nils-herrmann/clean_scirepeval huggingface dataset:
Active learning for community detection in stochastic block models

The stochastic block model (SBM) is an important generative model for random graphs in network science and machine learning, useful for benchmarking community detection (or clustering) algorithms. The symmetric SBM generates a graph with 2n nodes which cluster into two equally sized communities. Nodes connect with probability p within a community and q across different communities. We consider the case of p = a ln(n)/n and q = b ln(n)/n. In this case, it was recently shown that recovering the community membership (or label) of every node with high probability (w.h.p.) using only the graph is possible if and only if the Chernoff-Hellinger (CH) divergence D(a; b) = (√a - √a)2 ≥ 1. In this work, we study if, and by how much, community detection below the clustering threshold (i.e. D(a; b) <; 1) is possible by querying the labels of a limited number of chosen nodes (i.e., active learning). Our main result is to show that, under certain conditions, sampling the labels of a vanishingly small fraction of nodes (a number sub-linear in n) is sufficient for exact community detection even when D(a; b) <; 1. Furthermore, we provide an efficient learning algorithm which recovers the community memberships of all nodes w.h.p. as long as the number of sampled points meets the sufficient condition. We also show that recovery is not possible if the number of observed labels is less than n1-D(a;b). The validity of our results is demonstrated through numerical experiments.
Rate-distortion based reconstruction optimization in distributed source coding for interactive multiview video streaming

Interactive multiview video streaming (IMVS) is an application where, as the streaming multiview video is played back in time, an observer iteratively requests one of many available views at the server. In response, the server sends the appropriate pre-encoded data to the observer, with data chosen for transmission depending on the specific transmitted data available in the observer's cache. The primary challenge in IMVS is to design a structure for the pre-encoded multiview data, so that during an IMVS streaming session, the transmission rate is appropriately traded off with the pre-encoded data storage size. Previously, we have developed novel distributed source coding (DSC) based frame configurations to optimize the said tradeoff, outperforming periodical insertions of I-frames in both transmission and storage costs. In this paper, we show that by exploiting the freedom to choose a target decoded frame at a view switching point for DSC, further performance gains can be achieved: by up to 0.7 dB in our experiments.
Critical sampling for wavelet filterbanks on arbitrary graphs

Current formulations of critically-sampled graph wavelet filterbanks work only for bipartite graphs where downsampling signals on either partition leads to a spectrum folding phenomenon. The lack of such a natural downsampling scheme for arbitrary graphs poses difficulties in designing filterbanks. In this paper, we propose a critical sampling scheme on an arbitrary graph that chooses a sampling set for each channel, given a set of analysis/synthesis filters, by seeking to minimize a bound on the overall reconstruction error associated with the filterbank. Our algorithm is efficient since it requires a few simple graph filtering operations in each iteration. Our initial experiments show that its output is consistent with the sampling scheme for bipartite graphs and results in superior performance over other methods.
Sparse representation of depth maps for efficient transform coding

Compression of depth maps is important for “image plus depth” representation of multiview images, which enables synthesis of novel intermediate views via depth-image-based rendering (DIBR) at decoder. Previous depth map coding schemes exploit unique depth characteristics to compactly and faithfully reproduce the original signal. In contrast, given that depth maps are not directly viewed but are only used for view synthesis, in this paper we manipulate depth values themselves, without causing severe synthesized view distortion, in order to maximize sparsity in the transform domain for compression gain. We formulate the sparsity maximization problem as an l0-norm optimization. Given l0-norm optimization is hard in general, we first find a sparse representation by iteratively solving a weighted l1 minimization via linear programming (LP). We then design a heuristic to push resulting LP solution away from constraint boundaries to avoid quantization errors. Using JPEG as an example transform codec, we show that our approach gained up to 2.5dB in rate-distortion performance for the interpolated view.
Multidimensional intelligent diagnosis system based on Support Vector Machine Classifier

Heeding the diagnostic requirements of electromechanical systems applied in automotive and aeronautical sectors, a multidimensional diagnostic system based on Support Vector Machine classifier is presented in this paper. In this context, different stationary and non-stationary speed and torque conditions are taken into account over an experimental actuator, in the same way, different single and combined failures scenarios are analyzed. In order to achieve a proper reliability in the diagnosis process, a multidimensional strategy is proposed: currents and vibrations from an electro-mechanical actuator are acquired. A great deal of features is calculated using statistical parameters from the acquired signals in time and frequency domain. Additionally, advanced time-frequency domain analysis techniques, such as Wavelet Packet Transform and Empirical Mode Decomposition, are used to achieve features which provide information in non-stationary conditions. The feature space dimensionality is analyzed by a feature reduction stage based on Partial Least Squares, which optimizes and reduces the feature set to be used for diagnosis proposes. The classification core is based on Support Vector Machine. Moreover, this work provides a performance comparison between the proposed classification algorithm and others such as Neural Network, k- Nearest Neighbor and Classification Trees. Experimental results are presented to demonstrate the feasibility and diagnostic capability of the proposed system.
Hardware-driven compressive sampling for fast target localization using single-chip UWB radar sensor

To design an energy-efficient UWB ranging system, we propose a compressive sampling (CS) technique tightly coupled to a recently proposed hardware. Our goal is to design a system that is robust to high noise and consumes less energy while providing reliable localization. In this work, we first introduce a representation of UWB signals as group sparse signals with the number of groups corresponding to the number of objects in the environment. Also, we design an efficient measurement system that is constructed using low-density parity-check (LDPC) matrix, in order to satisfy several constraints imposed by the hardware: non-negative integer entries in measurement (sensing) matrix, constant row-wise sum of non-zero entries in the matrix, and a unique structure characterized by Kronecker product. To enhance performance, we propose a window-based reweighted L1 minimization that outperforms other existing algorithms in our simulation. The result shows that our proposed method can achieve reliable target-localization, while using only 40% of the scanning (sampling) time required by the sequential scanning scheme, even in highly-noisy environments.
Novelty detection methodology based on multi-modal one-class support vector machine

The lack of information of complicated industrial systems represents one of the main limitation to implement condition monitoring and diagnosis systems. Novelty detection framework plays an essential role for monitoring systems in which the information about the different operation conditions or fault scenarios is unavailable or limited. In this context, this work presents a novelty detection approach applied to a main rotatory element of an industrial packaging machine, a camshaft. The developed novelty detection method begins with the assumption that only data corresponding to a healthy operation of the machine is available, and the objective is to detect anomalies in the behavior of the machine. To monitor the packing machine, first, the current signals acquired from the main motor are processed by means of a normalized time-frequency map. Next, a set of features are calculated from the frequency maps. Then a set of novelty models are trained. When abnormal data is detected, an alarm will be activated to be confirmed by the user. The proposed methodology includes the re-training of the novelty detection models to include such behaviors. The proposed methodology shows a good performance to identify abnormal behavior on the machine and successfully incorporate novel scenarios.
SPICE model of thermoelectric elements including thermal effects

An electrical model for a Peltier cell, based on the analogy between thermal and electrical variables, is proposed. The use of thermal models allows the global performance of thermocooling circuit and signal system to be checked by using electrical circuit analysis programs such as SPICE. The maximum error in the steady state between measured and simulated temperatures are less than 0.3/spl deg/C for hot temperature and less than 0.2/spl deg/C for cold temperature with 31.7/spl deg/C of temperature change.
Reduced dimension policy iteration for wireless network control via multiscale analysis

A novel framework for the analysis and optimization of wireless networks operations is proposed. The temporal evolution of the state of the network is modeled as the trajectory of the state of a Finite State Machine (FSM). The state space of the FSM and the statistics of state transition are represented as a directed graph. Graph reduction and transform techniques are proposed to reduce the dimension of the graph associated with the FSM and analyze the properties of functions defined on its state space. The proposed methodology is based on the intrinsic multi-dimensional/multi-scale structure of the state space of the FSM and enables the analysis and minimization of cost-to-go functions, i.e., functions measuring the expected long-term cost associated with a control strategy, on coarser versions of the original graph.
Active learning on weighted graphs using adaptive and non-adaptive approaches

This paper studies graph-based active learning, where the goal is to reconstruct a binary signal defined on the nodes of a weighted graph, by sampling it on a small subset of the nodes. A new sampling algorithm is proposed, which sequentially selects the graph nodes to be sampled, based on an aggressive search for the boundary of the signal over the graph. The algorithm generalizes a recent method for sampling nodes in unweighted graphs. The generalization improves the sampling performance using the information gained from the available graph weights. An analysis of the number of samples required by the proposed algorithm is provided, and the gain over the unweighted method is further demonstrated in simulations. Additionally, the proposed method is compared with an alternative state-of-the-art method, which is based on the graph's spectral properties. It is shown that the proposed method significantly outperforms the spectral sampling method, if the signal needs to be predicted with high accuracy. On the other hand, if a higher level of inaccuracy is tolerable, then the spectral method outperforms the proposed aggressive search method. Consequently, we propose a hybrid method, which is shown to combine the advantages of both approaches.
Wavelet-based compressed spectrum sensing for cognitive radio wireless networks

Spectrum sensing is an essential functionality of cognitive radio wireless networks (CRWNs) that enables detecting unused frequency sub-bands for dynamic spectrum access. This paper proposes a compressed spectrum sensing framework by (i) constructing a sparsity basis in wavelet domain that helps compressed sensing at sub-Nyquist rates and (ii) applying a wavelet-based singularity detector on the reconstructed signal to identify available frequency sub-bands with low complexity. In particular, for the compressed sensing, an optimized Haar wavelet basis is employed to sparsely represent piecewise constant (PWC) signals which closely approximates the frequency spectrum of a sensed signal. Our simulation results show that our proposed framework outperforms existing compressed spectrum sensing methods by providing higher accuracy at lower sampling rates.
Depth-assisted stereo video enhancement using graph-based approaches

In stereo video applications, the quality of the two views may vary based on different camera capturing conditions and setup, compression/transmission, and sensor noise. Although some studies show that the perceived video quality may not be significantly affected by the lower quality view, maintaining a similar video quality is still desired in order to prevent eye strain during extended viewing sessions. In this paper, we study a graph-based approach to enhance the lower quality views by referring to the high quality view in addition to an accompanying depth map. We construct a graphical signal model with joint bilateral edge weights and show that graph-based joint bilateral filtering can better suppress several types of noises, e.g., Gaussian, motion as well as quantization noise.
Context-based adaptive image coding

In this paper we present an adaptive image coding algorithm based on novel backward-adaptive quantization/classification techniques. We use a simple scalar quantizer to quantize the image subbands. Our algorithm uses several contexts to characterize the subband data and different arithmetic coder parameters are matched to each context. We show how the context selection can be driven by rate-distortion criteria and how the performance can be improved by replacing the scalar quantization strategy by a entropy-constrained approach. Our results are comparable or better than the recent state-of-the-art with our algorithm also having advantages in terms of simplicity.
Multiple description speech coding for robust communication over lossy packet networks

Robust speech communication over unreliable channels is one of the key research areas in the development of voice-over IP (VoIP) technology. The authors propose a multiple description coding (MDC) based speech packetization scheme to combat packet losses. The basic idea is to encode each input speech frame into multiple packets, each of which can be independently decoded. Explicit redundancy is added such that each packet can render an acceptable signal reconstruction of the original frame. Unlike previous approaches using explicit redundancy for loss recovery (D.E. Tsoukalas et al., 1997), we propose to improve the redundancy coding efficiency using context adaptive techniques. Simulation results on independent packet losses show that the proposed scheme gives better average reconstruction audio quality at low loss rates (/spl les/20%) compared to that of previous works.
A gradient-based rate control algorithm with applications to MPEG video

In this paper, we present a rate control algorithm for MPEG video. The goal is to minimize the distortion while keeping the change in distortion between consecutive frames small. We formulate this goal as a constrained optimization problem and find a solution using an iterative gradient search method. To reduce the computation cost, we propose a model based on spline curves to approximate the rate distortion functions which also takes into account the frame dependencies. Simulations on short video sequences show that, at the same bit rate and buffer constraint, our technique generates output sequences with smaller and more stable mean square error than other approaches, while maintaining strictly constant bit rate for every group of pictures, at the expense of higher computation cost.
On-Line System for Fault Detection in Induction Machines Based on Wavelet Convolution

Motor current signature analysis has been successfully used in induction machines for fault diagnosis. The method however does not always achieve good results when the load torque is not constant. For proper results temporal analysis on determined frequency harmonics is needed. This paper proposes an automatic system for fault analysis based on wavelet functions, which allows on-line fault detection on time domain achieving good experimental results on stationary and non stationary state.
Quality metric for filter arrangement in a multispectral filter array

Mosaicked color filter arrays and demosaicking methods for multispectral images have been proposed in recent years. Several studies have evaluated the multispectral filter array (MSFA) pattern, but a method to optimize the pattern has not been proposed. We focus on the spatial arrangement of MSFAs to improve the demosaicked image quality. To evaluate the filter arrangement, we propose a new metric that uses both the spatial and spectral correlation of the filters. The arrangement is optimized by using simulated annealing and the proposed metric. An advantage of the proposed metric and its optimization is that they do not need to use image data. Experimental results show that the new metric is proportional to the peak-to-signal noise ratio (PSNR) and that a higher PSNR can be obtained by minimizing the metric using simulated annealing.
Enhanced Industrial Machinery Condition Monitoring Methodology Based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on principal component analysis and linear discriminant analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a feed-forward neural network and one-class support vector machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.
Designing sparse graphs via structure tensor for block transform coding of images

The graph Fourier transform (GFT) - adaptive to the signal structures of local pixel blocks - has recently been shown to be a good alternative to fixed transforms, e.g., the Discrete Cosine Transform (DCT), for image coding. However, the majority of proposed GFTs assume an underlying 4-connected graph structure with vertical and horizontal edges only. In this paper, we propose a design methodology to select more general sparse graph structures and edge weights, on which GFTs are defined for block-based coding. Specifically, we first cluster blocks via the Lloyd-Max algorithm based on their principal gradients, which are eigenvectors of the computed structure tensors. For each cluster a graph template with edges orthogonal to the principal gradient is designed. Finally, optimal edge weights are computed assuming each template is a graph describing the inter-pixel correlation in a Gaussian Markov Random Field (GMRF). Experimental results show that GFTs derived from our graph templates lead to sparser signal representations and fewer encoding bits than DCT for a set of natural test images.
Demagnetization fault detection by means of Hilbert Huang transform of the stator current decomposition in PMSM

This paper presents a study of the permanent magnet synchronous motor (PMSM) running under demagnetization. The simulation has been carried out by means of two dimensional (2-D) finite element analysis (FEA), and simulations results were compared with experimental results. The demagnetization fault is analyzed by means of decomposition of stator currents obtained at different speeds for torque change.
Analytical model-based bit allocation for wavelet coding with applications to multiple description coding and region of interest coding

We address the problem of allocating bits to the different regions in an image coded with a progressive wavelet coder such as SPIHT (Set Partitioning in Hierarchical Trees) . This type of problem appears in applications such as Region of Interest (ROI) coding or Multiple Description Coding (MDC). The wavelet coefficients in both cases are divided by different factors before coding to enable different bit allocation to different regions, because the coefficients in each region are refined at different speeds. While this is a popular approach for ROI coding [2, 3], we propose using it for MDC as well. In this work, we introduce a priority scaling factor ( ) as a dividing factor. The main contribution of this work is to provide an analytical technique to determine what the should be, given criteria such as relative importance of the regions in ROI coding or degree of redundancy in an MDC. Our approach is based on an approximation to Mallat’s model . We show how our selection of is basically the same as that obtained by optimization of empirical data, with significantly less complexity.
A Lagrangian optimization approach to rate control for delay-constrained video transmission over burst-error channels

We propose a rate control algorithm based on Lagrangian optimization for video transmission over burst-error channels. In our rate control approach, the delay and channel capacity constraints in the video transmission are translated into rate constraints at the encoder. Given that a feedback channel is available, the rate control mechanism can dynamically adjust the video encoding rate to meet the changing rate constraints as the channel conditions vary. Lagrangian optimization is used to find the optimal bit-allocation for the input video frames under the rate constraints, with the objective of minimizing the overall distortion at the decoder. We show how the performance of the transmission system, as measured in terms of the received video quality or the data loss rate, can be improved when information about the channel state is available and the encoder has an a priori probabilistic model of the channel behavior.
Level estimation for sparse reconstruction in discrete tomography

In discrete tomography (DT), the goal is to reconstruct from multiple linear projections an unknown image, which is known to have few distinct pixel level intensities. Such images arise in tomography problems where very high contrast is expected, e.g., in angiography medical imaging. A common assumption for DT is that the set of possible intensity levels is known in advance. However, determining the intensity levels is a difficult problem, coupled with measurement calibration and the used reconstruction algorithm. We introduce an unsupervised DT algorithm that jointly reconstructs the image and estimates the unknown intensity levels. Our algorithm alternates between (i) an l1 sparse recovery step with a reweighed cost function that pushes the reconstructed values close to the estimated intensities, and (ii) an estimation step for the most likely intensity levels. We experimentally demonstrate that the proposed algorithm successfully estimates the unknown levels and leads to high quality reconstruction of angiographic images.