nils-herrmann/clean_scirepeval huggingface dataset:
Blind image deconvolution: theory and applications

Blind image deconvolution: theory and applications Images are ubiquitous and indispensable in science and everyday life. Mirroring the abilities of our own human visual system, it is natural to display observations of the world in graphical form. Images are obtained in areas 1 2 Blind Image Deconvolution: problem formulation and existing approaches ranging from everyday photography to astronomy, remote sensing, medical imaging, and microscopy. In each case, there is an underlying object or scene we wish to observe; the original or true image is the ideal representation of the observed scene. Yet the observation process is never perfect: there is uncertainty in the measurements , occurring as blur, noise, and other degradations in the recorded images. Digital image restoration aims to recover an estimate of the original image from the degraded observations. The key to being able to solve this ill-posed inverse problem is proper incorporation of prior knowledge about the original image into the restoration process. Classical image restoration seeks an estimate of the true image assuming the blur is known. In contrast, blind image restoration tackles the much more difficult, but realistic, problem where the degradation is unknown. In general, the degradation is nonlinear (including, for example, saturation and quantization) and spatially varying (non uniform motion, imperfect optics); however, for most of the work, it is assumed that the observed image is the output of a Linear Spatially Invariant (LSI) system to which noise is added. Therefore it becomes a Blind Deconvolution (BD) problem, with the unknown blur represented as a Point Spread Function (PSF). Classical restoration has matured since its inception, in the context of space exploration in the 1960s, and numerous techniques can be found in the literature (for recent reviews see [1, 2]). These differ primarily in the prior information about the image they include to perform the restoration task. The earliest algorithms to tackle the BD problem appeared as long ago as the mid-1970s , and attempted to identify known patterns in the blur; a small but dedicated effort followed through the late 1980s (see for instance [5, 6, 7, 8, 9]), and a resurgence was seen in the 1990s (see the earlier reviews in [10, 11]). Since then, the area has been extensively explored by the signal processing , astronomical, and optics communities. Many of the BD algorithms have their roots in estimation theory, linear algebra, and numerical analysis. An important question …
Isogeometric Analysis : Approximation , stability and error estimates for h-refined meshes

We begin the mathematical study of Isogeometric Analysis based on NURBS (non-uniform rational B-splines.) Isogeometric Analysis is a generalization of classical Finite Element Analysis (FEA) which possesses improved properties. For example, NURBS are capable of more precise geometric representation of complex objects and, in particular, can exactly represent many commonly engineered shapes, such as cylinders, spheres and tori. Isogeometric Analysis also simplifies mesh refinement because the geometry is fixed at the coarsest level of refinement and is unchanged throughout the refinement process. This eliminates geometrical errors and the necessity of linking the refinement procedure to a CAD representation of the geometry, as in classical FEA. In this work we study approximation and stability properties in the context of h-refinement. We develop approximation estimates based on a new BrambleHilbert lemma in so-called “bent” Sobolev spaces appropriate for NURBS approximations and we establish inverse estimates similar to ones for finite elements. We apply the theoretical results to several cases of interest including elasticity, isotropic incompressible elasticity and Stokes flow, and advection-diffusion, and perform numerical tests which corroborate the mathematical results. We also perform numerical calculations that involve hypotheses outside our theory and these suggest that there are many other interesting mathematical properties of Isogeometric Analysis yet to be proved.
MuseumFinland - Finnish museums on the semantic web

This article presents the semantic portal MuseumFinland for publishing heterogeneous museum collections on the Semantic Web. It is shown how museums with their semantically rich and interrelated collection content can create a large, consolidated semantic collection portal together on the web. By sharing a set of ontologies, it is possible to make collections semantically interoperable, and provide the museum visitors with intelligent content-based search and browsing services to the global collection base. The architecture underlying MuseumFinland separates generic search and browsing services from the underlying application dependent schemas and metadata by a layer of logical rules. As a result, the portal creation framework and software developed has been applied successfully to other domains as well. MuseumFinland got the Semantic Web Challence Award (second prize) in 2004.
CR-GAN: Learning Complete Representations for Multi-view Generation

Generating multi-view images from a single-view input is an essential yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn “incomplete” representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network. We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning pathways collaborate and compete in a parameter-sharing manner, yielding considerably improved generalization ability to “unseen” dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. The experimental results prove that CR-GAN significantly outperforms stateof-the-art methods, especially when generating from “unseen” inputs in wild conditions.
Enriching Structured Knowledge with Open Information

We propose an approach for semantifying web extracted facts. In particular, we map subject and object terms of these facts to instances; and relational phrases to object properties defined in a target knowledge base. By doing this we resolve the ambiguity inherent in the web extracted facts, while simultaneously enriching the target knowledge base with a significant number of new assertions. In this paper, we focus on the mapping of the relational phrases in the context of the overall work ow. Furthermore, in an open extraction setting identical semantic relationships can be represented by different surface forms, making it necessary to group these surface forms together. To solve this problem we propose the use of markov clustering. In this work we present a complete, ontology independent, generalized workflow which we evaluate on facts extracted by Nell and Reverb. Our target knowledge base is DBpedia. Our evaluation shows promising results in terms of producing highly precise facts. Moreover, the results indicate that the clustering of relational phrases pays of in terms of an improved instance and property mapping.
Founding Cryptography on Oblivious Transfer

Suppose your netmail is being erratically censored by Captain Yossarian. Whenever you send a message, he censors each bit of the message with probability 1/2, replacing each censored bit by some reserved character. Well versed in such concepts as redundancy, this is no real problem to you. The question is, can it actually be turned around and used to your advantage? We answer this question strongly in the affirmative. We show that this protocol, more commonly known as oblivious transfer, can be used to simulate a more sophisticated protocol, known as oblivious circuit evaluation([Y]). We also show that with such a communication channel, one can have completely noninteractive zero-knowledge proofs of statements in NP. These results do not use any complexity-theoretic assumptions. We can show that they have applications to a variety of models in which oblivious transfer can be done.
Non-Invasive Glucose Measurement by Use of Metabolic Heat Conformation Method

A non-invasive glucose measurement system based on the method of metabolic heat conformation (MHC) is presented in this paper. This system consists of three temperature sensors, two humidity sensors, an infrared sensor and an optical measurement device. The glucose level can be deduced from the quantity of heat dissipation, blood flow rate of local tissue and degree of blood oxygen saturation. The methodology of the data process and the measurement error are also analyzed. The system is applied in a primary clinical test. Compared with the results of a commercial automated chemistry analyzer, the correlation coefficient of the collected data from the system is 0.856. Result shows that the correlation coefficient improves when the factor of heat dissipated by evaporation of the skin is added in. A non-invasive method of measuring the blood flow rate of local tissue by heat transmission between skin and contacted conductor is also introduced. Theoretical derivation and numerical simulation are completed as well. The so-called normalized difference mean (NDM) is chosen to express the quantity of the blood flow rate. The correlation coefficient between the blood flow rates by this method and the results of a Doppler blood flow meter is equal to 0.914.
Real time robust L1 tracker using accelerated proximal gradient approach

Recently sparse representation has been applied to visual tracker by modeling the target appearance using a sparse approximation over a template set, which leads to the so-called L1 trackers as it needs to solve an ℓ1 norm related minimization problem for many times. While these L1 trackers showed impressive tracking accuracies, they are very computationally demanding and the speed bottleneck is the solver to ℓ1 norm minimizations. This paper aims at developing an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers. In our proposed L1 tracker, a new ℓ1 norm related minimization model is proposed to improve the tracking accuracy by adding an ℓ1 norm regularization on the coefficients associated with the trivial templates. Moreover, based on the accelerated proximal gradient approach, a very fast numerical solver is developed to solve the resulting ℓ1 norm related minimization problem with guaranteed quadratic convergence. The great running time efficiency and tracking accuracy of the proposed tracker is validated with a comprehensive evaluation involving eight challenging sequences and five alternative state-of-the-art trackers.
A Li-ion battery management system based on CAN-bus for electric vehicle

A battery management system (BMS) based on the CAN-bus was designed for the Li-ion battery pack which consisted of many series-connected battery cells and was distributed dispersedly on the electric vehicle (EV). The BMS consisted of one master module and several sampling modules. The hardware design of the sampling circuit and the CAN expanding circuit was introduced. The strategies of the battery SOC (state of charge) estimation and the battery safety management were also presented.
Smartphone-based Wi-Fi pedestrian-tracking system tolerating the RSS variance problem

The Wi-Fi fingerprinting (WF) technique normally suffers from the RSS (Received Signal Strength) variance problem caused by environmental changes that are inherent in both the training and localization phases. Several calibration algorithms have been proposed but they only focus on the hardware variance problem. Moreover, smartphones were not evaluated and these are now widely used in WF systems. In this paper, we analyze various aspect of the RSS variance problem when using smartphones for WF: device type, device placement, user direction, and environmental changes over time. To overcome the RSS variance problem, we also propose a smartphone-based, indoor pedestrian-tracking system. The scheme uses the location where the maximum RSS is observed, which is preserved even though RSS varies significantly. We experimentally validate that the proposed system is robust to the RSS variance problem.
Tangible 3D: Hand Gesture Interaction for Immersive 3D Modeling

Most of all interaction tasks relevant for a general three-dimensional virtual environment can be supported by 6DOF control and grab/select input. Obviously a very efficient method is direct manipulation with bare hands, like in real environment. This paper shows the possibility to perform non-trivial tasks using only a few well-known hand gestures, so that almost no training is necessary to interact with 3D-softwares. Using this gesture interaction we have built an immersive 3D modeling system with 3D model representation based on a mesh library, which is optimized not only for real-time rendering but also accommodates for changes of both vertex positions and mesh connectivity in real-time. For performing the gesture interaction, the user's hand is marked with just four fingertipthimbles made of inexpensive material as simple as white paper. Within our scenario, the recognized hand gestures are used to select, create, manipulate and deform the meshes in a spontaneous and intuitive way. All modeling tasks are performed wirelessly through a camera/vision tracking method for the head and hand interaction.
Resource Allocation in NFV: A Comprehensive Survey

Network functions virtualization (NFV) is a new network architecture framework where network function that traditionally used dedicated hardware (middleboxes or network appliances) are now implemented in software that runs on top of general purpose hardware such as high volume server. NFV emerges as an initiative from the industry (network operators, carriers, and manufacturers) in order to increase the deployment flexibility and integration of new network services with increased agility within operator's networks and to obtain significant reductions in operating expenditures and capital expenditures. NFV promotes virtualizing network functions such as transcoders, firewalls, and load balancers, among others, which were carried out by specialized hardware devices and migrating them to software-based appliances. One of the main challenges for the deployment of NFV is the resource allocation of demanded network services in NFV-based network infrastructures. This challenge has been called the NFV resource allocation (NFV-RA) problem. This paper presents a comprehensive state of the art of NFV-RA by introducing a novel classification of the main approaches that pose solutions to solve it. This paper also presents the research challenges that are still subject of future investigation in the NFV-RA realm.
A Proxy Design to Leverage the Interconnection of CoAP Wireless Sensor Networks with Web Applications

In this paper, we present the design of a Constrained Application Protocol (CoAP) proxy able to interconnect Web applications based on Hypertext Transfer Protocol (HTTP) and WebSocket with CoAP based Wireless Sensor Networks. Sensor networks are commonly used to monitor and control physical objects or environments. Smart Cities represent applications of such a nature. Wireless Sensor Networks gather data from their surroundings and send them to a remote application. This data flow may be short or long lived. The traditional HTTP long-polling used by Web applications may not be adequate in long-term communications. To overcome this problem, we include the WebSocket protocol in the design of the CoAP proxy. We evaluate the performance of the CoAP proxy in terms of latency and memory consumption. The tests consider long and short-lived communications. In both cases, we evaluate the performance obtained by the CoAP proxy according to the use of WebSocket and HTTP long-polling.
Comparative efficacy, speed, and adverse effects of three PTSD treatments: exposure therapy, EMDR, and relaxation training.

The authors examined the efficacy, speed, and incidence of symptom worsening for 3 treatments of posttraumatic stress disorder (PTSD): prolonged exposure, relaxation training, or eye movement desensitization and reprocessing (EMDR; N = 60). Treaments did not differ in attrition, in the incidence of symptom worsening, or in their effects on numbing and hyperarousal symptoms. Compared with EMDR and relaxation training, exposure therapy (a) produced significantly larger reductions in avoidance and reexperiencing symptoms, (b) tended to be faster at reducing avoidance, and (c) tended to yield a greater proportion of participants who no longer met criteria for PTSD after treatment. EMDR and relaxation did not differ from one another in speed or efficacy.
NCRF++: An Open-source Neural Sequence Labeling Toolkit

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch1, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTMCRF, facilitating reproducing and refinement on those methods.
Robotic Grasping of Novel Objects using Vision

We consider the problem of grasping novel objects, specifically ones that are being seen for the first time through vision. Grasping a previously unknown object, one for which a 3-d model is not available, is a challenging problem. Further, even if given a model, one still has to decide where to grasp the object. We present a learning algorithm that neither requires, nor tries to build, a 3-d model of the object. Given two (or more) images of an object, our algorithm attempts to identify a few points in each image corresponding to good locations at which to grasp the object. This sparse set of points is then triangulated to obtain a 3-d location at which to attempt a grasp. This is in contrast to standard dense stereo, which tries to triangulate every single point in an image (and often fails to return a good 3-d model). Our algorithm for identifying grasp locations from an image is trained via supervised learning, using synthetic images for the training set. We demonstrate this approach on two robotic manipulation platforms. Our algorithm successfully grasps a wide variety of objects, such as plates, tape-rolls, jugs, cellphones, keys, screwdrivers, staplers, a thick coil of wire, a strangely shaped power horn, and others, none of which were seen in the training set. We also apply our method to the task of unloading items from dishwashers.
Applauses in hotel reviews: Genuine or deceptive?

With the profusion of social media, users increasingly browse through hotel reviews posted in websites such as TripAdvisor.com or Expedia.com to make a booking. Concurrently, contributing deceptive reviews to unduly applaud hotels is fast becoming a well-established e-business malpractice. Therefore, analyzing differences between genuine and deceptive reviews has become a pressing issue. Though such differences are generally difficult to detect, there could be telltale signs in terms of readability, genre, and writing style of reviews. This paper thus conducts a linguistic analysis to investigate the extent to which readability, genre, and writing style could predict review authenticity. Drawing data from a publicly available secondary dataset that includes 400 genuine and 400 deceptive reviews for hotels, results indicate that readability and writing style could be significant predictors of review authenticity. With respect to review genre however, differences between genuine and deceptive reviews appeared largely blurred. The implications of the findings are discussed. Finally, the paper concludes with notes on limitations and future research directions.
Learning modular neural network policies for multi-task and multi-robot transfer

Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy classes, but exacerbates the challenge of data collection, since such methods tend to be less efficient than RL with low-dimensional, hand-designed representations. Transfer learning can mitigate this problem by enabling us to transfer information from one skill to another and even from one robot to another. We show that neural network policies can be decomposed into “task-specific” and “robot-specific” modules, where the task-specific modules are shared across robots, and the robot-specific modules are shared across all tasks on that robot. This allows for sharing task information, such as perception, between robots and sharing robot information, such as dynamics and kinematics, between tasks. We exploit this decomposition to train mix-and-match modules that can solve new robot-task combinations that were not seen during training. Using a novel approach to train modular neural networks, we demonstrate the effectiveness of our transfer method for enabling zero-shot generalization with a variety of robots and tasks in simulation for both visual and non-visual tasks.
Smooth eye movement interaction using EOG glasses

Orbits combines a visual display and an eye motion sensor to allow a user to select between options by tracking a cursor with the eyes as the cursor travels in a circular path around each option. Using an off-the-shelf Jins MEME pair of eyeglasses, we present a pilot study that suggests that the eye movement required for Orbits can be sensed using three electrodes: one in the nose bridge and one in each nose pad. For forced choice binary selection, we achieve a 2.6 bits per second (bps) input rate at 250ms per input. We also inntroduce Head Orbits, where the user fixates the eyes on a target and moves the head in synchrony with the orbiting target. Measuring only the relative movement of the eyes in relation to the head, this method achieves a maximum rate of 2.0 bps at 500ms per input. Finally, we combine the two techniques together with a gyro to create an interface with a maximum input rate of 5.0 bps.
Data-Aided Channel Estimation in Large Antenna Systems

This paper is concerned with a uplink scheme for multicell large antenna systems. We study a channel estimation technique where partially decoded data is used to estimate the channel. We show that there are two types of interference components in this scheme that do not vanish even when the number of antennas grows to infinity. The first type, referred to as cross-contamination, is due to the correlation among the data signals from different users. The second type, referred to as self-contamination, is due to the dependency between the channel estimate and the estimation error. Cross contamination is in principle similar to pilot contamination in a conventional pilot-based channel estimation scheme, while self-contamination is unique for the data-aided scheme. For efficient use of the channel, the data part in a signaling frame is typically much longer than the pilot part for a practical system. Consequently, compared with pilot signals, data signals naturally have lower cross correlation. This fact reduces the cross-contamination effect in the data-aided scheme. Furthermore, self-contamination can be effectively suppressed by iterative processing. These results are confirmed by both analyses and simulations.