nils-herrmann/clean_scirepeval huggingface dataset:
Routing a mixed fleet of electric and conventional vehicles

In this paper, we propose the Electric Vehicle Routing Problem with Time Windows and Mixed Fleet (E-VRPTWMF) to optimize the routing of a mixed fleet of electric commercial vehicles (ECVs) and conventional internal combustion commercial vehicles (ICCVs). Contrary to existing routing models for ECVs, which assume energy consumption to be a linear function of traveled distance, we utilize a realistic energy consumption model that incorporates speed, gradient and cargo load distribution. This is highly relevant in the context of ECVs because energy consumption determines the maximal driving range of ECVs and the recharging times at stations. To address the problem, we develop an Adaptive Large Neighborhood Search algorithm that is enhanced by a local search for intensification. In numerical studies on newly designed E-VRPTWMF test instances, we investigate the effect of considering the actual load distribution on the structure and quality of the generated solutions. Moreover, we study the influence of different objective functions on solution attributes and on the contribution of ECVs to the overall routing costs. Finally, we demonstrate the performance of the developed algorithm on benchmark instances of the related problems VRPTW and E-VRPTW.
Feature Correlation Hypergraph: Exploiting High-order Potentials for Multimodal Recognition

In computer vision and multimedia analysis, it is common to use multiple features (or multimodal features) to represent an object. For example, to well characterize a natural scene image, we typically extract a set of visual features to represent its color, texture, and shape. However, it is challenging to integrate multimodal features optimally. Since they are usually high-order correlated, e.g., the histogram of gradient (HOG), bag of scale invariant feature transform descriptors, and wavelets are closely related because they collaboratively reflect the image texture. Nevertheless, the existing algorithms fail to capture the high-order correlation among multimodal features. To solve this problem, we present a new multimodal feature integration framework. Particularly, we first define a new measure to capture the high-order correlation among the multimodal features, which can be deemed as a direct extension of the previous binary correlation. Therefore, we construct a feature correlation hypergraph (FCH) to model the high-order relations among multimodal features. Finally, a clustering algorithm is performed on FCH to group the original multimodal features into a set of partitions. Moreover, a multiclass boosting strategy is developed to obtain a strong classifier by combining the weak classifiers learned from each partition. The experimental results on seven popular datasets show the effectiveness of our approach.
Hackers - Heroes of the Computer Revolution

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A Novel Intrusion Detection Method Based on Principle Component Analysis in Computer Security

Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer security in recent years. In this paper, a new intrusion detection method based on Principle Component Analysis (PCA) with low overhead and high efficiency is presented. System call data and command sequences data are used as information sources to validate the proposed method. The frequencies of individual system calls in a trace and individual commands in a data block are computed and then data column vectors which represent the traces and blocks of the data are formed as data input. PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for real-time intrusion detection.
Cascade Region Proposal and Global Context for Deep Object Detection

Deep region-based object detector consists of a region proposal step and a deep object recognition step. In this paper, we make significant improvements on both of the two steps. For region proposal we propose a novel lightweight cascade structure which can effectively improve RPN proposal quality. For object recognition we re-implement global context modeling with a few modifications and obtain a performance boost (4.2% mAP gain on the ILSVRC 2016 validation set). Besides, we apply the idea of pre-training extensively and show its importance in both steps. Together with common training and testing tricks, we improve Faster R-CNN baseline by a large margin. In particular, we obtain 87.9% mAP on the PASCAL VOC 2012 test set, 65.3% on the ILSVRC 2016 test set and 36.8% on the COCO test-std set.
Hematocolpos causing severe urinary retention in an adolescent girl with imperforate hymen: an uncommon presentation

Hematocolpos is rarely presented as a pelvic mass which mechanically compresses the bladder and the urethra thereby causing urinary retention. A 12-year-old girl referred with the history of lower abdominal pain and retention of urine for 24 h. The patient had not started her menses yet. Three weeks before she also complained of discomfort on passing urine, frequency and urgency and was taken to a local outpatient clinic where she was given antibiotics with the diagnosis of urinary tract infection, she had also the history of intermittent urinary catheterization (three times before) in an emergency department because of acute severe urinary retention. Transabdominal ultrasonography revealed a pelvic semi-solid mass suggestive of hematocolpos. Pelvic examination revealed a pale blue imperforate hymen bulging from the vaginal introitus outwards. A cruciate incision was made over the hymen. Postoperative period was uneventful. In case of acute severe urinary retention in an adolescent girl, the clinicians should keep in mind that imperforate hymen may be a causative factor and this condition may easily be treated surgically.
Solving Sudoku Using Probabilistic Graphical Models

Sudoku is a popular number puzzle. Here, we model the puzzle as a probabilistic graphical model and drive a modification to the well-known sum-product and max-product message passing to solve the puzzle. In addition, we propose a Sudoku solver utilizing a combination of message passing and Sinkhorn balancing and show that as Sudoku puzzles become larger, the impact of loopy propagation does not increase.
First-Party Content and Coordination in Two-Sided Markets

T strategic use of first-party content by two-sided platforms is driven by two key factors: the nature of buyer and seller expectations (favorable versus unfavorable) and the nature of the relationship between first-party content and third-party content (complements or substitutes). Platforms facing unfavorable expectations face an additional constraint: their prices and first-party content investment need to be such that low (zero) participation equilibria are eliminated. This additional constraint typically leads them to invest more (less) in first-party content relative to platforms facing favorable expectations when firstand third-party content are substitutes (complements). These results hold with both simultaneous and sequential entry of the two sides. With two competing platforms—incumbent facing favorable expectations and entrant facing unfavorable expectations— and multi-homing on one side of the market, the incumbent always invests (weakly) more in first-party content relative to the case in which it is a monopolist.
A highly pitch scalable 3D vertical gate (VG) NAND flash decoded by a novel self-aligned independently controlled double gate (IDG) string select transistor (SSL)

Despite vertical stacking, the lateral scaling of 3D NAND Flash is critically important because otherwise >;16 stacking layers are needed to be cost competitive to 20nm 2D NAND. In this work, we propose a 3D vertical gate (VG) NAND using a self-aligned independently controlled double gate (IDG) string select transistor (SSL) decoding method. The IDG SSL provides excellent program inhibit and read selection without any penalty of cell size increase, making our 3D VG NAND cell as scalable as conventional 2D NAND. We present the world's first <; 50nm (37.5nm) half-pitch 3D NAND. The BL decoding and page operation methods are illustrated in detail. This highly pitch scalable VG with IDG SSL approach provides a very cost competitive 3D NAND.
Constructing Legal Arguments with Rules in the Legal Knowledge Interchange Format (LKIF)

The Legal Knowledge Interchange Format (LKIF), being developed in the European ESTRELLA project, defines a knowledge representation language for arguments, rules, ontologies, and cases in XML. In this article, the syntax and argumentation-theoretic semantics of the LKIF rule language is presented and illustrated with an example based on German family law. This example is then applied to show how LKIF rules can be used with the Carneades argumentation system to construct, evaluate and visualize arguments about a legal case.
Anatomical study of the greater palatine artery and related structures of the palatal vault: considerations for palate as the subepithelial connective tissue graft donor site

Palate is considered as a tissue graft donor site for dental surgical procedures. Therefore, the aim of this study was to investigate the anatomy of palatal structures, such as greater palatine artery, greater palatine foramen, and incisive fossa, in order to consider their topography at planning the graft dimensions and reduce the potential risk of injury of greater palatine artery. Direct inspection of 41 Thai cadavers was performed. The results showed the statistically significant differences as for the length of female and male palates (p = 0.017); however, vertical measurements were equally distributed in examined population. Main location of greater palatine foramen was palatal to the second molar (35.7%), as well as, interproximal to the second and third molars (35.7%) in women, and palatal to the second molar in men (65%). GPA was branching most frequently at the level of first premolar (38%) and at first and second molars together (43%) in women. In men, the branching on the alveolar process side was commonly observed at the level of first and second premolars together (56%), and at the level of second and third molars together (32%). In the area between maxillary first premolar and second molar, it appeared possible to harvest a connective tissue graft measuring at least 5 mm in height. The results of this research will provide the useful data for other comparative studies and for assisting periodontologists in planning the dimensions and harvesting the subepithelial connective tissue grafts from palate.
Deeply-Learned Feature for Age Estimation

Human age provides key demographic information. It is also considered as an important soft biometric trait for human identification or search. Compared to other pattern recognition problems (e.g., object classification, scene categorization), age estimation is much more challenging since the difference between facial images with age variations can be more subtle and the process of aging varies greatly among different individuals. In this work, we investigate deep learning techniques for age estimation based on the convolutional neural network (CNN). A new framework for age feature extraction based on the deep learning model is built. Compared to previous models based on CNN, we use feature maps obtained in different layers for our estimation work instead of using the feature obtained at the top layer. Additionally, a manifold learning algorithm is incorporated in the proposed scheme and this improves the performance significantly. Furthermore, we also evaluate different classification and regression schemes in estimating age using the deep learned aging pattern (DLA). To the best of our knowledge, this is the first time that deep learning technique is introduced and applied to solve the age estimation problem. Experimental results on two datasets show that the proposed approach is significantly better than the state-of-the-art.
A benchmark 3D fingerprint database

Fingerprint biometric is one of the most successful biometrics applied in both forensic law enforcement and security applications. Recent developments in fingerprint acquisition technology have resulted in touchless live scan devices that generate 3D representation of fingerprints, and thus can overcome the deformation and smearing problems caused by conventional contact-based acquisition techniques. However, there are yet no 3D full fingerprint databases with their corresponding 2D prints for fingerprint biometric research. This paper presents a 3D fingerprint database we have established in order to investigate the 3D fingerprint biometric comprehensively. It consists of 3D fingerprints as well as their corresponding 2D fingerprints captured by two commercial fingerprint scanners from 150 subjects in Australia. Besides, we have tested the performance of 2D fingerprint verification, 3D fingerprint verification, and 2D to 3D fingerprint verification. The results show that more work is needed to improve the performance of 2D to 3D fingerprint verification. In addition, the database is expected to be released publicly in late 2014.
Two Practical Rhetorical Structure Theory Parsers

We describe the design, development, and API for two discourse parsers for Rhetorical Structure Theory. The two parsers use the same underlying framework, but one uses features that rely on dependency syntax, produced by a fast shift-reduce parser, whereas the other uses a richer feature space, including both constituentand dependency-syntax and coreference information, produced by the Stanford CoreNLP toolkit. Both parsers obtain state-of-the-art performance, and use a very simple API consisting of, minimally, two lines of Scala code. We accompany this code with a visualization library that runs the two parsers in parallel, and displays the two generated discourse trees side by side, which provides an intuitive way of comparing the two parsers.
A High Precision Bandgap Reference Used in Power Management ICs

A 2.5V high precision BiCMOS bandgap reference with supply voltage range of 6V to 18V was proposed and realized. It could be applied to lots of Power Management ICs (Intergrated Circuits) due the high voltage. By introducing a preregulated current source, the PSRR (Power Supply Rejection Ratio) of 103dB at low frequency and the line regulation of 26.7μV/V was achieved under 15V supply voltage at ambient temperature of 27oC. Moreover, if the proper resistance trimming is implemented, the temperature coefficient could be reduced to less than 16.4ppm/oC. The start up time of the reference voltage could also be decreased with an additional bipolar and capacitor.
Understanding the Relationship between Human Behavior and Susceptibility to Cyber Attacks: A Data-Driven Approach

Despite growing speculation about the role of human behavior in cyber-security of machines, concrete data-driven analysis and evidence have been lacking. Using Symantec’s WINE platform, we conduct a detailed study of 1.6 million machines over an 8-month period in order to learn the relationship between user behavior and cyber attacks against their personal computers. We classify users into 4 categories (gamers, professionals, software developers, and others, plus a fifth category comprising everyone) and identify a total of 7 features that act as proxies for human behavior. For each of the 35 possible combinations (5 categories times 7 features), we studied the relationship between each of these seven features and one dependent variable, namely the number of attempted malware attacks detected by Symantec on the machine. Our results show that there is a strong relationship between several features and the number of attempted malware attacks. Had these hosts not been protected by Symantec’s anti-virus product or a similar product, they would likely have been infected. Surprisingly, our results show that software developers are more at risk of engaging in risky cyber-behavior than other categories.
VA2: A Visual Analytics Approach for Evaluating Visual Analytics Applications

Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications.
Container Orchestration for Scientific Workflows

Recently, Linux container technology has been gaining attention as it promises to transform the way software is developed and deployed. The portability and ease of deployment makes Linux containers an ideal technology to be used in scientific workflow platforms. AWE/Shock is a scalable data analysis platform designed to execute data intensive scientific workflows. Recently we introduced Skyport, an extension to AWE/Shock, that uses Docker container technology to orchestrate and automate the deployment of individual workflow tasks onto the worker machines. The installation of software in independent execution environments for each task reduces complexity and offers an elegant solution to installation problems such as library version conflicts. The systematic use of isolated execution environments for workflow tasks also offers a convenient and simple mechanism to reproduce scientific results.
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs

This paper presents results of our experiments using the Ubuntu Dialog Corpus – the largest publicly available multi-turn dialog corpus. First, we use an in-house implementation of previously reported models to do an independent evaluation using the same data. Second, we evaluate the performances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, we create an ensemble by averaging predictions of multiple models. The ensemble further improves the performance and it achieves a state-of-the-art result for this dataset. Finally, we discuss our future plans using this corpus.
Low-pass and high-pass microwave filters with transmission zero based on metamaterial concepts

In this work, highly selective filters based on periodic arrays of electrically small resonators are pointed out. The high-pass filters are implemented in microstrip technology by etching complementary split ring resonators (CSRRs), or complementary spiral resonators (CSRs), in the ground plane, and series capacitive gaps, or interdigital capacitors, in the signal strip. The structure exhibits a composite right/left handed (CRLH) behavior and, by properly tuning the geometry of the elements, a high pass response with a sharp transition band is obtained. The low-pass filters, also implemented in microstrip technology, are designed by cascading open complementary split ring resonators (OCSRRs) in the signal strip. These low pass filters do also exhibit a narrow transition band. The high selectivity of these microwave filters is due to the presence of a transmission zero. Since the resonant elements are small, filter dimensions are compact. Several prototype device examples are reported in this paper.
Philippine currency paper bill counterfeit detection through image processing using Canny Edge Technology

Image edge information is essentially one of the most significant information in an image, which can describe the target outline, its relative position within the target area, and other important information. Edge detection is one of the most important process in image processing, and the detection results directly affects the image analysis. Traditional edge detection algorithms are accomplished through detecting the maximum value of the first derivative or zero crossing of the second derivative. Though it seems the representative first order differential operators have known advantages like simple computation, speed and ease of implementation, they are more sensitive to noise and their detection effect are not perfect in most engineering application. Schemes for counterfeit detection has been continuously evolving through various techniques and algorithms run on machines and devices. This paper presents image enhancement and image sensing through the use of Canny Edge Technology. Moreover, counterfeit detection on selected Philippine banknotes was achieved by incorporating a distinct security feature known as Optically Variable Device (OVD) patch, as an improvement to the traditional three-way detection of bills (watermark, security thread, see-through marks)A MATLAB GUI program was developed that acquired and processed the image through Canny Edge Technology. The Canny Edge algorithm proved a robust method in detecting fake bills through OVD security features, revealing statistically significant detection proportion under a 5% level of significant for the four cases under test.