nils-herrmann/clean_scirepeval huggingface dataset:
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform

We introduce a new low-distortion embedding of l<sub>2</sub><sup>d</sup> into l<sub>p</sub><sup>O(log n)</sup> (p=1,2), called the <i>Fast-Johnson-Linden-strauss-Transform</i>. The FJLT is faster than standard random projections and just as easy to implement. It is based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform. Sparse random projections are unsuitable for low-distortion embeddings. We overcome this handicap by exploiting the "Heisenberg principle" of the Fourier transform, ie, its local-global duality. The FJLT can be used to speed up search algorithms based on low-distortion embeddings in l<sub>1</sub> and l<sub>2</sub>. We consider the case of approximate nearest neighbors in l<sub>2</sub><sup>d</sup>. We provide a faster algorithm using classical projections, which we then further speed up by plugging in the FJLT. We also give a faster algorithm for searching over the hypercube.
A Novel Planar Endfire Circularly Polarized Antenna With Wide Axial-Ratio Beamwidth and Wide Impedance Bandwidth

A novel wideband planar endfire circularly polarized (CP) antenna with wide 3 dB axial ratio (AR) beamwidth is presented. The operation principle of the proposed CP antenna is described at first through the combination of a planar magnetic dipole and a V-shape open loop. Then, it is demonstrated how its AR beamwidth can be precisely controlled with resorting to the shape and thickness of the open loop element. Finally, the theoretical design approach is numerically verified, and the results are then experimentally validated. It is observed that a 3 dB AR beamwidth is achieved in an extremely wide angular region up to 250° within the principal elevation plane over a frequency range of 2.41-2.51 GHz. The proposed antenna has exhibited a wide impedance bandwidth of 22.23% in fraction, a 3 dB AR bandwidth of no less than 8.00% in fraction, and a simple planar profile in geometry. In addition, its endfire beam is in parallel with its plane.
Communication with Aboveground Devices in Wireless Underground Sensor Networks: An Empirical Study

Wireless Underground Sensor Networks (WUSNs) consist of wirelessly connected underground sensor nodes that communicate untethered through soil. WUSNs have the potential to impact a wide variety of novel applications including intelligent irrigation, environment monitoring, border patrol, and assisted navigation. Although its deployment is mainly based on underground sensor nodes, a WUSN still requires aboveground devices for data retrieval, management, and relay functionalities. Therefore, the characterization of the bi-directional communication between a buried node and an aboveground device is essential for the realization of WUSNs. In this work, empirical evaluations of underground-to- aboveground (UG2AG) and aboveground-to-underground (AG2UG) communication are presented. More specifically, testbed experiments have been conducted with commodity sensor motes in a real-life agricultural field. The results highlight the asymmetry between UG2AG and AG2UG communication with distinct behaviors for different burial depths. To combat the adverse effects of the change in wavelength in soil, an ultra wideband antenna scheme is deployed, which increases the communication range by more than 350% compared to the original antennas. The results also reveal that a 21% increase in the soil moisture decreases the communication range by more than 70%. To the best of our knowledge, this is the first empirical study that highlights the effects of the antenna design, burial depth, and soil moisture on both UG2AG and AG2UG communication performance. These results have a significant impact on the development of multi-hop networking protocols for WUSNs.
An immersive 3D video-conferencing system using shared virtual team user environments

Videoconferencing is going to become attractive for geo-graphically distributed team collaboration, specifically to avoid travelling and to increase flexibility. Against this background this paper presents a next generation system - a 3D videoconference providing immersive tele-presence and natural representation of all participants in a shared virtual meeting space to enhance quality of human-centred communication. This system is based on the principle of a shared virtual table environment, which guarantees correct eye contact and gesture reproduction. The key features of our system are presented and compared to other approaches like tele-cubicles. Furthermore the current system design and details of the real-time hardware and software concept are explained.
3 D Lidar-Camera Intrinsic and Extrinsic Calibration : Observability Analysis and Analytical Least Squares-based Initialization

This paper addresses the problem of estimating the intrinsic parameters of the 3D Velodyne lidar while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to alleviate this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal observability conditions, under which, it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes, as well as standard metrics of the calibration errors, are used to assess the validity of our approach.
Multilingual Language Processing From Bytes

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start positions, lengths, and labels are separate entries in our vocabulary. Because we operate directly on unicode bytes rather than languagespecific words or characters, we can analyze text in many languages with a single model. Due to the small vocabulary size, these multilingual models are very compact, but produce results similar to or better than the state-ofthe-art in Part-of-Speech tagging and Named Entity Recognition that use only the provided training datasets (no external data sources). Our models are learning “from scratch” in that they do not rely on any elements of the standard pipeline in Natural Language Processing (including tokenization), and thus can run in standalone fashion on raw text.
Automatic Labeling for Entity Extraction in Cyber Security

Timely analysis of information in cybersecurity necessitates automated information extraction from unstructured text. Unfortunately, state-of-the-art extraction methods require training data, which is unavailable in the cyber-security domain. To avoid the arduous task of handlabeling data, we develop a very precise method to automatically label text from several data sources by leveraging article-specific structured data and provide public access to corpus annotated with cyber-security entities. We then prototype a maximum entropy model that processes this corpus of auto-labeled text to label new sentences and present results showing the Collins Perceptron outperforms the MLE with LBFGS and OWL-QN optimization for parameter fitting. The main contribution of this paper is an automated technique for creating a training corpus from text related to a database. As a multitude of domains can benefit from automated extraction of domain-specific concepts for which no labeled data is available, we hope our solution is widely applicable.
Detecting and Classifying Emotion in Popular Music

Music expresses emotion. However, analyzing the emotion in music by computer is a difficult task. Some work can be found in the literature, but the results are not satisfactory. In this paper, an emotion detection and classification system for pop music is presented. The system extracts feature values from the training music files by PsySound2 and generates a music model from the resulting feature dataset by a classification algorithm. The model is then used to detect the emotion perceived in music clips. To further improve the classification accuracy, we evaluate the significance of each music feature and remove the insignificant features. The system uses a database of 195 music clips to enhance reliability and robustness.
Ebook Readers in Higher Education

Ebook readers have received a mixed press, with some hailing them as the future of reading and others believing that they will never be popular. The study outlined here aims to understand the attitudes of, and issues of importance to, lecturers in UK academia, with a view to improving the design of ebook readers for education in the future. An evaluation of five portable devices is presented, in which lecturers were given the opportunity to read an ebook and provide feedback via a questionnaire. Results are compared with concerns arising from other experiments in the same field, and recommendations are made for successful ebook design.
Join-Graph Propagation Algorithms

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes.
Bounded Ciphertext Policy Attribute Based Encryption

In a ciphertext policy attribute based encryption system, a user’s private key is associated with a set of attributes (describing the user) and an encrypted ciphertext will specify an access policy over attributes. A user will be able to decrypt if and only if his attributes satisfy the ciphertext’s policy. In this work, we present the first construction of a ciphertext-policy attribute based encryption scheme having a security proof based on a number theoretic assumption and supporting advanced access structures. Previous CP-ABE systems could either support only very limited access structures or had a proof of security only in the generic group model. Our construction can support access structures which can be represented by a bounded size access tree with threshold gates as its nodes. The bound on the size of the access trees is chosen at the time of the system setup. Our security proof is based on the standard Decisional Bilinear Diffie-Hellman assumption.
Fully Deniable Message Authentication Protocols Preserving Confidentiality

1Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA 2Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan 3Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fujian 350007, P. R. China 4Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Rd., Xitun Dist., Taichung 40724, Taiwan ∗Corresponding author: ccc@cs.ccu.edu.tw
Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web

Automatic detection of emotions like sarcasm or nastiness in online written conversation is a difficult task. It requires a system that can manage some kind of knowledge to interpret that emotional language is being used. In this work, we try to provide this knowledge to the system by considering alternative sets of features obtained according to different criteria. We test a range of different feature sets using two different classifiers. Our results show that the sarcasm detection task benefits from the inclusion of linguistic and semantic information sources, while nasty language is more easily detected using only a set of surface patterns or indicators.
A Study of Security Vulnerabilities on Docker Hub

Docker containers have recently become a popular approach to provision multiple applications over shared physical hosts in a more lightweight fashion than traditional virtual machines. This popularity has led to the creation of the Docker Hub registry, which distributes a large number of official and community images. In this paper, we study the state of security vulnerabilities in Docker Hub images. We create a scalable Docker image vulnerability analysis (DIVA) framework that automatically discovers, downloads, and analyzes both official and community images on Docker Hub. Using our framework, we have studied 356,218 images and made the following findings: (1) both official and community images contain more than 180 vulnerabilities on average when considering all versions; (2) many images have not been updated for hundreds of days; and (3) vulnerabilities commonly propagate from parent images to child images. These findings demonstrate a strong need for more automated and systematic methods of applying security updates to Docker images and our current Docker image analysis framework provides a good foundation for such automatic security update.
Dynamic Simulation-Based Action Planner for a Reconfigurable Hybrid Leg-Wheel Planetary Exploration Rover

In this paper, an action planning algorithm is presented for a reconfigurable hybrid leg–wheel mobile robot. Hybrid leg–wheel robots have recently receiving growing interest from the space community to explore planets, as they offer a solution to improve speed and mobility on uneven terrain. One critical issue connected with them is the study of an appropriate strategy to define when to use one over the other locomotion mode, depending on the soil properties and topology. Although this step is crucial to reach the full hybrid mechanism’s potential, little attention has been devoted to this topic. Given an elevation map of the environment, we developed an action planner that selects the appropriate locomotion mode along an optimal path toward a point of scientific interest. This tool is helpful for the space mission team to decide the next move of the robot during the exploration. First, a candidate path is generated based on topology and specifications’ criteria functions. Then, switching actions are defined along this path based on the robot’s performance in each motion mode. Finally, the path is rated based on the energy profile evaluated using a dynamic simulator. The proposed approach is applied to a concept prototype of a reconfigurable hybrid wheel–leg robot for planetary exploration through extensive simulations and real experiments.
Dynamic analysis and control design of optocoupler-isolated LLC series resonant converters with wide input and load variations

LLC series resonant converters for consumer or industrial electronics frequently encounter considerable changes in both input voltage and load current requirements. This paper presents theoretical and practical details involved with the dynamic analysis and control design of LLC series resonant dc-to-dc converters operating with wide input and load variations. The accuracy of dynamic analysis and validity of control design are confirmed with both computer simulations and experimental measurements.
Self-intersection Removal in Triangular Mesh Offsetting

Proposed in this paper is an efficient algorithm to remove self-intersections from the raw offset triangular mesh. The resulting regular mesh can be used in shape inflation, tool path generation, and process planning to name a few. Objective is to find the valid region set of triangles defining the outer boundary of the offset volume from the raw offset triangular mesh. Starting with a seed triangle, the algorithm grows the valid region to neighboring triangles until it reaches triangles with self-intersection. Then the region growing process crosses over the self-intersection and moves to the adjacent valid triangle. Therefore the region growing traverses valid triangles and intersecting triangles adjacent to valid triangles only. This property makes the algorithm efficient and robust, since this method omits unnecessary traversing invalid region, which usually has very complex geometric shape and contains many meaningless self-intersections.
Sentiment analysis on microblog utilizing appraisal theory

People and companies selling goods or providing services have always desired to know what people think about their products. The number of opinions on the Web has significantly increased with the emergence of microblogs. In this paper we present a novel method for sentiment analysis of a text that allows the recognition of opinions in microblogs which are connected to a particular target or an entity. This method differs from other approaches in utilizing appraisal theory, which we employ for the analysis of microblog posts. The results of the experiments we performed on Twitter showed that our method improves sentiment classification and is feasible even for such specific content as presented on microblogs.
Training Very Deep Networks

Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success. However, training becomes more difficult as depth increases, and training of very deep networks remains an open problem. Here we introduce a new architecture designed to overcome this. Our so-called highway networks allow unimpeded information flow across many layers on information highways. They are inspired by Long Short-Term Memory recurrent networks and use adaptive gating units to regulate the information flow. Even with hundreds of layers, highway networks can be trained directly through simple gradient descent. This enables the study of extremely deep and efficient architectures.
Cloud Load Balancing Techniques : A Step Towards Green Computing

Cloud computing is emerging as a new paradigm of large-scale distributed computing. It is a framework for enabling convenient, on-demand network access to a shared pool of computing resources. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. The goal of load balancing is to minimize the resource consumption which will further reduce energy consumption and carbon emission rate that is the dire need of cloud computing. This determines the need of new metrics, energy consumption and carbon emission for energy-efficient load balancing in cloud computing. This paper discusses the existing load balancing techniques in cloud computing and further compares them based on various parameters like performance, scalability, associated overhead etc. that are considered in different techniques. It further discusses these techniques from energy consumption and carbon emission perspective.
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

How to develop slim and accurate deep neural networks has become crucial for realworld applications, especially for those employed in embedded systems. Though previous work along this research line has shown some promising results, most existing methods either fail to significantly compress a well-trained deep network or require a heavy retraining process for the pruned deep network to re-boost its prediction performance. In this paper, we propose a new layer-wise pruning method for deep neural networks. In our proposed method, parameters of each individual layer are pruned independently based on second order derivatives of a layer-wise error function with respect to the corresponding parameters. We prove that the final prediction performance drop after pruning is bounded by a linear combination of the reconstructed errors caused at each layer. By controlling layer-wise errors properly, one only needs to perform a light retraining process on the pruned network to resume its original prediction performance. We conduct extensive experiments on benchmark datasets to demonstrate the effectiveness of our pruning method compared with several state-of-the-art baseline methods. Codes of our work are released at: https://github.com/csyhhu/L-OBS.
Evaluating Pattern Recognition Techniques in Intrusion Detection Systems

Pattern recognition is the discipline studying the design and operation of systems capable to recognize patterns with specific properties in data so urce . Intrusion detection, on the other hand, is in charge of identifying anomalou s activities by analyzing a data source, be it the logs of an operating system or in the network traffic. It is easy to find similarities between such research fields , and it is straightforward to think of a way to combine them. As to the descriptions abov e, we can imagine an Intrusion Detection System (IDS) using techniques prop er of the pattern recognition field in order to discover an attack pattern within the network traffic. What we propose in this work is such a system, which exp loits the results of research in the field of data mining, in order to discover poten tial attacks. The paper also presents some experimental results dealing with p erformance of our system in a real-world operational scenario.
Dissecting Contextual Word Embeddings: Architecture and Representation

Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated.