nils-herrmann/clean_scirepeval huggingface dataset:
Mirrored Light Field Video Camera Adapter

This paper proposes the design of a custom mirror-based light field camera adapter that is cheap, simple in construction, and accessible. Mirrors of different shape and orientation reflect the scene into an upwards-facing camera to create an array of virtual cameras with overlapping field of view at specified depths, and deliver video frame rate light fields. We describe the design, construction, decoding and calibration processes of our mirrorbased light field camera adapter in preparation for an open-source release to benefit the robotic vision community. The latest report, computer-aided design models, diagrams and code can be obtained from the following repository: https://bitbucket.org/acrv/mirrorcam.
Lumped-element equivalent circuit models for distributed microwave directional couplers

In this paper, we propose the lumped equivalent circuit models for the distributed coupled line directional and branch line directional couplers. Firstly, we have developed the conversion topologies from distributed microwave circuits to the lumped element circuits. Secondly, as applications of the proposed topologies, we have generated two 3 dB lumped-element directional couplers from their distributed versions. It was found that the derived S-parameters for the lumped-element couplers agree well with those of the corresponding distributed ones at the center frequency as well as within a certain bandwidth. This technology can be applied to construct highly integrated directional couplers in the WiFi/WiMAX RF applications in order to save valuable PCB space.
Learning Deep NBNN Representations for Robust Place Categorization

This letter presents an approach for semantic place categorization using data obtained from RGB cameras. Previous studies on visual place recognition and classification have shown that by considering features derived from pretrained convolutional neural networks (CNNs) in combination with part-based classification models, high recognition accuracy can be achieved, even in the presence of occlusions and severe viewpoint changes. Inspired by these works, we propose to exploit local deep representations, representing images as set of regions applying a Naïve Bayes nearest neighbor (NBNN) model for image classification. As opposed to previous methods, where CNNs are merely used as feature extractors, our approach seamlessly integrates the NBNN model into a fully CNN. Experimental results show that the proposed algorithm outperforms previous methods based on pretrained CNN models and that, when employed in challenging robot place recognition tasks, it is robust to occlusions, environmental and sensor changes.
Chain : Operator Scheduling for Memory Minimization in Data Stream Systems

In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams --- adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the run-time system memory usage. We then present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing run-time memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams, and multiple queries of the above types. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling, compare it with competing scheduling strategies, and validate our analytical conclusions.
Contact detection and reaction of a wheelchair mounted robotic arm equiped with mechanical gravity canceller

Safety issue has become the primary concern in wheelchair mounted robotic arm applications. We introduced mechanical gravity canceller in the design to realize a light weight manipulator, which also yield the greatly simplify of manipulator dynamics. Based on the simplified dynamics, sensor based contact detection can be easily implemented to enable safety. Contact reaction schemes are also applied through a impedance control law to achieve desired backdrivability. Experiments are conducted to illustrate the proposed method.
Internet security architecture

Fear of security breaches has been a major r eason f or the business world’ s reluctance to embrace the Inter net as a viable means of communication. A widely adopted solution consists of physically separating private networks from the rest of Internet using firewalls. This paper discusses the curr ent cryptographic security measures available for the Internet infrastructur e as an alternative to physical segregation. First the IPsec ar chitecture including security protocols in the Internet Layer and the related key management pr oposals are introduced. The transport layer security protocol and security issues in the netw ork control and management are then presented. The paper is addr essed to r eaders with a basic understanding of common security mechanisms including encryption, authentication and key exchange techniques.
Mechanical Properties of Austenitic Stainless Steel Made by Additive Manufacturing

Using uniaxial tensile and hardness testing, we evaluated the variability and anisotropy of the mechanical properties of an austenitic stainless steel, UNS S17400, manufactured by an additive process, selective laser melting. Like wrought materials, the mechanical properties depend on the orientation introduced by the processing. The recommended stress-relief heat treatment increases the tensile strength, reduces the yield strength, and decreases the extent of the discontinuous yielding. The mechanical properties, assessed by hardness, are very uniform across the build plate, but the stress-relief heat treatment introduced a small non-uniformity that had no correlation to position on the build plate. Analysis of the mechanical property behavior resulted in four conclusions. (1) The within-build and build-to-build tensile properties of the UNS S17400 stainless steel are less repeatable than mature engineering structural alloys, but similar to other structural alloys made by additive manufacturing. (2) The anisotropy of the mechanical properties of the UNS S17400 material of this study is larger than that of mature structural alloys, but is similar to other structural alloys made by additive manufacturing. (3) The tensile mechanical properties of the UNS S17400 material fabricated by selective laser melting are very different from those of wrought, heat-treated 17-4PH stainless steel. (4) The large discontinuous yielding strain in all tests resulted from the formation and propagation of Lüders bands.
Integration of Parallel Write Ahead Logging and Cicada Concurrency Control Method

We proposed the idea of combining the Cicada concurrency control mechanism with P-WAL (parallel write-ahead logging) and experimentally evaluated the proposed control mechanism. Our results show that P-WAL can be combined suitably with Cicada without decreasing Cicada's performance. We then experimentally evaluated the most optimal parallel write-ahead logging parameter among (1) conservative lock release; (2) early lock release, the early release of locks on database objects; and (3) group commit, which writes the transaction log to storage all at once. The results show that early lock release and group commit were the most optimal parameters for the combination of Cicada and P-WAL.
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?

The performance of Neural Machine Translation (NMT) systems often suffers in lowresource scenarios where sufficiently largescale parallel corpora cannot be obtained. Pretrained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases – providing gains of up to 20 BLEU points in the most favorable
Low-cost and area-efficient FPGA implementations of lattice-based cryptography

The interest in lattice-based cryptography is increasing due to its quantum resistance and its provable security under some worst-case hardness assumptions. As this is a relatively new topic, the search for efficient hardware architectures for lattice-based cryptographic building blocks is still an active area of research. We present area optimizations for the most critical and computationally-intensive operation in lattice-based cryptography: polynomial multiplication with the Number Theoretic Transform (NTT). The proposed methods are implemented on an FPGA for polynomial multiplication over the ideal ℤp[x]〈xn + 1〉. The proposed hardware architectures reduce slice usage, number of utilized memory blocks and total memory accesses by using a simplified address generation, improved memory organization and on-the-fly operand generations. Compared to prior work, with similar performance the proposed hardware architectures can save up to 67% of occupied slices, 80% of used memory blocks and 60% of memory accesses, and can fit into smallest Xilinx Spartan-6 FPGA.
Outsourceable two-party privacy-preserving biometric authentication

Biometric authentication, a key component for many secure protocols and applications, is a process of authenticating a user by matching her biometric data against a biometric database stored at a server managed by an entity. If there is a match, the user can log into her account or obtain the services provided by the entity. Privacy-preserving biometric authentication (PPBA) considers a situation where the biometric data are kept private during the authentication process. That is the user's biometric data record is never disclosed to the entity, and the data stored in the entity's biometric database are never disclosed to the user. Due to the reduction in operational costs and high computing power, it is beneficial for an entity to outsource not only its data but also computations such as biometric authentication process to a cloud. However, due to well-documented security risks faced by a cloud, sensitive data like biometrics should be encrypted first and then outsourced to the cloud. When the biometric data are encrypted and cannot be decrypted by the cloud, the existing PPBA protocols are not applicable. Therefore, in this paper, we propose a two-party PPBA protocol when the biometric data in consideration are fully encrypted and outsourced to a cloud. In the proposed protocol, the security of the biometric data is completely protected since the encrypted biometric data are never decrypted during the authentication process. In addition, we formally analyze the security of the proposed protocol and provide extensive empirical results to show its runtime complexity.
ndmaSNF: cancer subtype discovery based on integrative framework assisted by network diffusion model

Recently, with the rapid progress of high-throughput sequencing technology, diverse genomic data are easy to be obtained. To effectively exploit the value of those data, integrative methods are urgently needed. In this paper, based on SNF (Similarity Network Diffusion) , we proposed a new integrative method named ndmaSNF (network diffusion model assisted SNF), which can be used for cancer subtype discovery with the advantage of making use of somatic mutation data and other discrete data. Firstly, we incorporate network diffusion model on mutation data to make it smoothed and adaptive. Then, the mutation data along with other data types are utilized in the SNF framework by constructing patient-by-patient similarity networks for each data type. Finally, a fused patient network containing all the information from different input data types is obtained by using a nonlinear iterative method. The fused network can be used for cancer subtype discovery through the clustering algorithm. Experimental results on four cancer datasets showed that our ndmaSNF method can find subtypes with significant differences in the survival profile and other clinical features.
Bistatic Synthetic Aperture Radar Imaging for Arbitrary Flight Trajectories

In this paper, we present an analytic, filtered backprojection (FBP) type inversion method for bistatic synthetic aperture radar (BISAR). We consider a BISAR system where a scene of interest is illuminated by electromagnetic waves that are transmitted, at known times, from positions along an arbitrary, but known, flight trajectory and the scattered waves are measured from positions along a different flight trajectory which is also arbitrary, but known. We assume a single-scattering model for the radar data, and we assume that the ground topography is known but not necessarily flat. We use microlocal analysis to develop the FBP-type reconstruction method. We analyze the computational complexity of the numerical implementation of the method and present numerical simulations to demonstrate its performance.
Deep Convolutional Neural Networks for Fire Detection in Images

Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balanced benchmark dataset provided in this paper. We therefore propose to use even deeper Convolutional Neural Networks for fire detection in images, and enhancing these with fine tuning based on a fully connected layer. We use two pretrained state-of-the-art Deep CNNs, VGG16 and Resnet50, to develop our fire detection system. The Deep CNNs are tested on our imbalanced dataset, which we have assembled to replicate real world scenarios. It includes images that are particularly difficult to classify and that are deliberately unbalanced by including significantly more non-fire images than fire images. The dataset has been made available online. Our results show that adding fully connected layers for fine tuning indeed does increase accuracy, however, this also increases training time. Overall, we found that our deeper CNNs give good performance on a more challenging dataset, with Resnet50 slightly outperforming VGG16. These results may thus lead to more successful fire detection systems in practice.
Gated Recurrent Units for Airline Sentiment Analysis of Twitter Data

We explore the use of a bi-directional gated recurrent unit (GRU) network for sentiment analysis of Twitter data directed at U.S airlines. The performance of the GRU is then compared with a convolutional neural network (CNN) and also the comparatively simpler vanilla single layer network. We wish to not only identify which model has the highest test accuracy, but also its precision, recall rates as well as its robustness to unbalanced classes in the training set. We also discuss the common practice of re-weighting data for unbalanced classes and its effect on a model’s prediction from a Bayesian viewpoint. Indeed, as it turns out, there is a balance to be found between using local information, that is information contained in each tweet and global information, the information present in the entire training set or test set when making predictions. We also implement a Long Short Term Memory (LSTM) network for text generation of some positive or negative airline tweets and the relatively coherent text demonstrate the power of these networks.
Metasurface holograms reaching 80% efficiency.

Surfaces covered by ultrathin plasmonic structures--so-called metasurfaces--have recently been shown to be capable of completely controlling the phase of light, representing a new paradigm for the design of innovative optical elements such as ultrathin flat lenses, directional couplers for surface plasmon polaritons and wave plate vortex beam generation. Among the various types of metasurfaces, geometric metasurfaces, which consist of an array of plasmonic nanorods with spatially varying orientations, have shown superior phase control due to the geometric nature of their phase profile. Metasurfaces have recently been used to make computer-generated holograms, but the hologram efficiency remained too low at visible wavelengths for practical purposes. Here, we report the design and realization of a geometric metasurface hologram reaching diffraction efficiencies of 80% at 825 nm and a broad bandwidth between 630 nm and 1,050 nm. The 16-level-phase computer-generated hologram demonstrated here combines the advantages of a geometric metasurface for the superior control of the phase profile and of reflectarrays for achieving high polarization conversion efficiency. Specifically, the design of the hologram integrates a ground metal plane with a geometric metasurface that enhances the conversion efficiency between the two circular polarization states, leading to high diffraction efficiency without complicating the fabrication process. Because of these advantages, our strategy could be viable for various practical holographic applications.
Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms

Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informal conversational settings, but despite its evident impact on event coreference resolution and extraction, there has been relatively little work on computational methods for identifying and resolving VPE. Here, we present a novel approach to detecting and resolving VPE by using supervised discriminative machine learning techniques trained on features extracted from an automatically parsed, publicly available dataset. Our approach yields state-of-the-art results for VPE detection by improving F1 score by over 11%; additionally, we explore an approach to antecedent identification that uses the Margin-Infused-RelaxedAlgorithm, which shows promising results.
Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory

On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions.
A Region Ensemble for 3-D Face Recognition

In this paper, we introduce a new system for 3D face recognition based on the fusion of results from a committee of regions that have been independently matched. Experimental results demonstrate that using 28 small regions on the face allow for the highest level of 3D face recognition. Score-based fusion is performed on the individual region match scores and experimental results show that the Borda count and consensus voting methods yield higher performance than the standard sum, product, and min fusion rules. In addition, results are reported that demonstrate the robustness of our algorithm by simulating large holes and artifacts in images. To our knowledge, no other work has been published that uses a large number of 3D face regions for high-performance face matching. Rank one recognition rates of 97.2% and verification rates of 93.2% at a 0.1% false accept rate are reported and compared to other methods published on the face recognition grand challenge v2 data set.
Design of Fractional Order PID Controller for DC Motor using Genetic Algorithm

Design of fractional order PID (FOPID) controller for DC motor is proposed in this paper. A FOPID (PID) is a PID controller whose derivative and integral orders are fractional numbers rather than integers. Design stage of such controller consists of determining six parameters – proportional constant (Kp), integral constant (Ki), derivative constant (Kd), filter time constant (τd), integral order (λ) and derivative order (μ). The proposed approach poses the problem as designing a DC motor speed controller on the concept of fixed structure robust controller and mixed sensitivity H∞ method. The uncertainty caused by the parameter changes of motor resistance, motor inductance and load are formulated as multiplicative uncertainty weight, which are used in the objective function in the design. Genetic Algorithm (GA) and Simulated Annealing (SA) are employed to carry out the aforementioned design procedure. Comparisons are made with a PID with derivative first order filter controller and it is shown that the proposed FOPID controller can highly improve the system robustness with respect to model uncertainties. The comparison of PID and FOPID controllers is also been done on the basis of Time Domain Performance index i.e. ISE (Integral of Square Error).
Combating catastrophic forgetting with developmental compression

Generally intelligent agents exhibit successful behavior across problems in several settings. Endemic in approaches to realize such intelligence in machines is catastrophic forgetting: sequential learning corrupts knowledge obtained earlier in the sequence, or tasks antagonistically compete for system resources. Methods for obviating catastrophic forgetting have sought to identify and preserve features of the system necessary to solve one problem when learning to solve another, or to enforce modularity such that minimally overlapping sub-functions contain task specific knowledge. While successful, both approaches scale poorly because they require larger architectures as the number of training instances grows, causing different parts of the system to specialize for separate subsets of the data. Here we present a method for addressing catastrophic forgetting called developmental compression. It exploits the mild impacts of developmental mutations to lessen adverse changes to previously-evolved capabilities and 'compresses' specialized neural networks into a generalized one. In the absence of domain knowledge, developmental compression produces systems that avoid overt specialization, alleviating the need to engineer a bespoke system for every task permutation and suggesting better scalability than existing approaches. We validate this method on a robot control problem and hope to extend this approach to other machine learning domains in the future.