nils-herrmann/clean_scirepeval huggingface dataset:
Can soft biometric traits assist user recognition

Biometrics is rapidly gaining acceptance as the technology that can meet the ever increasing need for security in critical applications. Biometric systems automatically recognize individuals based on their physiological and behavioral characteristics. Hence, the fundamental requirement of any biometric recognition system is a human trait having several desirable properties like universality, distinctiveness, permanence, collectability, acceptability, and resistance to circumvention. However, a human characteristic that possesses all these properties has not yet been identified. As a result, none of the existing biometric systems provide perfect recognition and there is a scope for improving the performance of these systems. Although characteristics like gender, ethnicity, age, height, weight and eye color are not unique and reliable, they provide some information about the user. We refer to these characteristics as “soft” biometric traits and argue that these traits can complement the identity information provided by the primary biometric identifiers like fingerprint and face. This paper presents the motivation for utilizing soft biometric information and analyzes how the soft biometric traits can be automatically extracted and incorporated in the decision making process of the primary biometric system. Preliminary experiments were conducted on a fingerprint database of 160 users by synthetically generating soft biometric traits like gender, ethnicity, and height based on known statistics. The results show that the use of additional soft biometric user information significantly improves ( ≈ 6%) the recognition performance of the fingerprint biometric system.
A Compact Single Conductor Transmission Line Launcher for Telemetry in Borehole Drilling

A very compact and conformal launcher for single conductor transmission lines (SCTLs) at the HF-band is presented. Also the concept of using drill pipes as the conductor of SCTL is introduced for the first time to satisfy the need for cost-effective and real-time data communication in drilling process. It is shown that a TM surface wave can be launched effectively making use of the drilling mud around the drill pipe. Provision of TM surface wave launchers that can fit within the borehole at both ends makes it feasible to transmit data over the drill pipe from downhole to the surface and vice versa. The launcher is composed of an open ended helical conductor which concentrically surrounds the drill pipe with appropriate pitch and length forming a compact surface wave launcher. Keeping its dimension smaller than  $0.005\lambda \times 0.005\lambda \times 0.03\lambda $ , the proposed launcher provides 2% fractional bandwidth at low HF-band.
Information dynamics based self-adaptive reservoir for delay temporal memory tasks

Recurrent neural networks of the reservoir computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of variable temporal memory. Specifically for delayed response tasks involving the transient memorization of information (temporal memory), self-adaptation in RC is crucial for generalization to varying delays. In this work using information theory, we combine a generalized intrinsic plasticity rule with a local information dynamics based schema of reservoir neuron leak adaptation. This allows the RC network to be optimized in a self-adaptive manner with minimal parameter tuning. Local active information storage, measured as the degree of influence of previous activity on the next time step activity of a neuron, is used to modify its leak-rate. This results in RC network with non-uniform leak rate which depends on the time scales of the incoming input. Intrinsic plasticity (IP) is aimed at maximizing the mutual information between each neuron’s input and output while maintaining a mean level of activity (homeostasis). Experimental results on two standard benchmark tasks confirm the extended performance of this system as compared to the static RC (fixed leak and no IP) and RC with only IP. In addition, using both a simulated wheeled robot and a more complex physical hexapod robot, we demonstrate the ability of the system to achieve long temporal memory for solving a basic T-shaped maze navigation task with varying delay time scale.
Evaluation of tracking algorithm performance without ground-truth data

Visual tracking is a topic on which a lot of scientific work has been carried out in the last years. An important aspect of tracking algorithms is the performance evaluation, which has been carried out typically through hand-labeled ground-truth data. Since the manual generation of ground truth is a time-consuming, error-prone and tedious task, recently many researchers have focused their attention on self-evaluation techniques for performance analysis. In this paper we propose a novel tool that enables image processing researchers to test the performance of tracking algorithms without resorting to hand-labeled ground truth data. The proposed approach consists of computing a set of features describing shape, appearance and motion of the tracked objects and combining them through a naive Bayesian classifier, in order to obtain a probability score representing the overall evaluation of each tracking decision. The method was tested on three different targets (vehicles, humans and fish) with three different tracking algorithms and the results show how this approach is able to reflect the quality of the performed tracking.
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.
VM Placement Strategies for Cloud Scenarios

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP) that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSP technique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
EigenTrustp++: Attack resilient trust management

This paper argues that trust and reputation models should take into account not only direct experiences (local trust) and experiences from the circle of “friends”, but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrust, one of the most popular reputation systems to date, and identify the inherent vulnerabilities of EigenTrust in terms of its local trust vector, its global aggregation of local trust values, and its eigenvector based reputation propagating model. Then we present EigenTrust++, an attack resilient trust management scheme. EigenTrust++ extends the eigenvector based reputation propagating model, the core of EigenTrust, and counters each of vulnerabilities identified with alternative methods that are by design more resilient to dishonest feedbacks and sparse network connectivity under four known attack models. We conduct extensive experimental evaluation on EigenTrust++, and show that EigenTrust++ can significantly outperform EigenTrust in terms of both performance and attack resilience in the presence of dishonest feedbacks and sparse network connectivity against four representative attack models.
Genome engineering empowers the diatom Phaeodactylum tricornutum for biotechnology.

Diatoms, a major group of photosynthetic microalgae, have a high biotechnological potential that has not been fully exploited because of the paucity of available genetic tools. Here we demonstrate targeted and stable modifications of the genome of the marine diatom Phaeodactylum tricornutum, using both meganucleases and TALE nucleases. When nuclease-encoding constructs are co-transformed with a selectable marker, high frequencies of genome modifications are readily attained with 56 and 27% of the colonies exhibiting targeted mutagenesis or targeted gene insertion, respectively. The generation of an enhanced lipid-producing strain (45-fold increase in triacylglycerol accumulation) through the disruption of the UDP-glucose pyrophosphorylase gene exemplifies the power of genome engineering to harness diatoms for biofuel production.
Understanding sequence and reply relationships within email conversations: a mixed-model visualization

It has been proposed that email clients could be improved if they presented messages grouped into conversations. An email conversation is the tree of related messages that arises from the use of the reply operation. We propose two models of conversation. The first model characterizes a conversation as a chronological sequence of messages; the second as a tree based on the reply relationship. We show how existing email clients and prior research projects implicitly support each model to a greater or lesser degree depending on their design, but none fully supports both models simultaneously. We present a mixed-model visualization that simultaneously presents sequence and reply relationships among the messages of a conversation, making both visible at a glance. We describe the integration of the visualization into a working prototype email client. A usability study indicates that the system meets our usability goals and verifies that the visualization fully conveys both types of relationships within the messages of an email conversation.
Gaussian Neural Networks for Glass Bottles Inspection: A Learning Procedure

In glass bottle inspection, the defects detection is of first importance. For online system detection, high speed and robust detection of faults are highly required. Neural networks have recently, and successfully, been applied to fault detection in many manufacturing processes. In this study, a Gaussian neural network, an extension of the RBF network, trained through a competitive algorithm, has been chosen for fault detection. Four parameters extracted from images of the bottles are used as inputs of the network. The number of Gaussian units is adjusted by an informational criterion. Experimental results show that the performance of this network are better than classical parametric and non parametric classifiers.
Mixed Reality Serious Games and Gamification for smart education

The appeal of Mixed Reality (MR) digital games arouses interest among researchers and education specialists who since their recent proliferation, they have been trying to introduce their motivating potential in learning contexts. Our main research question in this work focuses on whether MR digital games can, via novel Presence (feeling of ‘being and doing there’ in a virtual or augmented world) and MR gamification (dynamics, mechanics, components) support and foster future learning and teaching, to address a wide variety and variation of educational contexts. To accomplish the above, a consistent computational framework that supports new types of Mixed-Reality Serious Games and Gamification (MRSGs) is established in this work that features MR gesturebased and game-based learning. The introduced term Mixed Reality Serious Games (MRSGs), refers to digital mini game-shells that allow the learners and teachers to sense the feeling of ‘Presence’ experienced under a novel MR educational learning framework, in both Virtual Reality (VR) as well as Augmented Reality (AR) formal and informal learning. The former (VR) allows for the unique feeling of ‘being there’ and ‘doing there’ in the virtual world, that will be transforming the overall game-based learning experience, via latest innovations as well as recent progress in low-cost h/w Head Mounted Displays (HMDs). The latter (AR) blends real and virtual elements so that the 3D virtual element is registered accurately in the real world and interacted freely by the learner via various mobile displays, including smart glasses, natural, gesture-based interaction (mobile RGB and RGB-D), MR virtual characters and gamified learning processes. Game-based learning also involves the incorporation of games into lessons. The principal aim of applying games in education is to increase students’ engagement and motivation. In our case studies we provide two mini MRSGs in VR and AR that accompany the primary school history class and particular the period of the Minoan Civilization, as manifested by the archaeological site of Ancient Knossos in Heraklion, Greece. The AR MRSG implements a desktop-based holographic application using the Meta-AR glasses. This MRSG consist of three mini game shells in which the student has to complete various learning tasks by using gesture-based interaction. These tasks consist of puzzle-based and constructional games tasks. The VR MRSG implements a similar approach of a Virtual tour in Knossos with quests that student need to accomplish to collect rewards. The student has to fully explore the palace, interact with special characters and complete their quests. These MRSGs are a first attempt to formally study latest h/w and s/w advances in Mixed Reality technologies, applied in gesture-based and game-based learning in both formal and informal educational contexts. Moreover we compared the gamification elements for each enabling technology and define the gamification dynamics, mechanics and components that need to be utilized in each MR environment.
Online Structure Analysis for Real-Time Indoor Scene Reconstruction

We propose a real-time approach for indoor scene reconstruction. It is capable of producing a ready-to-use 3D geometric model even while the user is still scanning the environment with a consumer depth camera. Our approach features explicit representations of planar regions and nonplanar objects extracted from the noisy feed of the depth camera, via an online structure analysis on the dynamic, incomplete data. The structural information is incorporated into the volumetric representation of the scene, resulting in a seamless integration with KinectFusion's global data structure and an efficient implementation of the whole reconstruction process. Moreover, heuristics based on rectilinear shapes in typical indoor scenes effectively eliminate camera tracking drift and further improve reconstruction accuracy. The instantaneous feedback enabled by our on-the-fly structure analysis, including repeated object recognition, allows the user to selectively scan the scene and produce high-fidelity large-scale models efficiently. We demonstrate the capability of our system with real-life examples.
1 Wave Dispersion in High-Rise Buildings due to Soil-Structure Interaction

Nonparametric techniques for estimation of wave dispersion in buildings by seismic interferometry are applied to a simple model of a soil-structure interaction (SSI) system with coupled horizontal and rocking response. The system consists of a viscously damped shearbeam, representing a building, on a rigid foundation embedded in a half-space. The analysis shows that (1) wave propagation through the system is dispersive. The dispersion is characterized by lower phase velocity (softening) in the band containing the fundamental system mode of vibration, and little change in the higher frequency bands, relative to the building shear wave velocity. This mirrors its well known effect on the frequencies of vibration, i.e. reduction for the fundamental mode (softening) and no significant change for the higher modes of vibration, in agreement with the duality of the wave and vibrational nature of structural response. Nevertheless, the phase velocity identified from broader band IRFs is very close to the superstructure shear wave velocity, as found by the earlier study. The analysis reveals that (2) the reason for this apparent paradox is that the latter estimates are biased towards the higher values, representative of the higher frequencies in the band, where the response is less affected by SSI. It is also discussed that (3) bending flexibility and soil flexibility produce similar effects on the phase velocities and frequencies of vibration of a building. 1 Ph.D. Candidate, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531,  mrahmani@usc.edu 2 Ph.D. Candidate, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531,  mebrahim@usc.edu 3 Research Professor, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531,  mtodorov@usc.edu Earthquake Engineering and Structural Dynamics. DOI: 10.1002/eqe.2454, Final Draft. First published online on June 23, 2014,
End-to-End Interpretation of the French Street Name Signs Dataset

We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France. Each image contains several views of the same street name sign. Every image has normalized, title case folded ground-truth text as it would appear on a map. We believe that the FSNS dataset is large and complex enough to train a deep network of significant complexity to solve the street name extraction problem “end-to-end” or to explore the design trade-offs between a single complex engineered network and multiple sub-networks designed and trained to solve sub-problems. We present such an “end-to-end” network/graph for Tensor Flow and its results on the FSNS dataset.
Building Occlusion Detection From Ghost Images

This paper proposes a novel occlusion detection method for urban true orthophoto generation. In this new method, occlusion detection is performed using a ghost image; this method is therefore considerably different from the traditional Z-buffer method, in which occlusion detection is performed during the generation of a true orthophoto (to avoid ghost image occurrence). In the proposed method, a model is first established that describes the relationship between each ghost image and the boundary of the corresponding building occlusion, and then an algorithm is applied to identify the occluded areas in the ghost images using the building displacements. This theory has not previously been applied in true orthophoto generation. The experimental results demonstrate that the method proposed in this paper is capable of effectively avoiding pseudo-occlusion detection, with a success rate of 99.2%, and offers improved occlusion detection accuracy compared with the traditional Z-buffer detection method. The advantage of this method is that it avoids the shortcoming of performing occlusion detection and true orthophoto generation simultaneously, which results in false visibility and false occlusions; instead, the proposed method detects occlusions from ghost images and therefore provides simple and effective true orthophoto generation.
Problems in dealing with missing data and informative censoring in clinical trials

Acommon problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.
Digital dentistry: an overview of recent developments for CAD/CAM generated restorations

As in many other industries, production stages are increasingly becoming automated in dental technology. As the price of dental laboratory work has become a major factor in treatment planning and therapy, automation could enable more competitive production in high-wage areas like Western Europe and the USA. Advances in computer technology now enable cost-effective production of individual pieces. Dental restorations produced with computer assistance have become more common in recent years. Most dental companies have access to CAD/CAM procedures, either in the dental practice, the dental laboratory or in the form of production centres. The many benefits associated with CAD/CAM generated dental restorations include: the access to new, almost defect-free, industrially prefabricated and controlled materials; an increase in quality and reproducibility and also data storage commensurate with a standardised chain of production; an improvement in precision and planning, as well as an increase in efficiency. As a result of continual developments in computer hardware and software, new methods of production and new treatment concepts are to be expected, which will enable an additional reduction in costs. Dentists, who will be confronted with these techniques in the future, require certain basic knowledge if they are to benefit from these new procedures. This article gives an overview of CAD/CAM-technologies and systems available for dentistry today.
Design and Implementation of Anti Spyware System using Design Patterns

Spyware is considered as a great threat to confidentiality that it can cause loss of control over private data for computer users. This kind of threat might select some data and send it to another third party without the consent of the user. Spyware detection techniques have been presented traditionally by three approaches; signature, behavior and specification based detection techniques. These approaches were successful in detecting known spyware but it suffers from some drawbacks such as; the need for updating data describing the system behavior to detect new or unknown spywares, and the high level of false positive or false negative rate. Therefore, in this paper we introduce a proposed anti spyware system design and implementation using design patterns for detecting and classifying spyware. This proposed approach can be reusable and modifying itself for any new or unknown spyware.