nils-herrmann/clean_scirepeval huggingface dataset:
Colorization by Example

We present a new method for colorizing grayscale images by transferring color from a segmented example image. Rather than relying on a series of independent pixel-level decisions, we develop a new strategy that attempts to account for the higher-level context of each pixel. The colorizations generated by our approach exhibit a much higher degree of spatial consistency, compared to previous automatic color transfer methods [WAM02]. We also demonstrate that our method requires considerably less manual effort than previous user-assisted colorization methods [LLW04].
 Given a grayscale image to colorize, we first determine for each pixel which example segment it should learn its color from. This is done automatically using a robust supervised classification scheme that analyzes the low-level feature space defined by small neighborhoods of pixels in the example image. Next, each pixel is assigned a color from the appropriate region using a neighborhood matching metric, combined with spatial filtering for improved spatial coherence. Each color assignment is associated with a confidence value, and pixels with a sufficiently high confidence level are provided as "micro-scribbles" to the optimization-based colorization algorithm of Levin et al. [LLW04], which produces the final complete colorization of the image.
MPI Motion Simulator: Development and Analysis of a Novel Motion Simulator

This paper discusses the technical issues that were required to adapt a KUKA Robocoaster for use as a real-time motion simulator. Within this context, the paper addresses the physical modifications and the software control structure that were needed to have a flexible and safe experimental setup. It also addresses the delays and transfer function of the system. The paper is divided into two sections. The first section describes the control and safety structures of the MPI Motion Simulator. The second section shows measurements of latencies and frequency responses of the motion simulator. The results show that the frequency responses of the MPI Motion Simulator compare favorably with high-end Stewart Platforms, and therefore demonstrate the suitability of robot-based motion simulators for flight simulation.
Design of Hamming Code Encoding and Decoding Circuit Using Transmission Gate Logic

Debalina Roy Choudhury1, Krishanu Podder2 1Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, 700107, India 2Department of Electronics and Communication Engineering, Tripura Institute of Technology, Tripura, 799009, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In this paper, the Hamming code encoder
Voltage intensity based non-invasive blood glucose monitoring

Diabetes is one among the supreme health challenges of the current century. Most common method for estimation of blood glucose concentration is using glucose meter. The process involves pricking the finger and extracting the blood along with chemical analysis being done with the help of disposable test strips. Non-invasive method for glucose estimation promotes regular testing, adequate control and reduction in health care cost. The proposed method makes use of a near infrared sensor for determination of blood glucose. Near-infrared (NIR) is sent through the fingertip, before and after blocking the blood flow by making use of a principle called occlusion. By analyzing the variation in voltages received after reflection in both the cases with the dataset, the current diabetic condition as well as the approximate glucose level of the individual is predicted. The results obtained are being validated with glucose meter readings and statistical analysis of the readings where done. Analysis shows that the bias as well as the standard deviation decreases as the glucose concentration increases. The obtained result is then communicated with a smart phone through Bluetooth for further communication with the doctor.
Simultaneous tracking and shape estimation with laser scanners

Advanced driver assistance systems and the environment perception for autonomous vehicles will benefit from systems robustly tracking objects while simultaneously estimating their shape. Unlike many recent approaches that represent object shapes by approximated models such as boxes or ellipses, this paper proposes an algorithm that estimates a free-formed shape derived from raw laser measurements. For that purpose local occupancy grid maps are used to model arbitrary object shapes. Beside shape estimation the algorithm keeps a stable reference point on the object. This will be important to avoid apparent motion if the observable part of an object contour changes. The algorithm is part of a perception system and is tested with two 4-layer laser scanners.
Information system security management in the new millennium

R apid advances in electronic networks and computerbased information systems have given us enormous capabilities to process, store, and transmit digital data in most business sectors. This has transformed the way we conduct trade, deliver government services, and provide health care. Changes in communication and information technologies and particularly their confluence has raised a number of concerns connected with the protection of organizational information assets. Achieving consensus regarding safeguards for an information system, among different stakeholders in an organization, has become more difficult than solving many technical problems that might arise. This “Technical Opinion” focuses on understanding the nature of information security in the next millennium. Based on this understanding it suggests a set of principles that would help in managing information security in the future.
Configuring role-based access control to enforce mandatory and discretionary access control policies

Access control models have traditionally included mandatory access control (or lattice-based access control) and discretionary access control. Subsequently, role-based access control has been introduced, along with claims that its mechanisms are general enough to simulate the traditional methods. In this paper we provide systematic constructions for various common forms of both of the traditional access control paradigms using the role-based access control (RBAC) models of Sandhu et al., commonly called RBAC96. We see that all of the features of the RBAC96 model are required, and that although for the manatory access control simulation, only one administrative role needs to be assumed, for the discretionary access control simulations, a complex set of administrative roles is required.
Cells in Multidimensional Recurrent Neural Networks

The transcription of handwritten text on images is one task in machine learning and one solution to solve it is using multi-dimensional recurrent neural networks (MDRNN) with connectionist temporal classification (CTC). The RNNs can contain special units, the long short-term memory (LSTM) cells. They are able to learn long term dependencies but they get unstable when the dimension is chosen greater than one. We defined some useful and necessary properties for the one-dimensional LSTM cell and extend them in the multi-dimensional case. Thereby we introduce several new cells with better stability. We present a method to design cells using the theory of linear shift invariant systems. The new cells are compared to the LSTM cell on the IFN/ENIT and Rimes database, where we can improve the recognition rate compared to the LSTM cell. So each application where the LSTM cells in MDRNNs are used could be improved by substituting them by the new developed cells.
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews

Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities. In Aspect Based Sentiment Analysis (ABSA), it is critical to identify aspect categories and extract aspect terms from the sentences of user-generated reviews. However, the two tasks are often treated independently, even though they are closely related. Intuitively, the learned knowledge of one task should inform the other learning task. In this paper, we propose a multi-task learning model based on neural networks to solve them together. We demonstrate the improved performance of our multi-task learning model over the models trained separately on three public dataset released by SemEval work-
Efficient instrumentation for code coverage testing

Evaluation of Code Coverage is the problem of identifying the parts of a program that did not execute in one or more runs of a program. The traditional approach for code coverage tools is to use static code instrumentation. In this paper we present a new approach to dynamically insert and remove instrumentation code to reduce the runtime overhead of code coverage. We also explore the use of dominator tree information to reduce the number of instrumentation points needed. Our experiments show that our approach reduces runtime overhead by 38-90% compared with purecov, a commercial code coverage tool. Our tool is fully automated and available for download from the Internet.
Individual alpha neurofeedback training effect on short term memory.

Memory performance has been reported to be associated with electroencephalogram (EEG) alpha activity. This study aimed to improve short term memory performance by individual alpha neurofeedback training (NFT). With appropriate protocol designed for NFT, the experimental results showed that the participants were able to learn to increase the relative amplitude in individual alpha band during NFT and short term memory performance was significantly enhanced by 20 sessions of NFT. More importantly, further analysis revealed that the improvement of short term memory was positively correlated with the increase of the relative amplitude in the individual upper alpha band during training. In addition, effective strategies for individual alpha training varied among individuals and the most successful mental strategies were related to positive thinking.
Circuit and Layout Co-Design for ESD Protection in Bipolar-CMOS-DMOS (BCD) High-Voltage Process

The n-channel lateral double-diffused metal-oxide- semiconductor (nLDMOS) devices in high-voltage (HV) technologies are known to have poor electrostatic discharge (ESD) robustness. To improve the ESD robustness of nLDMOS, a co-design method combining a new waffle layout structure and a trigger circuit is proposed to fulfill the body current injection technique in this work. The proposed layout and circuit co-design method on HV nLDMOS has successfully been verified in a 0.5-¿m 16-V bipolar-CMOS-DMOS (BCD) process and a 0.35- ¿m 24-V BCD process without using additional process modification. Experimental results through transmission line pulse measurement and failure analyses have shown that the proposed body current injection technique can significantly improve the ESD robustness of HV nLDMOS.
Magnetic input for mobile virtual reality

Modern smartphones can create compelling virtual reality (VR) experiences through the use of VR enclosures, devices that encase the phone and project stereoscopic renderings through lenses into the user's eyes. Since the touch screen in such designs is typically hidden inside an enclosure, the main interaction mechanism of the device is not accessible. We present a new magnetic input mechanism for mobile VR devices which is wireless, unpowered, inexpensive, provides physical feedback, requires no calibration, and works reliably on the majority of modern smartphones. This is the main input mechanism for Google Cardboard, of which there are over one million units. We show robust gesture recognition, at an accuracy of greater than 95% across smartphones and assess the capabilities, accuracy and limitations of our technique through a user study.
STAMP: Stanford Transactional Applications for Multi-Processing

Transactional Memory (TM) is emerging as a promising technology to simplify parallel programming. While several TM systems have been proposed in the research literature, we are still missing the tools and workloads necessary to analyze and compare the proposals. Most TM systems have been evaluated using microbenchmarks, which may not be representative of any real-world behavior, or individual applications, which do not stress a wide range of execution scenarios. We introduce the Stanford Transactional Application for Multi-Processing (STAMP), a comprehensive benchmark suite for evaluating TM systems. STAMP includes eight applications and thirty variants of input parameters and data sets in order to represent several application domains and cover a wide range of transactional execution cases (frequent or rare use of transactions, large or small transactions, high or low contention, etc.). Moreover, STAMP is portable across many types of TM systems, including hardware, software, and hybrid systems. In this paper, we provide descriptions and a detailed characterization of the applications in STAMP. We also use the suite to evaluate six different TM systems, identify their shortcomings, and motivate further research on their performance characteristics.
Identification and evaluation of children with autism spectrum disorders.

Autism spectrum disorders are not rare; many primary care pediatricians care for several children with autism spectrum disorders. Pediatricians play an important role in early recognition of autism spectrum disorders, because they usually are the first point of contact for parents. Parents are now much more aware of the early signs of autism spectrum disorders because of frequent coverage in the media; if their child demonstrates any of the published signs, they will most likely raise their concerns to their child's pediatrician. It is important that pediatricians be able to recognize the signs and symptoms of autism spectrum disorders and have a strategy for assessing them systematically. Pediatricians also must be aware of local resources that can assist in making a definitive diagnosis of, and in managing, autism spectrum disorders. The pediatrician must be familiar with developmental, educational, and community resources as well as medical subspecialty clinics. This clinical report is 1 of 2 documents that replace the original American Academy of Pediatrics policy statement and technical report published in 2001. This report addresses background information, including definition, history, epidemiology, diagnostic criteria, early signs, neuropathologic aspects, and etiologic possibilities in autism spectrum disorders. In addition, this report provides an algorithm to help the pediatrician develop a strategy for early identification of children with autism spectrum disorders. The accompanying clinical report addresses the management of children with autism spectrum disorders and follows this report on page 1162 [available at www.pediatrics.org/cgi/content/full/120/5/1162]. Both clinical reports are complemented by the toolkit titled " Caring for Children With Autism Spectrum Disorders: A Resource Toolkit for Clinicians," which contains screening and surveillance tools, practical forms, tables, and parent handouts to assist the pediatrician in the identification, evaluation, and management of autism spectrum disorders in children.
SpectralNet: Spectral Clustering using Deep Neural Networks

Spectral clustering is a leading and popular technique in unsupervised data analysis. Two of its major limitations are scalability and generalization of the spectral embedding (i.e., out-of-sample-extension). In this paper we introduce a deep learning approach to spectral clustering that overcomes the above shortcomings. Our network, which we call SpectralNet, learns a map that embeds input data points into the eigenspace of their associated graph Laplacian matrix and subsequently clusters them. We train SpectralNet using a procedure that involves constrained stochastic optimization. Stochastic optimization allows it to scale to large datasets, while the constraints, which are implemented using a special-purpose output layer, allow us to keep the network output orthogonal. Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points. To further improve the quality of the clustering, we replace the standard pairwise Gaussian affinities with affinities leaned from unlabeled data using a Siamese network. Additional improvement can be achieved by applying the network to code representations produced, e.g., by standard autoencoders. Our end-to-end learning procedure is fully unsupervised. In addition, we apply VC dimension theory to derive a lower bound on the size of SpectralNet. State-of-the-art clustering results are reported on the Reuters dataset. Our implementation is publicly available at https://github.com/kstant0725/SpectralNet.
Proposed classification of blockchains based on authority and incentive dimensions

The potential of blockchain technology has received attention in the area of FinTech — the combination of finance and technology. Blockchain technology was first introduced as the technology behind the Bitcoin decentralized virtual currency, but there is the expectation that its characteristics of accurate and irreversible data transfer in a decentralized P2P network could make other applications possible. Although a precise definition of blockchain technology has not yet been given, it is important to consider how to classify different blockchain systems in order to better understand their potential and limitations. The goal of this paper is to add to the discussion on blockchain technology by proposing a classification based on two dimensions external to the system: (1) existence of an authority (without an authority and under an authority) and (2) incentive to participate in the blockchain (market-based and non-market-based). The combination of these elements results in four types of blockchains. We define these dimensions and describe the characteristics of the blockchain systems belonging to each classification.
Degeneracy study of the forward kinematics of planar 3-RPR parallel manipulators

Degeneracy study of the forward kinematics of planar 3-RPR parallel manipulators This paper investigates two situations in which the forward kinematics of planar 3-RPR parallel manipulators degenerates. These situations have not been addressed before. The first degeneracy arises when the three input joint variables ρ 1 , ρ 2 and ρ 3 satisfy a certain relationship. This degeneracy yields a double root of the characteristic polynomial in tan(/ 2) t ϕ = , which could be erroneously interpreted as two coalesce assembly modes. But, unlike what arises in non-degenerate cases, this double root yields two sets of solutions for the position coordinates (x, y) of the platform. In the second situation, we show that the forward kinematics degenerates over the whole joint space if the base and platform triangles are congruent and the platform triangle is rotated by 180 deg about one of its sides. For these " degenerate " manipulators, which are defined here for the first time, the forward kinematics is reduced to the solution of a 3 rd-degree polynomial and a quadratics in sequence. Such manipulators constitute, in turn, a new family of analytic planar manipulators that would be more suitable for industrial applications. 1  Solving the forward kinematic problem of a parallel manipulator often leads to complex equations and non analytic solutions, even when considering planar 3-DOF parallel manipulators . For these planar manipulators, Hunt showed that the forward kinematics admits at most 6 solutions  and several authors [3, 4] have shown independently that their forward kinematics can be reduced as the solution of a characteristic polynomial of degree 6. In , a set of two linear equations in the position coordinates (x, y) of the moving platform is first established, which makes it possible to write x and y as function of the sine and cosine of the orientation angle ϕ of the moving platform. Substituting these expressions of x and y into one of the constraint equations of the manipulator and using the tan-half angle substitution leads to a 6 th-degree polynomial in tan(/ 2) t ϕ =. Conditions under which the degree of this characteristic polynomial decreases were investigated in [5, 6]. Four distinct cases were found, namely, (i) manipulators for which two of the joints coincide (ii) manipulators with similar aligned platforms (iii) manipulators with nonsimilar aligned platforms and, (iv) manipulators with similar triangular platforms. For cases (i), (ii) and …
Capsule-Oriented Programming

"Explicit concurrency should be abolished from all higher-level programming languages (i.e. everything except - perhaps- plain machine code.)." Dijkstra  (paraphrased). A promising class of concurrency abstractions replaces explicit concurrency mechanisms with a single linguistic mechanism that combines state and control and uses asynchronous messages for communications, e.g. active objects or actors, but that doesn't remove the hurdle of understanding non-local control transfer. What if the programming model enabled programmers to simply do what they do best, that is, to describe a system in terms of its modular structure and write sequential code to implement the operations of those modules and handles details of concurrency? In a recently sponsored NSF project we are developing such a model that we call capsule-oriented programming and its realization in the Panini project. This model favors modularity over explicit concurrency, encourages concurrency correctness by construction, and exploits modular structure of programs to expose implicit concurrency.
Interactive Local Color Transfer Between Images

Altering image’s color is one of the most common tasks in image processing. However, most of existing methods are aimed to perform global color transfer. This usually means that the whole image is affected. But in many cases colors of only a part of an image needs changing, so it is important that the rest of the image remains unmodified. In this article we offer a fast and simple interactive algorithm based on local color statistics that allows altering color of only a part of an image, preserving image’s details and natural look.
Multilabel random walker image segmentation using prior models

The recently introduced random walker segmentation algorithm by Grady and Funka-Lea (2004) has been shown to have desirable theoretical properties and to perform well on a wide variety of images in practice. However, this algorithm requires user-specified labels and produces a segmentation where each segment is connected to a labeled pixel. We show that incorporation of a nonparametric probability density model allows for an extended random walkers algorithm that can locate disconnected objects and does not require user-specified labels. Finally, we show that this formulation leads to a deep connection with the popular graph cuts method by Boykov et al. (2001) and Wu and Leahy (1993).