nils-herrmann/clean_scirepeval huggingface dataset:
Image Processing Operations Identification via Convolutional Neural Network

In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.
Deep Generative Models with Learnable Knowledge Constraints

The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited applicability to the diverse DGMs that can lack a Bayesian formulation or even explicit density evaluation. PR also requires constraints to be fully specified a priori, which is impractical or suboptimal for complex knowledge with learnable uncertain parts. In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL. The resulting algorithm is modelagnostic to apply to any DGMs, and is flexible to adapt arbitrary constraints with the model jointly. Experiments on human image generation and templated sentence generation show models with learned knowledge constraints by our algorithm greatly improve over base generative models.
Geometric mechanics of curved crease origami.

Folding a sheet of paper along a curve can lead to structures seen in decorative art and utilitarian packing boxes. Here we present a theory for the simplest such structure: an annular circular strip that is folded along a central circular curve to form a three-dimensional buckled structure driven by geometrical frustration. We quantify this shape in terms of the radius of the circle, the dihedral angle of the fold, and the mechanical properties of the sheet of paper and the fold itself. When the sheet is isometrically deformed everywhere except along the fold itself, stiff folds result in creases with constant curvature and oscillatory torsion. However, relatively softer folds inherit the broken symmetry of the buckled shape with oscillatory curvature and torsion. Our asymptotic analysis of the isometrically deformed state is corroborated by numerical simulations that allow us to generalize our analysis to study structures with multiple curved creases.
A novel approach for ontology-based dimensionality reduction for web text document classification

Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering… etc.). This paper proposes an efficient approach to be used in reducing the size of the feature vector for web text document classification process. This approach is based on using WordNet ontology, utilizing the benefit of its hierarchal structure, to eliminate words from the generated feature vector that has no relation with any of WordNet lexical categories; this leads to the reduction of the feature vector size without losing information on the text. For mining tasks, the Vector Space Model (VSM) is used to represent text documents and the Term Frequency Inverse Document Frequency (TFIDF) is used as a term weighting method. The proposed ontology based approach was evaluated against the Principal component analysis (PCA) approach using several experiments. The experimental results reveal the effectiveness of our proposed approach against other traditional approaches to achieve a better classification accuracy, F-measure, precision, and recall
A Multimode 1-MHz PFC Front End With Digital Peak Current Modulation

This work presents a novel mixed-signal control scheme for a boost power factor correction (PFC) rectifier. The digital controller modulates the inductor peak current to produce a low-distortion ac line current in discontinuous conduction mode (DCM) and continuous conduction mode (CCM), without the need for average current sensing. A lookup table (LUT) optimizes efficiency at low input currents, by allowing operation at 125-500-kHz DCM based on calculated thresholds. At high input currents, the converter operates at 1-MHz CCM for reduced inductor footprint. An analog off-time generator with a digital frequency locked loop facilitates CCM operation, eliminating the need for slope compensation in the current loop and reduces frequency variations. The LUT is programmed with an adaptive output voltage of 250/450 V for low/high mains line voltage (85-265 Vrms) to optimize efficiency over a broad range of conditions. The 150-W PFC prototype operates up to 1 MHz with a peak efficiency of 95% and a total harmonic distortion of 5%.
A Review and Study on Fingerprint Based Gender Classification using Classified Techniques

In the era of technology and science, a gender classification have immense value. Fingerprint based gender classification helps to analyze the data in easy way and help to sort out the data. Now a days finger print based gender classification is seen in civilian, industrial, commercial, and unique Id of nation as AADHAR card. As the fingerprints are unique, gender classification helps to minimize the large data. It is utilized by using simple scanner which is available in affordable prices. This review study introduces various methods of fingerprint based gender classification, its limitations and strengths. After having study of this review we can try to increase the efficiency which will help for further research work also. This paper will help to design and develop an efficient algorithm for the fingerprint based intelligent gender identification system and after testing the samples, the performance efficiency of the proposed system found to be 88% for female and 78 % for male.
Assessment of Knowledge Regarding Oral Hygiene among Parents of Pre-School Children Attending Pediatric Out Patient Department in Dhulikhel Hospital.

Level of knowledge regarding oral hygiene among the parents of pre-school children plays an important role on maintaining the good oral hygiene of their children. In Nepal, sufficient research has not been carried out on this area.



Objective of this study is to assess the level of knowledge on oral hygiene of preschool children's parents attending pediatric outpatient department in Dhulikhel Hospital.



A descriptive study was conducted from November 2012 to January 2013 among one hundred parents of preschool children visiting pediatrics outpatient department of Dhulikhel Hospital. Paper and pencil based semi structured questionnaire was used for collecting data. Questions related to demographic information and knowledge were asked. Thirty questions were used for assessing knowledge level. Knowledge score was calculated by allocating one point for each correct answer and zero point for each wrong answer. Analyzed data were presented in terms of numbers and percentages. Total knowledge scores were categorized based on percentage. Knowledge score was categorized on four group - exclusive intervals - namely-poor (0-40%), moderate (40-60%), good (60-80%) and excellent (80-100%). Mann-Whitney U test and Kruskal-Wallis test were applied to check significance difference and chisquare test was used to check association among different background characteristic.



It was found that 81% had moderate knowledge, 15% had poor knowledge and 4% had good knowledge about oral hygiene. Median knowledge score was found to be 15 with range 10 to 21. Following variables were found to be significant difference on knowledge category: Education status (p<0.001), education level (p= 0.041), past experience about oral health problem (p = 0.008), Further significant association was found between knowledge category and educational status (p<0.001) and between knowledge category and past experience (p< 0.001).



Knowledge regarding oral hygiene was found satisfactory among the parents of preschool children visiting pediatric OPD of Dhulikhel Hospital.
Blind Image Denoising via Dependent Dirichlet Process Tree

Most existing image denoising approaches assumed the noise to be homogeneous white Gaussian distributed with known intensity. However, in real noisy images, the noise models are usually unknown beforehand and can be much more complex. This paper addresses this problem and proposes a novel blind image denoising algorithm to recover the clean image from noisy one with the unknown noise model. To model the empirical noise of an image, our method introduces the mixture of Gaussian distribution, which is flexible enough to approximate different continuous distributions. The problem of blind image denoising is reformulated as a learning problem. The procedure is to first build a two-layer structural model for noisy patches and consider the clean ones as latent variable. To control the complexity of the noisy patch model, this work proposes a novel Bayesian nonparametric prior called “Dependent Dirichlet Process Tree” to build the model. Then, this study derives a variational inference algorithm to estimate model parameters and recover clean patches. We apply our method on synthesis and real noisy images with different noise models. Comparing with previous approaches, ours achieves better performance. The experimental results indicate the efficiency of the proposed algorithm to cope with practical image denoising tasks.
Grasping from the air: Hovering capture and load stability

This paper reports recent research efforts to advance the functionality of Unmanned Aerial Vehicles (UAVs) beyond passive observation to active interaction with and manipulation of objects. The archetypical aerial manipulation task — grasping objects during flight — is difficult due to the unstable dynamics of rotorcraft and coupled object-aircraft motion. In this paper, we analyze key challenges encountered when lifting a grasped object and transitioning into laden free-flight. We demonstrate that dynamic load disturbances introduced by the load mass will be rejected by a helicopter with PID flight control. We determine stability bounds in which the changing mass-inertia parameters of the system due to the grasped object will not destabilize this flight controller. The conditions under which transient partial contact mechanics of objects resting on a surface will not induce instability are identified. We demonstrate grasping and retrieval of a variety of objects while hovering, without touching the ground, using the Yale Aerial Manipulator testbed.
Complex input convolutional neural networks for wide angle SAR ATR

To date, automatic target recognition (ATR) techniques in synthetic aperture radar (SAR) imagery have largely focused on features that use only the magnitude part of SAR's complex valued magnitude-plus-phase history. While such techniques are often very successful, they inherently ignore the significant amount of discriminatory information available in the phase. This paper describes a method for exploiting the complex information for ATR by using a convolutional neural network (CNN) that accepts fully complex input features. We show a performance leap from 87.30% to 99.21% accuracy on real collected wide-angle SAR data with the use of complex features.
Selection of Optimal Stopping Time for Nonlinear Diffusion Filtering

We develop a novel time-selection strategy for iterative image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the filtered image is minimized. The new method is applicable to any images where the noise to be removed is uncorrelated with the signal, under the assumptions that the filter used is suitable for the given type of data, and that neither the additive noise nor the filtering procedure alter the average gray value; no other knowledge (e.g. the noise variance, training data etc.) is needed. We analyse the theoretical properties of the method, then test the performance of our time estimation procedure experimentally, and demonstrate that it yields near-optimal results for a wide range of noise levels and for various filtering methods.
Compressed-air energy storage systems for stand-alone off-grid photovoltaic modules

In this work, a low-cost, low-volume, low-maintenance, small-scale compressed-air energy storage system (SS-CAES) is proposed, which can be used in conjunction with off-grid stand-alone photo-voltaic panels, for powering appliances and residential units in order to minimize the dependency on centralized power system grids. As a first step towards achieving this objective, we have designed and examined the compression efficiency of a single-stage, isothermal compression system that utilizes a fluid piston. Preliminary results clearly establish that the prototype holds enormous promise as energy storage systems that are compatible with renewable energy sources such as solar.
An RNN-Based Quantized F0 Model with Multi-Tier Feedback Links for Text-to-Speech Synthesis

A recurrent-neural-network-based F0 model for text-to-speech (TTS) synthesis that generates F0 contours given textual features is proposed. In contrast to related F0 models, the proposed one is designed to learn the temporal correlation of F0 contours at multiple levels. The frame-level correlation is covered by feeding back the F0 output of the previous frame as the additional input of the current frame; meanwhile, the correlation over long-time spans is similarly modeled but by using F0 features aggregated over the phoneme and syllable. Another difference is that the output of the proposed model is not the interpolated continuous-valued F0 contour but rather a sequence of discrete symbols, including quantized F0 levels and a symbol for the unvoiced condition. By using the discrete F0 symbols, the proposed model avoids the influence of artificially interpolated F0 curves. Experiments demonstrated that the proposed F0 model, which was trained using a dropout strategy, generated smooth F0 contours with relatively better perceived quality than those from baseline RNN models.
Knot theory in understanding proteins.

This paper aims to enthuse mathematicians, especially topologists, knot theorists and geometers to examine problems in the study of proteins. We have highlighted those advances and breakthroughs in knot theory that directly and indirectly help in understanding proteins. We have discussed the phenomena of knotting of protein backbone. This paper also provides a few open questions for knot theorists, the answers to which will help in further understanding of proteins.
Advertisement image classification using convolutional neural network

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, plant (fruits, flowers, leaf…) classification base on images. This paper proposes the image classification model applied for identifying the display of the online advertisement. The proposal model uses Convolutional Neural Network with two parameters (n, m) where n is a number of layers and m is number of filters in Conv layer. The proposed model is called nLmF-CNN. The suitable values of parameters (n, m) for advertisement image classification are identified by experiments. The input data of the proposed model are online captured images. The processing components of nLmF-CNN are developed as deep neural networks using ConvNetJs library. The output of the proposed model is YES/NO. YES means that the advertisements display clearly. NO means that the advertisements do not display or not clear. The experimental results 86% in our normalizing dataset showed the feasibility of a proposed model nLmF-CNN.
Hardware IP Protection through Gate-Level Obfuscation

Hardware Intellectual Property (IP) cores have emerged as an integral part of modern System-on-Chip (SoC) designs. However, recent trends of reverse engineering pose major threat to IP-based SOC design flow. The paper proposes a novel approach for hardware IP protection using gate-level obfuscation, which could make design less intelligible in order to neutralize or weaken the effect of reverse engineering. The basic idea is to hide the original logic function by using Physical Unclonable Function (PUF), multiplexer and configurable logic, so that it is difficult for reverse engineering attackers to get complete information of circuit net list. The design methodology could be applied in combinational logic and sequential logic. Simulation results on several IP cores show that we can achieve high levels of security through a well-formulated obfuscation scheme at less than 10% area overhead under delay constraint.
A multiscale measure for mixing

We present a multiscale measure for mixing that is based on the concept of weak convergence and averages the “mixedness” of an advected scalar field at various scales. This new measure, referred to as the Mix-Norm, resolves the inability of the L2 variance of the scalar density field to capture small-scale variations when advected by chaotic maps or flows. In addition, the Mix-Norm succeeds in capturing the efficiency of a mixing protocol in the context of a particular initial scalar field, wherein Lyapunov-exponent based measures fail to do so. We relate the Mix-Norm to the classical ergodic theoretic notion of mixing and present its formulation in terms of the power spectrum of the scalar field. We demonstrate the utility of the Mix-Norm by showing how it measures the efficiency of mixing due to various discrete dynamical systems and to diffusion. In particular, we show that the Mix-Norm can capture known exponential and algebraic mixing properties of certain maps. We also analyze numerically the behaviour of scalar fields evolved by the Standard Map using the Mix-Norm.
The role of emotions for moral judgments depends on the type of emotion and moral scenario.

Emotions seem to play a critical role in moral judgment. However, the way in which emotions exert their influence on moral judgments is still poorly understood. This study proposes a novel theoretical approach suggesting that emotions influence moral judgments based on their motivational dimension. We tested the effects of two types of induced emotions with equal valence but with different motivational implications (anger and disgust), and four types of moral scenarios (disgust-related, impersonal, personal, and beliefs) on moral judgments. We hypothesized and found that approach motivation associated with anger would make moral judgments more permissible, while disgust, associated with withdrawal motivation, would make them less permissible. Moreover, these effects varied as a function of the type of scenario: the induced emotions only affected moral judgments concerning impersonal and personal scenarios, while we observed no effects for the other scenarios. These findings suggest that emotions can play an important role in moral judgment, but that their specific effects depend upon the type of emotion induced. Furthermore, induced emotion effects were more prevalent for moral decisions in personal and impersonal scenarios, possibly because these require the performance of an action rather than making an abstract judgment. We conclude that the effects of induced emotions on moral judgments can be predicted by taking their motivational dimension into account. This finding has important implications for moral psychology, as it points toward a previously overlooked mechanism linking emotions to moral judgments.
SYNTHETIC APERTURE RADAR IMAGE FORMATON PROCESS : APPLICATION TO A REGION OF NORTH ALGERIA

Synthetic aperture radar imaging provides high resolution images of large areas. The intensities of pixels in a SAR image are based on the spatial orientation, roughness, and dielectric constant of the surface imaged. SAR is an active sensor, transmitting its own energy, and then measuring the return scattered by the earth’s surface back to the satellite’s antenna. SAR processing is the transformation of raw SAR signal data into a spatial image. In this communication, we present a contribution concerning implementation of a SAR image formation with the IDL language. This process is performed in a frequency domain correlation of the received signal including range compression, range migration, and azimuth compression. The output is the final step in which the data is transposed back into its original format and written to an output file. The data used are raw SAR image of scene of north Algeria of size 5616*28000 pixels of the 16 of February 1996. Evaluation of the method studied here is made on a Region of Reghaia lake ( near of Algiers city) of 2048*5616 pixels.
Automatic Machine Translation Evaluation: A Qualitative Approach

La consulta d’aquesta tesi queda condicionada a l’acceptació de les següents condicions  La difusió d’aquesta tesi per mitjà del servei TDX (www.tdx.cat) i a través del Dipòsit Digital de la UB (diposit.ub.edu) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei TDX ni al Dipòsit Digital de la UB. No s’autoritza la presentació del seu contingut en una finestra o marc aliè a TDX o al Dipòsit Digital de la UB (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.
Piecewise 3D Euler spirals

3D Euler spirals are visually pleasing, due to their property of having their curvature and their torsion change linearly with arc-length. This paper presents a novel algorithm for fitting piecewise 3D Euler spirals to 3D curves with G2 continuity and torsion continuity. The algorithm can also handle sharp corners. Our piecewise representation is invariant to similarity transformations and it is close to the input curves up to an error tolerance.
Completion and Reconstruction with Primitive Shapes

We consider the problem of reconstruction from incomplete point-clouds. To find a closed mesh the reconstruction is guided by a set of primitive shapes which has been detected on the input point-cloud (e.g. planes, cylinders etc.). With this guidance we not only continue the surrounding structure into the holes but also synthesize plausible edges and corners from the primitives’ intersections. To this end we give a surface energy functional that incorporates the primitive shapes in a guiding vector field. The discretized functional can be minimized with an efficient graphcut algorithm. A novel greedy optimization strategy is proposed to minimize the functional under the constraint that surface parts corresponding to a given primitive must be connected. From the primitive shapes our method can also reconstruct an idealized model that is suitable for use in a CAD system.