nils-herrmann/clean_scirepeval huggingface dataset:
Multi-View Stereo : Redundancy Benefits for 3 D Reconstruction

This work investigates the influence of using multiple views for 3D reconstruction with respect to depth accuracy and robustness. In particular we show that multiview matching not only contributes to scene completeness, but also improves depth accuracy by improved triangulation angles. We first start by synthetic experiments on a typical aerial photogrammetric camera network and investigate how baseline (i.e. triangulation angle) and redundancy affect the depth error. Our evaluation also includes a comparison between combined pairwise triangulated and fused stereo pairs in contrast to true multiview triangulation. By analyzing the 3D uncertainty ellipsoid of triangulated points we demonstrate the clear advantage of a multiview approach over fused two view stereo algorithms. We propose an efficient dense matching algorithm that utilizes pairwise optical flow followed by a robust correspondence chaining approach. We provide evaluation results of the proposed method on ground truth data and compare its performance in contrast to a multiview plane sweep method.
Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag

Various approaches for plagiarism detection exist. All are based on more or less sophisticated text analysis methods such as string matching, fingerprinting or style comparison. In this paper a new approach called Citation-based Plagiarism Detection is evaluated using a doctoral thesis, in which a volunteer crowd-sourcing project called GuttenPlag identified substantial amounts of plagiarism through careful manual inspection. This new approach is able to identify similar and plagiarized documents based on the citations used in the text. It is shown that citation-based plagiarism detection performs significantly better than text-based procedures in identifying strong paraphrasing, translation and some idea plagiarism. Detection rates can be improved by combining citation-based with text-based plagiarism detection.
A planar quasi-yagi antenna with a new microstrip-to-CPS balun by artificial transmission lines

In this paper, we discuss a new design of quasi-Yagi antenna for 5 GHz WLAN applications. The antenna employs a new microstrip-to-CPS balun which is realized by a newly proposed planar artificial transmission line (ATL). This ATL is composed of microstrip quasi-lumped elements and their discontinuities. By simultaneously increasing the series inductance and shunt capacitance of the line section while keeping their ratio unchanged, microstrip lines with a wide variety of characteristic impedances and electrical lengths can be synthesized in a very compact form. To further improve the antenna front-to-back ratio, in this paper the dipole arms are meandered such that the truncated ground plane is relatively large compared to the radiator. The overall size of the antenna is 45 by 30 mm2. The antenna configuration, design methodology, and simulation results are investigated in detail in the following sections.
Adaptive enhancement and noise reduction in very low light-level video

A general methodology for noise reduction and contrast enhancement in very noisy image data with low dynamic range is presented. Video footage recorded in very dim light is especially targeted. Smoothing kernels that automatically adapt to the local spatio-temporal intensity structure in the image sequences are constructed in order to preserve and enhance fine spatial detail and prevent motion blur. In color image data, the chromaticity is restored and demosaicing of raw RGB input data is performed simultaneously with the noise reduction. The method is very general, contains few user-defined parameters and has been developed for efficient parallel computation using a GPU. The technique has been applied to image sequences with various degrees of darkness and noise levels, and results from some of these tests, and comparisons to other methods, are presented. The present work has been inspired by research on vision in nocturnal animals, particularly the spatial and temporal visual summation that allows these animals to see in dim light.
Anatomy of Forehead, Glabellar, Nasal and Orbital Muscles, and Their Correlation with Distinctive Patterns of Skin Lines on the Upper Third of the Face: Reviewing Concepts

The purpose of this study is to establish a relationship between the skin lines on the upper third of the face in cadavers, which represent the muscle activity in life and the skin lines achieved by voluntary contraction of the forehead, glabellar, and orbital muscles in patients. Anatomical dissection of fresh cadavers was performed in 20 fresh cadavers, 11 females and 9 males, with ages ranging from 53 to 77 years. Subcutaneous dissection identified the muscle shape and the continuity of the fibers of the eyebrow elevator and depress muscles. Subgaleal dissection identified the cutaneous insertions of the muscles. They were correlated with skin lines on the upper third of the face of the cadavers that represent the muscle activity in life. Voluntary contraction was performed by 20 voluntary patients, 13 females and 7 males, with ages ranging from 35 to 62 years. Distinct patterns of skin lines on the forehead, glabellar and orbital areas, and eyebrow displacement were identified. The frontalis exhibited four anatomical shapes with four different patterns of horizontal parallel lines on the forehead skin. The corrugator supercilii showed three shapes of muscles creating six patterns of vertical glabellar lines, three symmetrical and three asymmetrical. The orbicularis oculi and procerus had single patterns. The skin lines exhibited in voluntary contraction of the upper third of the face in patients showed the same patterns of the skin lines achieved in cadavers. Skin lines in cadavers, which are the expression of the muscle activity in life, were similar to those achieved in the voluntary contraction of patients, allowing us to assert that the muscle patterns of patients were similar to those identified in cadavers. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .
Information theory-based shot cut/fade detection and video summarization

New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses MI as well. We show that it captures satisfactorily the visual content of the shot.
Corn Disease Identification from Leaf Images Using Convolutional Neural Networks

Plant diseases are one of the major factors that can negatively impact crop yields. Therefore, we want to create an automated system that can identify plant diseases as easily as possible. Our research goal is to help crop growers who might not be experts in plant pathology be able to identify diseases as soon as they occur in order to minimize the spread of disease and the damage to the crops. We propose a very light-weight convolutional neural network architecture that is trained to classify images of corn leaves into one of the four categories: common rust disease, northern leaf blight disease, gray leaf spot disease, or healthy. The dataset used is the PlantVillage dataset taken from plantvillage.org. The dataset consists of 8506 images of corn leaves from 4 categories. We evaluated our model using 5-fold cross-validation, and we achieves an accuracy of 97.09 % on average. The result of our model is slightly worse than the performance of other well-known CNNs architectures, but the complexity of our architecture is much lower, making it more suitable for real-time inferences on resource-constrained devices such as smartphones.
CORRUPT ORGANIZATIONS OR ORGANIZATIONS OF CORRUPT INDIVIDUALS ? TWO TYPES OF ORGANIZATION-LEVEL CORRUPTION

We focus on two fundamental dimensions of corruption in organizations: (1) whether the individual or the organization is the beneficiary of the corrupt activity and (2) whether the corrupt behavior is undertaken by an individual actor or by two or more actors. We use these dimensions to define a new conceptualization of corruption at the organization level: the organization of corrupt individuals. We contrast this conceptualization with the prevailing concept of organizational corruption and develop propositions that highlight their differences.
Benefits of an active spine supported bounding locomotion with a small compliant quadruped robot

We studied the effect of the control of an active spine versus a fixed spine, on a quadruped robot running in bound gait. Active spine supported actuation led to faster locomotion, with less foot sliding on the ground, and a higher stability to go straight forward. However, we did no observe an improvement of cost of transport of the spine-actuated, faster robot system compared to the rigid spine.
Link Prediction using Supervised Learning ∗

Social network analysis has attracted much attention in recent years. Link prediction is a key research directions within this area. In this research, we study link prediction as a supervised learning task. Along the way, we identify a set of features that are key to the superior performance under the supervised learning setup. The identified features are very easy to compute, and at the same time surprisingly effective in solving the link prediction problem. We also explain the effectiveness of the features from their class density distribution. Then we compare different classes of supervised learning algorithms in terms of their prediction performance using various performance metrics, such as accuracy, precision-recall, F-values, squared error etc. with a 5-fold cross validation. Our results on two practical social network datasets shows that most of the well-known classification algorithms (decision tree, k-nn,multilayer perceptron, SVM, rbf network) can predict link with surpassing performances, but SVM defeats all of them with narrow margin in all different performance measures. Again, ranking of features with popular feature ranking algorithms shows that a small subset of features always plays a significant role in the link prediction job.
Web wallet: preventing phishing attacks by revealing user intentions

We introduce a new anti-phishing solution, the Web Wallet. The Web Wallet is a browser sidebar which users can use to submit their sensitive information online. It detects phishing attacks by determining where users intend to submit their information and suggests an alternative safe path to their intended site if the current site does not match it. It integrates security questions into the user's workflow so that its protection cannot be ignored by the user. We conducted a user study on the Web Wallet prototype and found that the Web Wallet is a promising approach. In the study, it significantly decreased the spoof rate of typical phishing attacks from 63% to 7%, and it effectively prevented all phishing attacks as long as it was used. A majority of the subjects successfully learned to depend on the Web Wallet to submit their login information. However, the study also found that spoofing the Web Wallet interface itself was an effective attack. Moreover, it was not easy to completely stop all subjects from typing sensitive information directly into web forms.
PAC-Net: Pairwise Aesthetic Comparison Network for Image Aesthetic Assessment

Image aesthetic assessment is important for finding well taken and appealing photographs but is challenging due to the ambiguity and subjectivity of aesthetic criteria. We develop the pairwise aesthetic comparison network (PAC-Net), which consists of two parts: aesthetic feature extraction and pairwise feature comparison. To alleviate the ambiguity and subjectivity, we train PAC-Net to learn the relative aesthetic ranks of two images by employing a novel loss function, called aesthetic-adaptive cross entropy loss. Then, we develop simple schemes for using PAC-Net in the tasks of aesthetic ranking and aesthetic classification, respectively. Experimental results demonstrate that PAC-Net achieves the state-of-the-art performances in both the ranking and classification applications.
Aspect Extraction with Automated Prior Knowledge Learning

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines.
Centrocestus formosanus (Digenea: Heterophyidae) encysted in the freshwater fish, Puntius brevis, from Lao PDR.

The metacercariae of Centrocestus formosanus, a minute intestinal trematode of mammals and birds, were detected in the freshwater fish, Puntius brevis, from Vientiane Municipality, Lao PDR. The metacercariae were experimentally fed to mice, and adult flukes were recovered in their small intestines 7 days later. The adult flukes were morphologically characterized by having 32 (rarely 34) circumoral spines arranged in 2 alternative rows, a large bipartite seminal vesicle, an oval-shaped ovary, and an X-shaped excretory bladder. Based on these characters, the adults were identified as Centrocestus formosanus (Nishigori, 1924). The taxonomic significance of C. formosanus, in relation to a closely related species, C. caninus (Leiper, 1913), is briefly discussed. It has been first verified by adult worm recovery that C. formosanus is prevalent in Vientiane areas of Lao PDR, taking the freshwater fish, P. brevis, as a second intermediate host.
An Offset-Fed X-Band Reflectarray Antenna Using a Modified Element Rotation Technique

An X-band circularly polarized reflectarray antenna with an offset feed is designed, fabricated, and measured. The proposed design features the use of split square ring elements with a modified element rotation technique for phase compensation, which allows the rotation of the slot instead of the element itself. This is crucial in single-layer multiband reflectarray antenna designs where it is desired to minimize the effectively occupied area by individual elements. A design flow chart of the reflectarray antenna is summarized, and the element/system design procedure is presented in details. A reflectarray prototype was measured in a near field measurement system, which operates at 8.4 GHz with a gain of 30.4 dB and an aperture efficiency of 53.7%. Both the axial ratio bandwidth (AR <; 3 dB) and the 3 dB gain bandwidth are 6%.
Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phasereversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.
A single-layer SIW slot array antenna with TE20 mode

The idea of using higher order modes is demonstrated by an SIW slot array antenna with a TE20 mode to realize boresight radiation. In order to excite the TE20 mode, an SIW hybrid coupler and a compact SIW 90° phase shifter are designed to offer the two out-of-phase outputs with identical amplitude. Besides, a CPW-SIW transition is adopted for measurement. The SIW slot array antenna with the TE20 mode is designed at X-band in a single-layer PCB. The measured bandwidth is 5.5% (9.72–10.27 GHz). The measured total efficiency is 80% at 10 GHz. Compared to a conventional SIW slot array just with a power divider, using the TE20 mode provides the merits of not only reducing the number of vias but also relaxing fabrication limit and tolerance which could be the fabrication bottleneck at high frequencies.
Chip and Skim: Cloning EMV Cards with the Pre-play Attack

EMV, also known as "Chip and PIN", is the leading system for card payments worldwide. It is used throughout Europe and much of Asia, and is starting to be introduced in North America too. Payment cards contain a chip so they can execute an authentication protocol. This protocol requires point-of-sale (POS) terminals or ATMs to generate a nonce, called the unpredictable number, for each transaction to ensure it is fresh. We have discovered two serious problems: a widespread implementation flaw and a deeper, more difficult to fix flaw with the EMV protocol itself. The first flaw is that some EMV implementers have merely used counters, timestamps or home-grown algorithms to supply this nonce. This exposes them to a "pre-play" attack which is indistinguishable from card cloning from the standpoint of the logs available to the card-issuing bank, and can be carried out even if it is impossible to clone a card physically. Card cloning is the very type of fraud that EMV was supposed to prevent. We describe how we detected the vulnerability, a survey methodology we developed to chart the scope of the weakness, evidence from ATM and terminal experiments in the field, and our implementation of proof-of-concept attacks. We found flaws in widely-used ATMs from the largest manufacturers. We can now explain at least some of the increasing number of frauds in which victims are refused refunds by banks which claim that EMV cards cannot be cloned and that a customer involved in a dispute must therefore be mistaken or complicit. The second problem was exposed by the above work. Independent of the random number quality, there is a protocol failure: the actual random number generated by the terminal can simply be replaced by one the attacker used earlier when capturing an authentication code from the card. This variant of the pre-play attack may be carried out by malware in an ATM or POS terminal, or by a man-in-the-middle between the terminal and the acquirer. We explore the design and implementation mistakes that enabled these flaws to evade detection until now: shortcomings of the EMV specification, of the EMV kernel certification process, of implementation testing, formal analysis, and monitoring customer complaints. Finally we discuss countermeasures. More than a year after our initial responsible disclosure of these flaws to the banks, action has only been taken to mitigate the first of them, while we have seen a likely case of the second in the wild, and the spread of ATM and POS malware is making it ever more of a threat.
Overview of the TAC 2010 Knowledge Base Population Track

In this paper we give an overview of the Knowledge Base Population (KBP) track at TAC 2010. The main goal of KBP is to promote research in discovering facts about entities and expanding a structured knowledge base with this information. A large source collection of newswire and web documents is provided for systems to discover information. Attributes (a.k.a. “slots”) derived from Wikipedia infoboxes are used to create the reference knowledge base (KB). KBP2010 includes the following four tasks: (1) Regular Entity Linking, where names must be aligned to entities in the KB; (2) Optional Entity linking, without using Wikipedia texts; (3) Regular Slot Filling, which requires a system to automatically discover the attributes of specified entities from the source document collection and use them to expand the KB; (4) Surprise Slot Filling, which requires a system to return answers regarding new slot types within a short time period. KBP2010 has attracted many participants (over 45 teams registered for KBP 2010 (not including the RTEKBP Validation Pilot task), among which 23 teams submitted results). In this paper we provide an overview of the task definition and annotation challenges associated with KBP2010. Then we summarize the evaluation results and discuss the lessons that we have learned based on detailed analysis.