacturing. The most significant improvements are the usage of a drum-core topology, application-specified integrated circuit implementation, and face-down flip-chip technology. The fundamentals shown in this paper were essential for the design and development of a worldwide innovation: the smallest single-component IPS family that has the largest switching distance. Mounted in a stainless steel tube with a diameter of only 3 mm and a length of 33 mm, the smallest sensor in the family is able to detect metal targets at distances of up to 3 mm and provide an output switching signal with high-current capacity
Camera Distance from Face Images

We present a method for estimating the distance between a camera and a human head in 2D images from a calibrated camera. Leading head pose estimation algorithms focus mainly on head orientation (yaw, pitch, and roll) and translations perpendicular to the camera principal axis. Our contribution is a system that can estimate head pose under large translations parallel to the camera’s principal axis. Our method uses a set of exemplar 3D human heads to estimate the distance between a camera and a previously unseen head. The distance is estimated by solving for the camera pose using Effective Perspective n-Point (EPnP). We present promising experimental results using the Texas 3D Face Recognition Database.
Gene selection from microarray data for cancer classification - a machine learning approach

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, naïve Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.
Dynamics co-simulation of a type of spot welding robot by RecurDyn and Simulink

A co-simulation on virtual prototype of a type of spot welding robot has been accomplished by combining MATLAB/Simulink with RecurDyn. The control algorithm is executed under MATLAB to generate command forces, which are then exported to RecurDyn environment and applied to actuators of the virtual prototype at each cycle of time. Dynamic control on joint angles was accomplished through the co-simulation. Torques of each joint and the velocity of the clamp end were also acquired by co-simulation, which can give useful information for optimizing robot design.
A comparative analysis of tightly-coupled monocular, binocular, and stereo VINS

In this paper, a sliding-window two-camera vision-aided inertial navigation system (VINS) is presented in the square-root inverse domain. The performance of the system is assessed for the cases where feature matches across the two-camera images are processed with or without any stereo constraints (i.e., stereo vs. binocular). To support the comparison results, a theoretical analysis on the information gain when transitioning from binocular to stereo is also presented. Additionally, the advantage of using a two-camera (both stereo and binocular) system over a monocular VINS is assessed. Furthermore, the impact on the achieved accuracy of different image-processing frontends and estimator design choices is quantified. Finally, a thorough evaluation of the algorithm's processing requirements, which runs in real-time on a mobile processor, as well as its achieved accuracy as compared to alternative approaches is provided, for various scenes and motion profiles.
EnclaveDB: A Secure Database Using SGX

We propose EnclaveDB, a database engine that guarantees confidentiality, integrity, and freshness for data and queries. EnclaveDB guarantees these properties even when the database administrator is malicious, when an attacker has compromised the operating system or the hypervisor, and when the database runs in an untrusted host in the cloud. EnclaveDB achieves this by placing sensitive data (tables, indexes and other metadata) in enclaves protected by trusted hardware (such as Intel SGX). EnclaveDB has a small trusted computing base, which includes an in-memory storage and query engine, a transaction manager and pre-compiled stored procedures. A key component of EnclaveDB is an efficient protocol for checking integrity and freshness of the database log. The protocol supports concurrent, asynchronous appends and truncation, and requires minimal synchronization between threads. Our experiments using standard database benchmarks and a performance model that simulates large enclaves show that EnclaveDB achieves strong security with low overhead (up to 40% for TPC-C) compared to an industry strength in-memory database engine.
An evaluation of NoSQL databases for EHR systems

Electronic Health Record (EHR) systems offer significant benefits for healthcare. The improved availability of healthcare information from multiple locations contributes to the accuracy and timeliness of care, and should lead to overall improved quality of healthcare delivery. Practical experience and relevant research demonstrate that there are many technological issues that need to be addressed for modern healthcare systems to be effective in sharing EHRs as the structure and size of the healthcare data have changed considerably over time. Recent literature shows that the emerging NoSQL databases have significant advantages such as easy and automatic scaling, better performance and high availability which address the limitations of relational databases in distributed healthcare systems. In this paper we reviewed EHRs and the key features of NoSQL databases. We then evaluated the suitability of NoSQL databases in meeting the requirements of national EHR systems in sharing EHRs in a distributed system environment.
A Framework for Estimating Driver Decisions Near Intersections

We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework.
A comparative assessment of classification methods

Classification systems play an important role in business decision-making tasks by classifying the available information based on some criteria. The objective of this research is to assess the relative performance of some well-known classification methods. We consider classification techniques that are based on statistical and AI techniques. We use synthetic data to perform a controlled experiment in which the data characteristics are systematically altered to introduce imperfections such as nonlinearity, multicollinearity, unequal covariance, etc. Our experiments suggest that data characteristics considerably impact the classification performance of the methods. The results of the study can aid in the design of classification systems in which several classification methods can be employed to increase the reliability and consistency of the classification. D 2002 Elsevier Science B.V. All rights reserved.
The Moderating Role of Organizational Culture in the Relationship between Power, Trust, and eSCMS Adoption Intention

Building on multiple theoretical perspectives, we examined how organizational culture moderates the relationship between power, trust, and a firm’s eSCMS adoption intention. We tested the hypotheses using survey data collected from senior executives in China. Our findings reveal that a target firm’s perceived mediated power would negatively impact its trust toward a dominant firm, while its perceived non-mediated power would positively affect its trust. Meanwhile, trust can positively influence the target firm’s eSCMS adoption intention. Further, an internally focused culture weakens the negative effects of mediated power on trust. Meanwhile, an externally focused culture weakens the positive relationships between non-mediated power and trust, and between trust and eSCMS adoption intention. The externally focused culture could weaken the negative relationship between mediated power and trust either. The theoretical contributions and managerial implications of the study are discussed.
SPH Based Shallow Water Simulation

We present an efficient method that uses particles to solve the 2D shallow water equations. These equations describe the dynamics of a body of water represented by a height field. Instead of storing the surface heights using uniform grid cells, we discretize the fluid with 2D SPH particles and compute the height according to the density at each particle location. The particle discretization offers the benefits that it simplifies the use of sparsely filled domains and arbitrary boundary geometry. Our solver can handle terrain slopes and supports two-way coupling of the particle-based height field with rigid objects. An improved surface definition is presented that reduces visible bumps related to the underlying particle representation. It furthermore smoothes areas with separating particles to achieve better rendering results. Both the physics and the rendering are implemented on modern GPUs resulting in interactive performances in all our presented examples.
You Can Touch This: Eleven Years and 258218 Images of Objects

Touch has become a central input modality for a wide variety of interactive devices, most of our mobile devices are operated using touch. In addition to interacting with digital artifacts, people touch and interact with many other objects in their daily lives. We provide a unique photo dataset containing all touched objects over the last 11 years. All photos were contributed by Alberto Frigo, who was involved early on in the "Quantified Self" movement. He takes photos of every object he touches with his dominant hand. We analyzed the 258,218 images with respect to the types objects, their distribution, and related activities.
The politics of comments: predicting political orientation of news stories with commenters' sentiment patterns

Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification.