nils-herrmann/clean_scirepeval huggingface dataset:
CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval

We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more challenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face image datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary Pattern (LBP) descriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.
Expanded Parts Model for Human Attribute and Action Recognition in Still Images

We propose a new model for recognizing human attributes (e.g. wearing a suit, sitting, short hair) and actions (e.g. running, riding a horse) in still images. The proposed model relies on a collection of part templates which are learnt discriminatively to explain specific scale-space locations in the images (in human centric coordinates). It avoids the limitations of highly structured models, which consist of a few (i.e. a mixture of) 'average' templates. To learn our model, we propose an algorithm which automatically mines out parts and learns corresponding discriminative templates with their respective locations from a large number of candidate parts. We validate the method on recent challenging datasets: (i) Willow 7 actions [7], (ii) 27 Human Attributes (HAT) , and (iii) Stanford 40 actions [37]. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.
Randomized Algorithms for Cross-Layer Network Control

We study the problem of joint congestion control and scheduling in wireless networks. We model the wireless network as a directed graph G=(V, E), where V denotes the set of nodes and E denotes the set of wireless links between the nodes. We propose a joint congestion control and scheduling scheme that achieves a fraction d1(G) of the capacity region, where d1(G) depends on certain structural properties of graph G as well as the nature of interference constraints. For specific families of graphs, which can represent a wide variety of wireless networks, d1(G) has been upper bounded by a factor independent of the number of nodes in the network for a wide range of interference models. The scheduling element of our joint congestion control and scheduling scheme is the maximal scheduling policy considered in many of the earlier works. Although, it is widely believed to be amenable to distributed implementation, no algorithms have been proposed for its implementation, except under the node-exclusive interference model which is suitable only for networks in which adjacent nodes can transmit over non-interfering channels. We propose a randomized algorithm for implementing maximal scheduling policy under a 2-hop interference model which is suitable for networks with a limited number of non-interfering channels (e.g., IEEE 802.11 DSSS networks)
Comparative analysis of homologous buildings using range imaging

This paper reports on a novel application of computer vision and image processing technologies to an interdisciplinary project in architectural history that seeks to help identify and visualize differences between homologous buildings constructed to a common template design. By identifying the mutations in homologous buildings, we assist humanists in giving voice to the contributions of the myriad additional “authors” for these buildings beyond their primary designers. We develop a framework for comparing 3D point cloud representations of homologous buildings captured using lidar: focusing on identifying similarities and differences, both among 3D scans of different buildings and between the 3D scans and the design specifications of architectural drawings. The framework addresses global and local alignment for highlighting gross differences as well as differences in individual structural elements and provides methods for readily highlighting the differences via suitable visualizations. The framework is demonstrated on pairs of homologous buildings selected from the Canadian and Ottoman rail networks. Results demonstrate the utility of the framework confirming differences already apparent to the humanist researchers and also revealing new differences that were not previously observed.
High contrast stochastic screenwatermarks for color halftone prints

Embedded watermarks in printed halftone images, which can subsequently be detected using an visual aid or using a watermark detection algorithm on a scan of the image, are of interest in wide range of applications. For black and white halftone printing using stochastic screens, digital watermarks that are embedded as correlations in the halftone screen have been previously proposed. Here we present a novel extension of these watermarks to color that produces a high contrast watermark by using the colorant separations coherently with a single watermarked stochastic screen and performing detection coherently across the color separations. Compared with independent watermarking of the halftone separations, the resulting watermark offers significantly higher contrast in the detected image.
Hybrid Solar-Wind Energy Harvesting for Embedded Applications: Supercapacitor-Based System Architectures and Design Tradeoffs

To enable off-grid deployments of autonomous systems for extended operational durations, robust energy harvesting in the medium power range (1-10 W) is essential. Supercapacitor-based solar energy harvesters have emerged as a popular alternative due to their long lifetime under repeated charge-discharge cycles, low maintenance, environmental friendliness, and energy predictability and scalability. Despite their advantages, such systems are not well matched with applications that require power continuously over their operational lifetime because solar power is unavailable during nights and severely reduced on cloudy days. For such applications, it is beneficial to combine solar power with another power source-such as wind-that exhibits complementary availability. In this paper, we present multiple solar/wind (hybrid) supercapacitor-based harvesters, leveraging existing open-source solar-only harvester designs. Our designs center around three main categories that i) add wind harvesting capability to create a wind-only harvesting system, ii) use multiple harvesters for utilizing hybrid sources of power and for providing fault tolerance, or iii) use a single harvester in a time multiplexed configuration to simultaneously harvest from multiple power sources. We provide extensive experimental results to document the functionality and operational performance of a representative set of these designs.
A Study on Geo-location Authentication Techniques

The explosive growth of the information over the internet makes a crucial task to make network safe and secure with many users hits and connectivity. Geo location based network provides context-aware services that help associate location with users and content. This paper proposes a survey between different technique to which is related to Geo location based Networks. The purpose of Geo location based network is to secure system from disparate user, so that the right user can take the right decision for the security of the items which are relevant for them. In this paper we are comparing different techniques on different aspects of systems which is used in Geo location based network.
Maximum weighted matching with interference constraints

In this paper, we study the problem of utility maximization in multi-hop wireless systems. To study the effect of wireless interference constraints on the utility maximization problem, we introduce a new class of weighted matching problems, namely maximum weighted K-valid matching problems (MWKVMPs). For K = 1, MWKVMP corresponds to the well studied maximum weighted matching problem (MWMP) in the literature, which can be solved in polynomial time. We prove several interesting results concerning the hardness of these problems for K ges 2. In particular, we show that MWKVMP does not even belong to APX; where APX denotes the class of problems to which a constant factor approximation can be obtained in polynomial time. Our results have strong implications concerning the hardness of scheduling transmissions in multi-hop wireless systems
Efficient Skin Region Segmentation Using Low Complexity Fuzzy Decision Tree Model

We propose an efficient skin region segmentation methodology using low complexity fuzzy decision tree constructed over B, G, R colour space. Skin and nonskin training dataset has been generated by using various skin textures obtained from face images of diversity of age, gender, and race people and nonskin pixels obtained from arbitrary thousands of random sampling of nonskin textures. Compact fuzzy model with very few numbers of rules allow to raster scan consumer photographs and classify each pixel as skin or nonskin for various face and human detection applications for embedded platforms.
A dynamic hand gesture recognition system for controlling VLC media player

In this paper we have discussed a low cost system which uses dynamic hand gesture recognition technique to control the VLC media player. This application contains a central computation module which segments the foreground part of the frame using skin detection and approximate median technique. The recognition of gesture is done by creating a Decision Tree, that uses various features extracted from the segmented part. This hand gesture recognition technique introduces a new, natural way to interact with computers.
An adaptive filtering framework for image registration

Image registration is a fundamental task in both image processing and computer vision. We present a novel method for local image registration based on adaptive filtering techniques. We utilize an adaptive filter to estimate and track correspondences among multiple images containing overlapping views of common scene regions. Image pixels are traversed in an order established by space-filling curves, to preserve the contiguity and hence track locally varying registration changes. The algorithm differs from pre-existing work on image registration in that it requires only local information and relatively low computational effort. These characteristics render the method suitable for deployment in imaging sensor networks, toward which the current work is directed. We evaluate the performance of the proposed algorithm using images captured with a digital camera in various real-world scenarios. Experimental results show that the proposed method can significantly improve accuracy and robustness over a global 2D parametric registration and can also outperform the local registration algorithm based on the Lucas-Kanade optical flow technique (Lucas, B. and Kanade, T., 1981).
Capacity Analysis For Orthogonal Halftone Orientation Modulation Channels

Halftone dot orientation modulation has recently been proposed as a method for data hiding in printed images. Extraction of data embedded with halftone orientation modulation is accomplished by computing, from the scanned hardcopy image, detection statistics that uniquely identify the embedded orientation. From a communications perspective, this data hiding setup forms an interesting class of channels with dot orientation as input and a vector of statistics as the output. This paper derives capacity expressions for these channels that allow for numerical evaluation of the capacity. Results provide significant insight for orientation modulation based print-scan resilient data hiding: the capacity varies significantly as a function of the image graylevel and experimentally observed error free data rates closely mirror the variation in capacity.
Set theoretic signal restoration using an error in variables criterion

The restoration of a signal degraded by a stochastic impulse response is formulated as a problem with uncertainties in both the measurements and the impulse response. The method of total least squares, and variants thereof, are effective techniques for solving this class of problems. However, unlike set theoretic estimation schemes, these methods do not allow the incorporation of other a priori information in the estimate. In this correspondence, two new sets motivated by total least squares are introduced for set theoretic estimation. The convexity of these sets is established and the projection operators onto these sets are given. Through simulations, the advantages of the new technique over conventional and older set theoretic schemes for restoration are demonstrated.
An Audio Watermark Designed for Efficient and Robust Resynchronization After Analog Playback

We propose a spread spectrum (SS) audio watermark designed to withstand analog playback, including desynchronization caused by small differences between playback and recording rates. Desynchronization robustness relies on detecting short blocks of a magnitude-only watermark embedded in the frequency domain where the resolution of the SS chips can be reduced. Lost spreading gain due to the lower number of SS chips is compensated using blind dynamic time warping (DTW) detection (does not access original signal). DTW aligns sequences of blocks to improve robustness to interference while mitigating the vulnerabilities of long SS sequences to desynchronization. Results demonstrate that the proposed watermark survives analog playback and warping up to ±2%. Additionally, compared with a recent baseline scheme that uses brute force resampling to search for resynchronization, the proposed watermark is 300 times more computationally efficient, and does not compromise robustness to either desynchronization (e.g., jitter, resampling, time warping, frequency scaling) or non-desynchronizing modifications (e.g., AAC compression and additive noise).
Strategies for resource allocation in wideband CDMA networks for high data rate applications

Present generation wireless networks are being designed considering voice as the primary traffic. They are not suitable to support high data traffic rate. Third-generation standards are proposed to meet the increasing data traffic demand over mobile telephones. Wideband code division multiple access (WCDMA) is a promising technique for radio access in future cellular networks and personal communication systems. This paper deals with the problem of resource allocation in the forward link for wideband CDMA networks. Three types of user classes are assumed with data rate of 9.6 kbit/s, 144 kbit/s and 384 kbit/s respectively. A mixed dynamic and static algorithm is proposed, under the constraint that the base station is total transmitted power-limited. The performance of different classes of traffic is compared. A comparative study for voice and data traffic has been carried out.
Figures of merit for color scanners

In the design and evaluation of color scanners and cameras, it is useful to have a single figure of merit that closely agrees with perceived color accuracy. In the past, several measures of goodness for color scanning filters have been proposed to fulfil such a requirement. Most of the proposed measures have had shortcomings in that they are either based on error metrics in color spaces that are not perceptually uniform, or in that they do not take into account the effects of measurement noise. An extension of the most promising measure, based on linearized CIELAB space, is proposed to obtain a new figure of merit that has a high degree of perceptual relevance and also accounts for the varying noise performance of different filters. The paper also provides a common framework for the different figures of merit and a comprehensive comparison of their computational complexity and reliability.
Multi-View Image Registration for Wide-Baseline Visual Sensor Networks

We present a new dense multi-view registration technique for wide-baseline video/images that integrates a parametric optical flow-based approach with a sparse set of feature correspondences, based on a locally planar approximation of a nonplanar scene. The proposed method can deal with illuminance variations between the views, which is critically important for wide-baseline applications. It differs from existing work on wide-baseline image registration in that it requires only image information and provides dense matching without computing any camera calibration matrices or performing any prior scene segmentation. These characteristics render the method suitable for practical deployment in visual sensor networks, towards which the current work is directed. We demonstrate the performance of the proposed method on simulated multi-view images of a virtual 3D world composed of piece-wise smooth textured surfaces, as well as real wide-baseline images of nonplanar textured surfaces.
3D georegistration of wide area motion imagery by combining SFM and chamfer alignment of vehicle detections to vector roadmaps

We propose a novel framework for accurate 3D georegistration of wide area motion imagery (WAMI), which is a challenging problem because parametric transformations are insufficient for aligning WAMI image frames to a georeferenced coordinate system in urban areas containing tall buildings and 3D structures. Using structure from motion (SfM) we estimate a 3D point cloud for the scene. Independently, we also compute a precise alignment between the roads in the WAMI frames and a georeferenced vector roadmap by detecting locations of moving vehicles and aligning these locations with the roads in the vector roadmap via parametric chamfer matching. The aligned vector roadmap then identifies corresponding pixels in the WAMI frames, which can be triangulated using the SfM camera parameters to obtain a set of sparse but georeferenced points in the SfM 3D coordinate frame that directly enable georegistration of the complete 3D scene point cloud via a similarity transform. The proposed methodology enables 3D georegistration of a sequence of WAMI frames using only georeferenced vector roadmaps, which are readily available, and without requiring independent georeferenced lidar scans that have been used in prior work. Our framework is validated on WAMI dataset including high resolution WAMI frames for the downtown Rochester, NY region. Experimental results demonstrate that the proposed framework produces an accurate georeferenced point cloud representation for the scene.
Optimal resource allocation for wireless video sensors with power-rate-distortion model of imager

We consider optimal resource allocation for wireless video sensors (WVSs), including the image sensor subsystem into the system analysis. By assigning a power-rate-distortion (P- R-D) characteristic for the image sensor, we build a comprehensive P-R-D framework for WVS optimization. Within the scope of the developed framework, we solve the problem of how to allocate power among the image sensor, compression, and transmission modules of a WVS, to achieve the optimal reconstructed video quality under power and rate constraints. To demonstrate the optimization method, we further establish a P-R-D model for an image sensor based on pixel level sigma-delta (ΣΔ) image sensor design. A P-R-D performance analysis for a WVS verifies that including the image sensor in the system optimization procedure can improve the overall video quality and prolong the life-time of the wireless video network.
Unsupervised Learning of Face Representations

We present an approach for unsupervised training of CNNs in order to learn discriminative face representations. We mine supervised training data by noting that multiple faces in the same video frame must belong to different persons and the same face tracked across multiple frames must belong to the same person. We obtain millions of face pairs from hundreds of videos without using any manual supervision. Although faces extracted from videos have a lower spatial resolution than those which are available as part of standard supervised face datasets such as LFW and CASIA-WebFace, the former represent a much more realistic setting, e.g. in surveillance scenarios where most of the faces detected are very small. We train our CNNs with the relatively low resolution faces extracted from video frames collected, and achieve a higher verification accuracy on the benchmark LFW dataset cf. hand-crafted features such as LBPs, and even surpasses the performance of state-of-the-art deep networks such as VGG-Face, when they are made to work with low resolution input images.
Watermark Synchronization: Perspectives and a New Paradigm

Synchronization is one of the most challenging elements of a watermarking system. In this paper, we survey and classify methods employed for watermark synchronization and highlight some inherent difficulties in synchronization that arise due to the nature of multimedia signals. We then propose a framework that addresses these hurdles in practical applications. Our framework utilizes feature-based embedding in concert with error correction codes capable of handling not only substitutions caused by various perturbations but also insertion and deletion events caused by erroneous feature estimates. We present a practical scheme for speech watermarking based on the framework. Experimental results show that the proposed methodology indeed enables synchronization in scenarios where a mismatch in estimated features between embedder and receiver would otherwise cause synchronization loss. We explore connections of the framework with recent theoretical analyses of watermark synchronization.
A SDN solution for system-on-chip world

System on chips (SoCs) are all around us in today's world. Therefore, in this paper we propose a flexible, technology-aware SoC design, named as Cloud-of-Chips (CoC), which is able to change its characteristics, such as routing logic, transmission paths, priorities, IC clustering, etc. We focus particularly on inside communication of CoC architecture. The basic idea of CoC is the creation of an architecture, which will be able to support all the requirements of a vast number of todays applications by adopting and changing its characteristics according to them. The valorization perspectives are of importance since the outcomes of this research will be applicable for embedded systems, real-time systems, communication systems, as well as for mainstream systems such as Internet-of-Things and Internet-of-Everything. As far as the inside communication of our CoC platform is concerned, we leverage on algorithms and strategies developed within the field of Software Defined Networking (SDN), which was introduced to deal with hardware redesign and ultimately provide cloud-like flexibility. At the end, we describe registration and authentication of every entity in our network.
A high speed power efficient dynamic comparator designed in 90nm CMOS technology

A fully dynamic latched comparator has been designed to meet the requirement of high speed and low power consumption. Such comparators are used in high speed data converters. In this work, dynamic comparators are designed in two different technologies and compared on the basis of delay, offset voltage and power consumption. These comparators work on the concept of charge sharing. Main focus is given towards reduction of both propagation delay and the power dissipation, which will be beneficial in improving performance of the comparator. 90nm CMOS technology is used to simulate the design with 1 V supply voltage. Hspice is used for designing and functional verification.