nils-herrmann/clean_scirepeval huggingface dataset:
On-The-Fly Academic Linked Data Integration

The web of Linked Open Data (LOD) has a prominent and rapid evolution recently. Over the last few years, LOD had developed to involve a wide range of various domains. Due to these facts, and the great interconnections among linked open datasets, linked data integration task had gained a huge attention and became a focal point of research. LOD applications aim to incorporate data from different LOD sources. Unfortunately, these sources of data are heterogeneous in schema and/or in vocabularies. Due to this heterogeneity, numerous challenges are emerging that have to be overcome. In this paper, a LOD integration framework is proposed, which aims to tackle these challenges. It works on integrating academic LOD datasets that reside in different LOD repositories with intrinsic schema and vocabularies heterogeneity. An automatic mapping technique in the integration processes is proposed in this paper. Consequently, an obvious decrease in execution time for the entire integration process, as well as, a great progress in the integrated data quality assessment metrics has been achieved.
A study of success and failure in product innovation: The case of the U.S. electronics industry

This paper summarizes the first phase of the Stanford Innovation Project, a long-term study of U.S. industrial innovation. As part of this initial phase, begun in 1982, two surveys were conducted: 1) an open-ended survey of 158 new products in the electronics industry, followed by 2) a structured survey of 118 of the original products. Both surveys used a pairwise comparison methodology. Our research identified eight broad areas that appear to be important for new product success in a high-technology environment: 1) market knowledge gained through frequent and intense customer interaction, which leads to high benefit-to-cost products; 2) and 3) planning and coordination of the new product process, especially the R&D phase; 4) emphasis on marketing and sales; 5) management support for the product throughout the development and launch stages; 6) the contribution margin of the product; 7) early market entry; 8) proximity of the new product technologies and markets to the existing strengths of the developing unit. Based on these results, a preliminary model of the new product process is proposed in the concluding section. There is nothing more difficult to plan, more doubtful of success, nor more dangerous to manage than the creation of a new system. Niccolo Machiavelli.
Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain

Posting reviews online has become an increasingly popular way for people to express opinions and sentiments toward the products bought or services received. Analyzing the large volume of online reviews available would produce useful actionable knowledge that could be of economic values to vendors and other interested parties. In this paper, we conduct a case study in the movie domain, and tackle the problem of mining reviews for predicting product sales performance. Our analysis shows that both the sentiments expressed in the reviews and the quality of the reviews have a significant impact on the future sales performance of products in question. For the sentiment factor, we propose Sentiment PLSA (S-PLSA), in which a review is considered as a document generated by a number of hidden sentiment factors, in order to capture the complex nature of sentiments. Training an S-PLSA model enables us to obtain a succinct summary of the sentiment information embedded in the reviews. Based on S-PLSFA, we propose ARSA, an Autoregressive Sentiment-Aware model for sales prediction. We then seek to further improve the accuracy of prediction by considering the quality factor, with a focus on predicting the quality of a review in the absence of user-supplied indicators, and present ARSQA, an Autoregressive Sentiment and Quality Aware model, to utilize sentiments and quality for predicting product sales performance. Extensive experiments conducted on a large movie data set confirm the effectiveness of the proposed approach.
Pedestrian intention recognition using Latent-dynamic Conditional Random Fields

We present a novel approach for pedestrian intention recognition for advanced video-based driver assistance systems using a Latent-dynamic Conditional Random Field model. The model integrates pedestrian dynamics and situational awareness using observations from a stereo-video system for pedestrian detection and human head pose estimation. The model is able to capture both intrinsic and extrinsic class dynamics. Evaluation of our method is performed on a public available dataset addressing scenarios of lateral approaching pedestrians that might cross the road, turn into the road or stop at the curbside. During experiments, we demonstrate that the proposed approach leads to better stability and class separation compared to state-of-the-art pedestrian intention recognition approaches.
RFID Systems: A Survey on Security Threats and Proposed Solutions

Low-cost Radio Frequency Identification (RFID) tags affixed to consumer items as smart labels are emerging as one of the most pervasive computing technology in history. This can have huge security implications. The present article surveys the most important technical security challenges of RFID systems. We first provide a brief summary of the most relevant standards related to this technology. Next, we present an overview about the state of the art on RFID security, addressing both the functional aspects and the security risks and threats associated to its use. Finally, we analyze the main security solutions proposed until date.
Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields

An accurate estimation of fingerprint orientation fields is an essential step in the overall fingerprint recognition process. Conventional gradient-based approaches are popular but very sensitive to noise. In this paper, we propose a novel implementation to improve the performance of gradient-based methods. The enhanced algorithm chooses the best orientation estimate from four overlapping neighborhoods of every image block, where the voting scheme is based on the reliability measures. We test our algorithm on real fingerprint images. The experiment results suggest that our enhanced algorithm achieves visibly better noise resistance with modest computation time in comparison with other gradient-based methods.
PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.
A lightweight method for virtual machine introspection

A method for the introspection of virtual machines is proposed. The main distinctive feature of this method is that it makes it possible to obtain information about the system operation using the minimum knowledge about its internal organization. The proposed approach uses rarely changing parts of the application binary interface, such as identifiers and parameters of system calls, calling conventions, and the formats of executable files. The lightweight property of the introspection method is due to the minimization of the knowledge about the system and by its high performance. The introspection infrastructure is based on the QEMU emulator, version 2.8. Presently, monitoring the file operations, processes, and API function calls are implemented. The available introspection tools (RTKDSM, Panda, and DECAF) get data for the analysis using kernel structures. All the data obtained (addresses of structures, etc.) is written to special profiles. Since the addresses and offsets strongly depend not only on the version of the operating system but also on the parameters of its assembly, these tools have to store a large number of profiles. We propose to use parts of the application binary interface because they are rarely modified and it is often possible to use one profile for a family of OSs. The main idea underlying the proposed method is to intercept the system and library function calls and read parameters and returned values. The processor provides special instructions for calling system and user defined functions. The capabilities of QEMU are extended by an instrumentation mechanism to enable one to track each executed instruction and find the instructions of interest among them. When a system call occurs, the control is passed to the system call detector that checks the number of the call and decides to which module the job should be forwarded. In the case of an API function call, the situation is similar, but the API function detector checks the function address. An introspection tool consisting of a set of modules is developed. These modules are dynamic libraries that are plugged in QEMU. The modules can interact by exchanging data.
Strip-type Grid Array Antenna with a Two-Layer Rear-Space Structure

A strip-type grid array antenna is analyzed using the finite-difference time-domain method. The space between the grid array and the ground plane, designated as the rear space, is filled with a dielectric layer and an air layer (two-layer rear-space structure). A VSWR bandwidth of approximately 13% is obtained with a maximum directivity of approximately 18 dB. The grid array exhibits a narrow beam with low side lobes. The cross-polarization component is small (-25 dB), as desired.
An Efficient Certificateless Encryption for Secure Data Sharing in Public Clouds

We propose a mediated certificateless encryption scheme without pairing operations for securely sharing sensitive information in public clouds. Mediated certificateless public key encryption (mCL-PKE) solves the key escrow problem in identity based encryption and certificate revocation problem in public key cryptography. However, existing mCL-PKE schemes are either inefficient because of the use of expensive pairing operations or vulnerable against partial decryption attacks. In order to address the performance and security issues, in this paper, we first propose a mCL-PKE scheme without using pairing operations. We apply our mCL-PKE scheme to construct a practical solution to the problem of sharing sensitive information in public clouds. The cloud is employed as a secure storage as well as a key generation center. In our system, the data owner encrypts the sensitive data using the cloud generated users' public keys based on its access control policies and uploads the encrypted data to the cloud. Upon successful authorization, the cloud partially decrypts the encrypted data for the users. The users subsequently fully decrypt the partially decrypted data using their private keys. The confidentiality of the content and the keys is preserved with respect to the cloud, because the cloud cannot fully decrypt the information. We also propose an extension to the above approach to improve the efficiency of encryption at the data owner. We implement our mCL-PKE scheme and the overall cloud based system, and evaluate its security and performance. Our results show that our schemes are efficient and practical.
Algorithm Trading using Q-Learning and Recurrent Reinforcement Learning

The reinforcement learning methods are applied to optimize the portfolios with asset allocation between risky and riskless instruments in this paper. We use classic reinforcement algorithm, Q-learning, to evaluate the performance in terms of cumulative profits by maximizing different forms of value functions: interval profit, sharp ratio, and derivative sharp ratio. Moreover, direct reinforcement algorithm (policy search) is also introduced to adjust the trading system by seeking the optimal allocation parameters using stochastic gradient ascent. We find that this direct reinforcement learning framework enables a simpler problem representation than that in value function based search algorithm, and thus avoiding Bellman's curse of dimensionality and offering great advantages in efficiency.
3D point cloud processing using spin images for object detection

The goal of this paper is to propose a step-by-step methodology of 3D point cloud processing for object detection depth1. Since the use of point clouds are relatively new for object detection, we need to build a capable framework in order to handle an enormous amount data. The framework follows a system of methods to process 3D point cloud and allows autonomous systems to detect objects. We first propose a pipeline based on spin images for the descriptor stage that proves ineffective for static object detection. We then change to a method called Signature of Histogram of OrienTation (SHOT) and successfully achieve object detection. We also perform further experiments to retrieve real time data from Quanergy's M8 long range LiDAR sensors and acquire visualizations of processed 3D point cloud.
SR-PVX: A Source Routing Based Network Virtualization Hypervisor to Enable POF-FIS Programmability in vSDNs

We design and implement a novel network virtualization hypervisor (NVH), which leverages source routing (SR) and provides network programmability based on the protocol-oblivious forwarding flow instruction set (POF-FIS), to realize protocol-independent virtual software-defined networks (vSDNs). The NVH, namely SR-PVX, uses a novel SR-based mechanism to ensure that the substrate switches can process packets from vSDNs with very small flow-table consumption. We design the network architecture, packet format, and packet processing procedure, and implement them to experimentally demonstrate that SR-PVX can control substrate POF switches to accomplish the SR-based vSDN slicing. We also perform an experiment to show that the POF-enabled vSDN created by SR-PVX can easily realize a stateful firewall with its POF-FIS programmability.
BDgraph: An R Package for Bayesian Structure Learning in Graphical Models

Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce an R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decomposable) with continuous, discrete, and mixed variables. The package efficiently implements recent improvements in the Bayesian literature, including that of Mohammadi and Wit (2015) and Mohammadi et al. (2017). To speed up computations, the computationally intensive tasks have been implemented in C++ and interfaced with R. In addition, the package contains several functions for simulation and visualization, as well as two multivariate datasets taken from the literature and are used to describe the package capabilities. The paper includes a brief overview of the statistical methods which have been implemented in the package. The main body of the paper explains how to use the package. Furthermore, we illustrate the package’s functionality in both real and artificial examples, as well as in an extensive simulation study.
Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks

This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications. & 2012 Elsevier Ltd. All rights reserved.
Analyzing Users’ Sentiment Towards Popular Consumer Industries and Brands on Twitter

Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12,898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month. We find that users tend to be most positive towards manufacturing and most negative towards service industries. In addition, they tend to be more positive or negative when interacting with brands than generally on Twitter. We also find that sentiment towards brands within an industry varies greatly and we demonstrate this using two industries as use cases. In addition, we discover that there is no strong correlation between topic sentiments of different industries, demonstrating that topic sentiments are highly dependent on the context of the industry that they are mentioned in. We demonstrate the value of such an analysis in order to assess the impact of brands on social media. We hope that this initial study will prove valuable for both researchers and companies in understanding users' perception of industries, brands and associated topics and encourage more research in this field.
GoSCAN: Decentralized scalable data clustering

Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms classically require access to the complete dataset. However, as huge amounts of data are increasingly originating from multiple, dispersed sources in distributed systems, alternative solutions are required. Furthermore, data and network dynamicity in a distributed setting demand adaptable clustering solutions that offer accurate clustering models at a reasonable pace. In this paper, we propose GoScan, a fully decentralized density-based clustering algorithm which is capable of clustering dynamic and distributed datasets without requiring central control or message flooding. We identify two major tasks: finding the core data points, and forming the actual clusters, which we execute in parallel employing gossip-based communication. This approach is very efficient, as it offers each peer enough authority to discover the clusters it is interested in. Our algorithm poses no extra burden of overlay formation in the network, while providing high levels of scalability. We also offer several optimizations to the basic clustering algorithm for improving communication overhead and processing costs. Coping with dynamic data is made possible by introducing an age factor, which gradually detects data-set changes and enables clustering updates. In our experimental evaluation, we will show that GoSCAN can discover the clusters efficiently with scalable transmission cost.
A Review on Internet of Things (IoT)

Internet, a revolutionary invention, is always transforming into some new kind of hardware and software making it unavoidable for anyone. The form of communication that we see now is either human-human or human-device, but the Internet of Things (IoT) promises a great future for the internet where the type of communication is machine-machine (M2M). This paper aims to provide a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks. Also, it describes a six-layered architecture of IoT and points out the related key challenges.
Video classification with Densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off

The current state-of-the-art in video classification is based on Bag-of-Words using local visual descriptors. Most commonly these are histogram of oriented gradients (HOG), histogram of optical flow (HOF) and motion boundary histograms (MBH) descriptors. While such approach is very powerful for classification, it is also computationally expensive. This paper addresses the problem of computational efficiency. Specifically: (1) We propose several speed-ups for densely sampled HOG, HOF and MBH descriptors and release Matlab code; (2) We investigate the trade-off between accuracy and computational efficiency of descriptors in terms of frame sampling rate and type of Optical Flow method; (3) We investigate the trade-off between accuracy and computational efficiency for computing the feature vocabulary, using and comparing most of the commonly adopted vector quantization techniques: $$k$$ k -means, hierarchical $$k$$ k -means, Random Forests, Fisher Vectors and VLAD.