Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Deep Image Inpainting

Recently, supervised learning has witnessed efficient employment in computer vision problems. But, its utility in various domain is limited by its reliance on massive amount of labeled data. This conflicts with human visual learning which requires just a few views of the object to recognize it. Relatively, unsupervised learning has gathered less recognition in computer vision. We propose a Generative Adversarial Netwok(GAN) based model architecture, with a decoupled loss, trained in an unsupervised mode to reconstruct the corrupted regions of an image. We exercise a joint loss function comprising a reconstruction loss, an adversarial loss and a parsing loss to optimize our model. Our model is evaluated on Google Street View dataset. Our qualitative, as well as quantitative results, verify the state-of-the-art performance of our work in comparison to previous approaches.
Skeleton-Based Action Recognition Based on Deep Learning and Grassmannian Pyramids

Ahstract- The accuracy of modern depth sensors, the robustness of skeletal data to illumination variations and the superb performance of deep learning techniques on several classification tasks have sparkled a renewed intereste towards skeleton-based action recognition. In this paper, we propose a four-stream deep neural network based on two types of spatial skeletal features and their corresponding temporal representations extracted by the novel Grassmannian Pyramid Descriptor (GPD). The performance of the proposed action recognition methodology is further enhanced by the use of a meta-learner that takes advantage of the meta knowledge extracted from the processing of the different features. Experiments on several well-known action recognition datasets reveal that our proposed methodology outperforms a number of state-of-the-art skeleton-based action recognition methods.
Radio-over-Fiber-supported Millimeter-wave Multiuser Transmission with Low-Complexity Antenna Units

A system for serving a large number of users at millimeter-wave (mmW) frequencies using a single Radio Frequency (RF) chain is presented. A single Remote Antenna Unit (RAU) supported by Radio-over-Fiber transport is used to transmit multiple 60GHz band signals to various users located at different spatial locations using the beamsteering characteristics of a Leaky Wave Antenna (LWA). Error Vector Magnitude analysis has been performed for each user signal up to a maximum of seven users per RF chain with wireless transmission over 2m. A performance comparison for different user-signal frequency spacings has been provided to understand the limitations of the system and results show that the proposed system design with the LWA performs better than systems using waveguide and horn antenna transmitters. A realization to double the number of served users is also presented which shows that up to 10 users can be served using half region of the LWA, with each user transmitting 1Gb/s data rate, delivering an aggregate data rate of 10Gb/s.
A V-Band SiGe Image-Reject Receiver Front-End for Atmospheric Remote Sensing

This paper presents a V-band receiver front-end for spaceborne atmospheric remote sensing. The receiver is implemented in a $\mathbf{0.13}\mu\mathbf{m}$ SiGe BiCMOS technology with $f_{T}/f_{MAX}$ of 250/330 GHz and consists of a Dicke switch, an LNA, an image-reject mixer, a frequency multiplier, and an IF amplifier. Across 56–69 GHz, the receiver achieves a mean conversion gain of 65 dB, a minimum noise figure of 4.3/6.1 dB without/with the Dicke switch, and a mean image rejection ratio of 31dB. The high-gain frequency quadrupler allows operation with an LO power as low as −18 dBm. This chip consumes a total DC power of 180 mW and occupies an active area of 3.1 mm2. This is the first reported monolithic receiver front-end for atmospheric measurements across the 60 GHz oxygen spectrum and it achieves the lowest noise figure among similar Si-based Dicke radiometers.
Null Space Strong Structural Controllability via Skew Zero Forcing Sets

In this paper, we examine strong structural controllability of a particular subspace of linear time-invariant networks, namely, the null space of the parameterized family of system matrices. In this direction, we establish a one-to-one correspondence between the set of input nodes for null space controllability and the notion of skew zero forcing sets. Using this class of zero forcing sets, we provide conditions to guarantee that the null space of the parameterized set of state matrices sharing a common network topology is trivial. Moreover, the uncontrollability of the zero mode of directed and undirected networks from a single node is discussed. In addition, methods for growing a network while preserving strong structural controllability of its null space from a set of control nodes is presented. Finally, we provide an application of the developed results for the bipartite consensus dynamics.
De novo male gonad transcriptome draft for the marine mussel Perumytilus purpuratus with a focus on its reproductive-related proteins

Perumytilus purpuratus is a marine mussel considered a bioengineer species with a broad distribution in the Pacific and Atlantic coast of South America. Studies have shown two geographically and genetically differentiated subpopulations at molecular level and in sperm morphological traits. To open avenues for molecular research on P. purpuratus, a global de novo transcriptome from gonadal tissue of mature males was sequenced using the Illumina platform. From a total of 126.38 million reads, 37,765 transcripts were successfully annotated. BUSCO analysis determined a level of 89% completeness for the assembled transcriptome. The functional gene ontology (GO) annotation indicated that, in terms of abundance, the transcripts related with molecular function were the most represented, followed by those related with biological process and cellular components. Additionally, a subset of GO annotations generated using the “sperm” term resulted in a total of 1,294 sequences where the biological process category was the more represented, with transcripts strongly associated to sperm-processes required for fertilization, and with processes where the sperm-egg interaction could be implicated. Our work will contribute to the evolutionary understanding of the molecular mechanisms related to tissue-specific functions. This work reports the first male gonad transcriptome for the mussel P. purpuratus, generating a useful transcriptomic resource for this species and other closely related mytilids.
A Novel Algorithm for Spacebome SAR DBF Based on Sparse Spatial Spectrum Estimation

A novel method of digital beam forming (DBF) based on sparse spatial spectrum estimation for spaceborne synthetic aperture radar (SAR) is proposed in this paper. This method converts the estimation of target scene elevation into the estimation of sparse spatial spectrum. , which reduces the impact of the number of signal samples. Finally. , the simulated results confirm the validity of the method.
Bangla Handwritten Digit Recognition Using Deep CNN for Large and Unbiased Dataset

Bangla handwritten digit recognition is a convenient starting point for building an OCR in the Bengali language. Lack of large and unbiased dataset, Bangla digit recognition was not standardized previously. But in this paper, a large and unbiased dataset known as NumtaDB is used for Bangla digit recognition. The challenges of the NumtaDB dataset are highly unprocessed and augmented images. So different kinds of preprocessing techniques are used for processing images and deep convolutional neural network (CNN) is used as the classification model in this paper. The deep convolutional neural network model has shown an excellent performance, securing the 13th position with 92.72% testing accuracy in the Bengali handwritten digit recognition challenge 2018 among 57 participating teams. A study of the network performance on the MNIST and EMNIST datasets were performed in order to bolster the analysis.
Shot Level Egocentric Video Co-summarization

Video co-summarization has emerged as an important problem in the areas of computer vision and multimedia communities. In this paper, we present a novel approach of co-summarizing egocentric videos at shot level. Our solution pipeline consists of three major components. We develop a new way of characterizing egocentric video frames by computing the differences in contrast, entropy and optic flow values between a central region and the surrounding region in a frame. This is termed as the center-surround model. Visual similarity between a test video shot and a database video shot is next derived using a game-theoretic framework. Each video shot is modelled as a player and the expected pay-off difference between any two such players at mixed Nash equilibrium is deemed as the similarity between them. A weighted bipartite graph is constructed next between the shots in a test and in a database video. Game-theoretic similarity values are deemed as the weights. Maximum Cardinality Minimum Weight matching in the bipartite graph yields non-greedy shot correspondences. Best matched shots from the test video are used to form the summary. Experimental comparisons on standard datasets clearly indicate the advantage of our solution.
Reduced-Reference Image Quality Assessment Based on Free-Energy Principle with Multi-Channel Decomposition

The free-energy principle studied in brain theory and neuroscience accounts for the mechanism of perception and understanding in human brain, which is highly adapted for measuring the visual quality of perceptions. On the other hand, psychologists and neurologists report that different frequency and orientation components of one stimulus arouse different neurons in striate cortex. In this paper, a novel reduce-reference (RR) image quality assessment (IQA) metric based on free-energy principle in multi-channel is proposed, which is called MCFEM (Multi-Channel Free-Energy principle Metric). We first decompose the input reference image and distorted image via a two-level discrete Haar wavelet transform (DHWT). Next, the free-energy features of each subband images are computed based on sparse representation. Finally, an overall quality index is received through the support vector regressor (SVR). Extensive experimental comparisons on four (LIVE, CSIQ, TID2008 and TID2013) benchmark image databases demonstrate that the proposed method is highly competitive with the representative RR and no-reference models as well as full-reference ones.
Feature Selection, Online Feature Selection Techniques for Big Data Classification: - A Review

In the recent times, several disciplines have to tackle with huge datasets, which are involved with a huge number of additional features. Feature Selection (FS) techniques target at reducing the noisy, redundant, or unnecessary features, which might degrade the performance of classification. Although there is several numbers of FS techniques, still it remains an active research field among the data mining, machine learning and pattern recognition groups. Several FS techniques are imposed with critical issues with regards to efficiency and usefulness, due to rise in data dimensionality, which happens nowadays. Nonetheless, conventional techniques are deficit of sufficient scalability to deal with datasets consisting of millions of instances and obtain results with success in a less amount of time. Therefore, in this case, an Online Feature Selection (OFS) algorithm can yield a better solution for solving this issue. This work reviews few of the available and well-known FS, OFS techniques by pointing out the pros and cons of those techniques. This technical work studies the details of traditional FS and OFS techniques depending on evolutionary computation that is helpful in getting the subsets of features from huge datasets. As a result, this review also provides a summary, and analysis of machine learning algorithms for huge datasets. In addition, the new machine learning strategies and methodologies are explained with their capacity of dealing with the different challenges with the ultimate goal of assisting the practitioners in selecting the suitable solutions for their use cases. This review work renders a view on the big data domain, finds the research gaps and possibilities, and offers a solid foundation, assistance for more research in the machine learning field that uses big dataset.
Differentiation of brain waves patterns in different states by multifractal analysis

We apply two methods (linear spectral analysis and nonlinear multifractal analysis) for analyzing the EEG records for experienced and inexperienced subjects, in meditation and background states. Through comparison of the obtained results, we show that the dominance of different rhythms (Lower Alpha vs. Upper Alpha) in different states for different types of subjects has a nonlinear nature. This is confirmed by the coincidence of spectral analysis and multi-fractal spectra analysis results, at least in the field where spectral analysis works. This opens new perspectives in applications of nonlinear methods for EEG time series study.
Migration for VNF Instances Forming Service Chain

When migrating virtual network function (VNF) instances forming a service chain (SC), huge amount of memory pages dirtied due to packet processing extend the interval between the moments when each VNF instance is suspended due to live migration. The generated dirty pages extend termination of the flows through the SC until the instance moving to the target server completes preparation for packet processing. Moreover, after recovery of the flows, quality of the flows deteriorates since the flows are detoured via the instances split into the distant source-target servers. We propose a VNF instance-migration method for alleviating flow termination and flow-quality degradation. Our method migrates the ingress VNF instance in the source server to exploit the flow termination for facilitating completion of the migration before recovery of the flows. Our method avoids endless migration caused by termination and recovery of the flows as well. Numerical results indicate that our method mitigates the impact of flow termination and flow-quality degradation.
A Recursive Expectation-Maximization Algorithm for Online Multi-Microphone Noise Reduction

Speech signals, captured by a microphone array mounted to a smart loudspeaker device, can be contaminated by ambient noise. In this paper, we present an online multichannel algorithm, based on the recursive EM (REM) procedure, to suppress ambient noise and enhance the speech signal. In the E-step of the proposed algorithm, a multichannel Wiener filter (MCWF) is applied to enhance the speech signal. The MCWF parameters, that is, the power spectral density (PSD) of the anechoic speech, the steering vector, and the PSD matrix of the noise, are estimated in the M-step. The proposed algorithm is specifically suitable for online applications since it uses only past and current observations and requires no iterations. To evaluate the proposed algorithm we used two sets of measurements. In the first set, static scenarios were generated by convolving speech utterances with real room impulse responses (RIRs) recorded in our acoustic lab with reverberation time set to 0.16 s and several signal to directional noise ratio (SDNR) levels. The second set was used to evaluate dynamic scenarios by using real recordings acquired by CEVA “smart and connected” development platform. Two practical use cases were evaluated: 1) estimating the steering vector with a known noise PSD matrix and 2) estimating the noise PSD matrix with a known steering vector. In both use cases, the proposed algorithm outperforms baseline multichannel denoising algorithms.
Skin Lesion Segmentation via Dense Connected Deconvolutional Network

Dermoscopy imaging analysis is a routine procedure for diagnosis and treatment of skin lesions. Segmentation is the very first step to demarcate skin lesions for further quantitative analysis. However, it is a challenging task due to various changes from different viewpoints and scales of skin lesions. To handle these challenges, we devise a new dense deconvolutional network (DDN) for skin lesion segmentation based on encoding module and decoding module. Our devised network consists of convolution unit, dense deconvolutionallayer (DDL) and chained residual pooling block. DDL is adopted to restore the high resolution of the original input by upsampling, while the chained residual pooling is utilized to fuse multilevel features. Also, the hierarchical supervision is added to capture low level detailed boundary information. The DDN is trained in an end-to-end manner and free of prior knowledge and complicated post-processing procedures. With fusing the local and global contextual information, the high-resolution prediction output is obtained. The validation on the public ISBI 2016 and 2017 skin lesion challenge dataset demonstrates the effectiveness of our proposed method.
Inter-Rater Agreement for Social Computing Studies

Different agreement scores are widely used in social computing studies to evaluate the reliability of crowdsourced ratings. In this research, we argue that the concept of agreement is problematic for many rating tasks in computational social science because they are characterized by subjectivity. We demonstrate this claim by analyzing four social computing datasets that are rated by crowd workers, showing that the agreement ratings are low despite deploying proper instructions and platform settings. Findings indicate that the more subjective the rating task, the lower the agreement, suggesting that tasks differ by their inherent subjectivity and that measuring the agreement of social computing tasks might not be the optimal way to ensure data quality. When creating sbjective tasks, the use of agreement metrics potentially gives a false picture of the consistency of crowd workers, as they over-simplify the reality of obtaining quality labels. We also provide empirical evidence on the stability of crowd ratings with a different number of raters, items, and categories, finding that the reliability s cores a re most sensitive to the number categories, somewhat less sensitive to the number of raters, and the least sensitive to the number of items. Our findings have implications for computational social scientists using crowdsourcing for data collection.
Multi-Level Analysis of Bit-Plane Based GLAC Feature and other Existing Texture Features for a Robust Hand Detection System

Hand detection is the vital step towards developing a gesture recognition system. Robust hand detection is a challenging task and needs a deeper investigation of hand-oriented features under practical conditions. Existing texture features such as Histogram of oriented gradients (HOG), and Gabor feature are efficient but requires high extraction time due to their dense nature. If the feature is extracted from an edge-filtered imaged, only the vital edge features will be processed while reducing the computation and time complexity. Therefore present work proposes a bit-plane based feature extraction approach. Also, a new texture feature is proposed, Gradient Local Auto-Correlations (GLAC) that extracts the 2nd order statistical parameters such as curvature statistics unlike HOG, Gabor, and histogram feature. GLAC is also modified to GLACgrid feature to extract local texture feature by using spatial binning grids of $2\times 3$ with 5 orientation bins. Experimental observations showed that performance of GLACgrid feature is approx. 3.5%, 10.6%, and 19% higher than HOG, Gabor and histogram feature, respectively. Evaluation models are developed using Naïve Bayes classifier, Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, support vector machine (SVM). Response time of bit-plane GLAC features are considerably lower than HOG and Gabor feature, which makes it an efficient candidate for realtime hand detection systems.
“What are You Listening to?” Explaining Predictions of Deep Machine Listening Systems

Researchers have proposed methods to explain neural network predictions by building explanations either in terms of input components (e.g., pixels in an image) or in terms of input regions (e.g., the area containing the face of a Labrador). Such methods aim to determine the trustworthiness of a model, as well as to guide its improvement. In this paper, we argue that explanations in terms of input regions are useful for analysing machine listening systems. We introduce a novel method based on feature inversion to identify a region in an input time-frequency representation that is most influential to a prediction. We demonstrate it for a state-of-the-art singing voice detection model. We evaluate the quality of the generated explanations on two public benchmark datasets. The results demonstrate that the presented method often identifies a region of an input instance that has a decisive effect on the classification.
Establishing Device to Device Communication Under Cellular architecture

The technology component for LTE-A is Device-toDevice (D2D) communication. The spectral efficiency of the cellular network can be increased by allowing D2D as an inhibit to the cellular network. The D2D devices communicate their data over a direct cellular link instead of passing it through the Base Station. Thus the load for the Base Station is reduced by establishing direct communication. The power Control Algorithm can be used to achieve performance maximization. The IRAPC-Iterative Resource Allocation and Power Control Algorithm and VoIP technology are used to achieve sum rate. We evaluate the performance of the IRAPC-Algorithm-Iterative Resource Allocation and Power Control via OMNET++ (SIMULATION TOOL)
A Grid Connected Five Level Cascaded Multilevel Inverter for PV Systems

In this paper a grid connected five level CMLI (Cascaded Multilevel Inverter) topology for PV systems is presented. Maximum power from solar PV (photo voltaic) array is extracted by providing controlled duty cycle to DC-DC boost converter. Control of Duty cycle is done using P&O (Perturb and Observe) type MPPT (Maximum Power Point Tracking) algorithm. After that control switching pulses are applied to five-level CMLI for conversion of DC voltage into AC. The output of the inverter is then supplied to the grid. The leakage current is reduced using low frequency transitions. Simulation results of voltage and current waveform of grid, PV array, stabilized output of MPPT and of CMLI is also shown.
The Application of Modified RFM-Analysis to Increase the Loyalty of Consumers of Industrial Rubber Articles

In the paper the trends in the Novosibirsk market of industrial rubber articles were analyzed, the key problems of the industry were revealed. The groups of customers with homogeneous behavior were identified by a modified RFM-analysis applied to client database of the company engaged in the producing and sale of industrial rubber articles. The structure of each group was studied. Loyalty programs to meet in most effective ways the needs of the customers from the selected groups have been developed.
High-Efficiency Crystalline Silicon Solar Cell Architectures

This article presents recent approaches for achieving high conversion efficiency of crystalline silicon solar cells at Delft University of Technology. The new approaches are based on heterojuction interfaces between the crystalline silicon (c-Si) absorber and carrier-selective passivating contact layers. We discuss silicon heterojunction solar cells with carrier-selective contact based on thin layers of hydrogenated amorphous silicon (a-Si: H) and on hydrogenated polycrystalline silicon (poly-Si) combined with a ultrathin silicon oxide layer. Both, the application of carrier-selective contacts in c-Si front-back contacted (FBC) and interdigitated back-contacted (IBC) solar cells with different thermal budgets are shown. The best performance was demonstrated with IBC c-Si solar cells with poly-Si carrier-selective passivating contacts with conversion efficiency $\eta=23.0\%$ and short-circuit current density JSC= 42.2 mAlcm2.