nils-herrmann/clean_scirepeval huggingface dataset:
An Algebraic Approach to Rule-Based Information Extraction

Traditional approaches to rule-based information extraction (IE) have primarily been based on regular expression grammars. However, these grammar-based systems have difficulty scaling to large data sets and large numbers of rules. Inspired by traditional database research, we propose an algebraic approach to rule-based IE that addresses these scalability issues through query optimization. The operators of our algebra are motivated by our experience in building several rule-based extraction programs over diverse data sets. We present the operators of our algebra and propose several optimization strategies motivated by the text-specific characteristics of our operators. Finally we validate the potential benefits of our approach by extensive experiments over real-world blog data.
Surgical treatment of superior cluneal nerve entrapment neuropathy.

OBJECT
Superior cluneal nerve (SCN) entrapment neuropathy is a poorly understood clinical entity that can produce low-back pain. The authors report a less-invasive surgical treatment for SCN entrapment neuropathy that can be performed with local anesthesia.



From November 2010 through November 2011, the authors performed surgery in 34 patients (age range 18-83 years; mean 64 years) with SCN entrapment neuropathy. The entrapment was unilateral in 13 patients and bilateral in 21. The mean postoperative follow-up period was 10 months (range 6-18 months). After the site was blocked with local anesthesia, the thoracolumbar fascia of the orifice was dissected with microscissors in a distal-to-rostral direction along the SCN to release the entrapped nerve.



were evaluated according to Japanese Orthopaedic Association (JOA) and Roland-Morris Disability Questionnaire (RMDQ) scores.  In all 34 patients, the SCN penetrated the orifice of the thoracolumbar fascia and could be released by dissection of the fascia. There were no intraoperative surgery-related complications. For all patients, surgery was effective; JOA and RMDQ scores indicated significant improvement (p < 0.05).



For patients with low-back pain, SCN entrapment neuropathy must be considered as a causative factor. Treatment by less-invasive surgery, with local anesthesia, yielded excellent clinical outcomes.
Hybrid Loss for Learning Single-Image-based HDR Reconstruction

This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial losses in addition to HDR-reconstruction loss. The reconstruction loss instead of MSE is more suitable for HDR since it puts more weight on both overand underexposed areas. It makes the reconstruction faithful to the input. Perceptual loss enables the networks to utilize knowledge about objects and image structure for recovering the intensity gradients of saturated and grossly quantized areas. Adversarial loss helps to select the most plausible appearance from multiple solutions. The hybrid loss that combines all the three losses is calculated in logarithmic space of image intensity so that the outputs retain a large dynamic range and meanwhile the learning becomes tractable. Comparative experiments conducted with other state-of-the-art methods demonstrated that our method produces a leap in image quality.
A Novel Efficient Pairing-Free CP-ABE Based on Elliptic Curve Cryptography for IoT

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic technique that integrates data encryption with access control for ensuring data security in IoT systems. However, the efficiency problem of CP-ABE is still a bottleneck limiting its development and application. A widespread consensus is that the computation overhead of bilinear pairing is excessive in the practical application of ABE, especially for the devices or the processors with limited computational resources and power supply. In this paper, we proposed a novel pairing-free data access control scheme based on CP-ABE using elliptic curve cryptography, abbreviated PF-CP-ABE. We replace complicated bilinear pairing with simple scalar multiplication on elliptic curves, thereby reducing the overall computation overhead. And we designed a new way of key distribution that it can directly revoke a user or an attribute without updating other users’ keys during the attribute revocation phase. Besides, our scheme use linear secret sharing scheme access structure to enhance the expressiveness of the access policy. The security and performance analysis show that our scheme significantly improved the overall efficiency as well as ensured the security.
A Unified Feature Selection Framework for Graph Embedding on High Dimensional Data

Although graph embedding has been a powerful tool for modeling data intrinsic structures, simply employing all features for data structure discovery may result in noise amplification. This is particularly severe for high dimensional data with small samples. To meet this challenge, this paper proposes a novel efficient framework to perform feature selection for graph embedding, in which a category of graph embedding methods is cast as a least squares regression problem. In this framework, a binary feature selector is introduced to naturally handle the feature cardinality in the least squares formulation. The resultant integral programming problem is then relaxed into a convex Quadratically Constrained Quadratic Program (QCQP) learning problem, which can be efficiently solved via a sequence of accelerated proximal gradient (APG) methods. Since each APG optimization is w.r.t. only a subset of features, the proposed method is fast and memory efficient. The proposed framework is applied to several graph embedding learning problems, including supervised, unsupervised, and semi-supervised graph embedding. Experimental results on several high dimensional data demonstrated that the proposed method outperformed the considered state-of-the-art methods.
ReverX: Reverse Engineering of Protocols

Communication protocols determine how network components interact with each other. Therefore, the ability to derive a speci cation of a protocol can be useful in various contexts, such as to support deeper black-box testing or e ective defense mechanisms. Unfortunately, it is often hard to obtain the speci cation because systems implement closed (i.e., undocumented) protocols, or because a time consuming translation has to be performed, from the textual description of the protocol to a format readable by the tools. To address these issues, we propose a new methodology to automatically infer a speci cation of a protocol from network traces, which generates automata for the protocol language and state machine. Since our solution only resorts to interaction samples of the protocol, it is well-suited to uncover the message formats and protocol states of closed protocols and also to automate most of the process of specifying open protocols. The approach was implemented in ReverX and experimentally evaluated with publicly available FTP traces. Our results show that the inferred speci cation is a good approximation of the reference speci cation, exhibiting a high level of precision and recall.
Microstrip Bandpass Filter Using Degenerate Modes of a Novel Meander Loop Resonator

A novel type of dual-mode microstrip bandpass filter using degenerate modes of a meander loop resonator has been developed for miniaturization of high selectivity narrowband microwave bandpass filters. A filter of this type having a 2.5% bandwidth at 1.58 GHz was designed and fabricated. The measured filter performance is presented.
Zirconia crowns for rehabilitation of decayed primary incisors: an esthetic alternative.

Esthetic management of extensively decayed primary maxillary anterior teeth requiring full coronal coverage restoration is usually challenging to the pediatric dentists especially in very young children. Many esthetic options have been tried over the years each having its own advantages, disadvantages and associated technical, functional or esthetic limitations. Zirconia crowns have provided a treatment alternative to address the esthetic concerns and ease of placement of extra-coronal restorations on primary anterior teeth. The present article presents a case where grossly decayed maxillary primary incisors were restored esthetically and functionally with ready made zirconia crowns (ZIRKIZ, HASS Corp; Korea). After endodontic treatment the decayed teeth were restored with zirconia crowns. Over a 30 months period, the crowns have demonstrated good retention and esthetic results. Dealing with esthetic needs in children with extensive loss of tooth structure, using Zirconia crowns would be practical and successful. The treatment described is simple and effective and represents a promising alternative for rehabilitation of decayed primary teeth.
Classification of human brain attention focused on meditation, effected by L-theanine acid in Oolong tea

L-theanine acid is an amino acid in tea which affects mental state directly. Along with other most popular tea types; white, green, and black tea, Oolong tea also has sufficient L-theanine to relax the human brain. It apparently can reduce the concern, blood pressure, dissolve the fat in the arteries, and especially slow aging by substances against free radicals. Therefore, this research study about the effect of L-theanine in Oolong Tea on human brain's attention focused on meditation during book reading state rely on each person by using electroencephalograph (EEG) and K-means clustering. An electrophysiological monitoring will properly measure the voltage fluctuation of Alpha rhythm for the understanding of higher attention processes of human brain precisely. K-means clustering investigates and defines that the group of converted waves data has a variable effective level rely on each classified group, which female with lower BMI has a higher effect on L-theanine than male apparently. In conclusion, the results promise the L-theanine significantly affects on meditation by increasing in Alpha waves on each person that beneficially supports production proven of Oolong tea in the future.
Scalable regular expression matching on data streams

Regular Expression (RE) matching has important applications in the areas of XML content distribution and network security. In this paper, we present the end-to-end design of a high performance RE matching system. Our system combines the processing efficiency of Deterministic Finite Automata (DFA) with the space efficiency of Non-deterministic Finite Automata (NFA) to scale to hundreds of REs. In experiments with real-life RE data on data streams, we found that a bulk of the DFA transitions are concentrated around a few DFA states. We exploit this fact to cache only the frequent core of each DFA in memory as opposed to the entire DFA (which may be exponential in size). Further, we cluster REs such that REs whose interactions cause an exponential increase in the number of states are assigned to separate groups -- this helps to improve cache hits by controlling the overall DFA size.
 To the best of our knowledge, ours is the first end-to-end system capable of matching REs at high speeds and in their full generality. Through a clever combination of RE grouping, and static and dynamic caching, it is able to perform RE matching at high speeds, even in the presence of limited memory. Through experiments with real-life data sets, we show that our RE matching system convincingly outperforms a state-of-the-art Network Intrusion Detection tool with support for efficient RE matching.
Deep Learning Based Food Recognition

Food is the cornerstone of people’s life. Nowadays more and more people cares about the dietary intake since unhealthy diet leads to numerous diseases, like obesity and diabetes. Accurately labelling food items is significantly essential to keep fit and live a healthy life. However, currently referring to nutrition experts or Amazon Mechanical Turk is the only way to recognize the food items. In this project, we propose a deep learning based food image recognition algorithms to improve the accuracy of dietary assessment. We applied convolutional neural network to solve this problem. Especially GoogLeNet Inception V3 and GoogLeNet Inception-ResNet were performed for classifying the food images on the slim version of Tensorflow. Optimizer like RMSprop or Adamare also tried to optimize the model. In this project, we will work to solve two key issues: 1. What are the methods to improve the accuracy of GoogLeNet Inception V3 and GoogLeNet Inception-ResNet? How accurate they can finally achieve? 2. How do these two GoogLeNet based methods compared with other methods like VGG and SVM by means of Top 1 accuracy and Top 5 accuracy?
Neural Machine Translation with External Phrase Memory

In this paper, we propose phraseNet, a neural machine translator with a phrase memory which stores phrase pairs in symbolic form, mined from corpus or specified by human experts. For any given source sentence, phraseNet scans the phrase memory to determine the candidate phrase pairs and integrates tagging information in the representation of source sentence accordingly. The decoder utilizes a mixture of word-generating component and phrase-generating component, with a specifically designed strategy to generate a sequence of multiple words all at once. The phraseNet not only approaches one step towards incorporating external knowledge into neural machine translation, but also makes an effort to extend the word-by-word generation mechanism of recurrent neural network. Our empirical study on Chinese-to-English translation shows that, with carefully-chosen phrase table in memory, phraseNet yields 3.45 BLEU improvement over the generic neural machine translator.
A Comprehensive Analysis of AM–AM and AM–PM Conversion in an LDMOS RF Power Amplifier

In this paper, a Volterra analysis built on top of a normal harmonic balance simulation is used for a comprehensive analysis of the causes of AM-PM distortion in a LDMOS RF power amplifier (PA). The analysis shows that any nonlinear capacitors cause AM-PM. In addition, varying terminal impedances may pull the matching impedances and cause phase shift. The AM-PM is also affected by the distortion that is mixed down from the second harmonic. As a sample circuit, an internally matched 30-W LDMOS RF PA is used and the results are compared to measured AM-AM, AM-PM and large-signal S11.
Review of halftoning techniques

Digital halftoning remains an active area of research with a plethora of new and enhanced methods. While several fine overviews exist, this purpose of this paper is to review retrospectively the basic classes of techniques. Halftoning algorithms are presented by the nature of the appearance of resulting patterns, including white noise, recursive tessellation, the classical screen, and blue noise. The metric of radially averaged power spectra is reviewed, and special attention is paid to frequency domain characteristics. The paper concludes with a look at the components that comprise a complete image rendering system. In particular when the number of output levels is not restricted to be a power of 2. A very efficient means of multilevel dithering is presented based on scaling order-dither arrays. The case of real-time video rendering is considered where the YUV-to-RGB conversion is incorporated in the dithering system. Example illustrations are included for each of the techniques described.
A Self-Checking TMR Voter for Increased Reliability Consensus Voting in FPGAs

Nanoscale field-programmable gate array (FPGA) circuits are more prone to radiation-induced effects in harsh environments because of their memory-based reconfigurable logic fabric. Consequently, for mission- or safety-critical applications, appropriate fault-tolerance techniques are widely employed. The most commonly applied technique for hardening FPGAs against radiation-induced upsets is triple modular redundancy (TMR). Voting circuits used in TMR implementations are decentralized and consensus is calculated from the redundant outputs off-chip. However, if there are an insufficient number of pins available on the chip carrier, the TMR system must be reduced to an on-chip unprotected simplex system, meaning voters used at those locations become single point of failure. In this paper, we propose a self-checking voting circuit for increased reliability consensus voting on FPGAs. Through fault injection and reliability analyses, we demonstrate that the proposed voter, which utilizes redundant voting copies, is approximately 26% more reliable than an unprotected simplex voter when reliability values of voters over normalized time are averaged.
Using cyber threat intelligence in SDN security

As the number and variety of cyber threats increase, it becomes more critical to share intelligence information in a fast and efficient manner. However, current cyber threat intelligence data do not contain sufficient information about how to specify countermeasures or how institutions should apply countermeasures automatically on their networks. A flexible and agile network architecture is required in order to determine and deploy countermeasures quickly. Software-defined networks facilitate timely application of cyber security measures thanks to their programmability. In this work, we propose a novel model for producing software-defined networking-based solutions against cyber threats and configuring networks automatically using risk analysis. We have developed a prototype implementation of the proposed model and demonstrated the applicability of the model. Furthermore, we have identified and presented future research directions in this area.
Large-scale electrophysiology: Acquisition, compression, encryption, and storage of big data

The use of large-scale electrophysiology to obtain high spatiotemporal resolution brain recordings (>100 channels) capable of probing the range of neural activity from local field potential oscillations to single-neuron action potentials presents new challenges for data acquisition, storage, and analysis. Our group is currently performing continuous, long-term electrophysiological recordings in human subjects undergoing evaluation for epilepsy surgery using hybrid intracranial electrodes composed of up to 320 micro- and clinical macroelectrode arrays. DC-capable amplifiers, sampling at 32kHz per channel with 18-bits of A/D resolution are capable of resolving extracellular voltages spanning single-neuron action potentials, high frequency oscillations, and high amplitude ultra-slow activity, but this approach generates 3 terabytes of data per day (at 4 bytes per sample) using current data formats. Data compression can provide several practical benefits, but only if data can be compressed and appended to files in real-time in a format that allows random access to data segments of varying size. Here we describe a state-of-the-art, scalable, electrophysiology platform designed for acquisition, compression, encryption, and storage of large-scale data. Data are stored in a file format that incorporates lossless data compression using range-encoded differences, a 32-bit cyclically redundant checksum to ensure data integrity, and 128-bit encryption for protection of patient information.
Multi-band multi-mode SDR radar platform

This paper presents the design results of the multi-band, multi-mode software-defined radar (SDR) system. The SDR platform consists of a multi-band RF modules of S, X, K-bands, and a multi-mode digital modules with a waveform generator for CW, Pulse, FMCW, and LFM Chirp as well as reconfigurable SDR-GUI software module for user interface. This platform can be used for various applications such as security monitoring, collision avoidance, traffic monitoring, and a radar imaging.
Comparison of Firewall and Intrusion Detection System

Firewall has many shortages, such as it cannot keep away interior attacks, it cannot provide a consistent security strategy, and it has a single bottleneck spot and invalid spot, etc. Intrusion Detection System (IDS) also has many defects, such as low detection ability, lack of effective response mechanism, poor manageability, etc. If firewall and IDS are integrated, the cooperation of them can implement the network security to a great extent. on the one hand, IDS monitors the network, provides a realtime detection of attacks from the interior and exterior, and automatically informs firewall and dynamically alters the rules of firewall once an attack is found; on the other hand, firewall loads dynamic rules to hold up the intrusion, controls the data traffic of IDS and provides the security protection of IDS Keywords— Protocol, Detection, Generation, Prevention,
Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition

Driver decisions and behaviors regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behavior and assist in driving tasks according to their status. In this paper, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this paper. The tasks include normal driving, left-, right-, and rear-mirror checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks, while the other three tasks are regarded as dangerous and distracting tasks. The driver behavior signals collected from the Kinect consist of a color and depth image of the driver inside the vehicle cabin. In addition, 3-D head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features for behavior recognition is evaluated using random forests and maximal information coefficient methods. Next, a feedforward neural network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.