Miracle-dz/UniFAR-FTUs huggingface dataset:
Anchored Neighborhood Index for Face Sketch Synthesis

Exemplar-based face sketch synthesis has long been impeded by the difficulty of accurate neighbor selection. Given a test patch extracted from the test photograph, the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-NN) matching algorithm is generally performed by existing methods to find <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-nearest photograph patches in the training data set, which contains some pairs of face sketches and photographs. Then, the training sketch patches corresponding to the selected nearest photograph patches are taken as the candidate to synthesize the target sketch patch. In the aforementioned neighbor selection process, training sketch patches is not taken into consideration in the process of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-nearest neighbor selection. In this paper, we proposed a simple yet effective neighbor selection algorithm, namely, anchored neighborhood index (ANI), to boost the synthesis performance by taking training sketch patches into the consideration. In addition, the proposed ANI can be conducted offline and, thus, it does not increase the computational complexity. Extensive experiments on public available database demonstrate that the proposed algorithm achieves superior performance compared with the state-of-the-art methods in terms of both objective image quality scores and face recognition accuracy.
Similarity measure for Public Persons

For the webportal "Who is in the News! " with statistics about the appearence of persons in written news we developed an extension, which measures the relationship of public persons depending on a time parameter, as the relationship may vary over time. On a training corpus of English and German news articles we built a measure by extracting the persons occurrence in the text via pretrained named entity extraction and then construct time series of counts for each person. Pearson correlation over a sliding window is then used to measure the relation of two persons.
A network topology approach to bot classification

Automated social agents, or bots are increasingly becoming a problem on social media platforms. There is a growing body of literature and multiple tools to aid in the detection of such agents on online social networking platforms. We propose that the social network topology of a user would be sufficient to determine whether the user is a automated agent or a human. To test this, we use a publicly available dataset containing users on Twitter labelled as either automated social agent or human. Using an unsupervised machine learning approach, we obtain a detection accuracy rate of 70%.
C-Type Natriuretic Peptide (CNP) Inhibition of Interferon-γ-Mediated Gene Expression in Human Endothelial Cells In Vitro

Cardiovascular diseases, including atherosclerosis, now account for more deaths in the Western world than from any other cause. Atherosclerosis has a chronic inflammatory component involving Th1 pro-inflammatory cytokines such as IFN-γ, which is known to induce endothelial cell inflammatory responses. On the other hand CNP, which acts via its receptors to elevate intracellular cGMP, is produced by endothelium and endocardium and is upregulated in atherosclerosis. It is believed to be protective, however its role in vascular inflammation is not well understood. The aim of this study was to investigate the effects of CNP on human endothelial cell inflammatory responses following IFN-γ stimulation. Human umbilical vein endothelial cells were treated with either IFN-γ (10 ng/mL) or CNP (100 nm), or both in combination, followed by analysis by flow cytometry for expression of MHC class I and ICAM-1. IFN-γ significantly increased expression of both molecules, which was significantly inhibited by CNP or the cGMP donor 8-Bromoguanosine 3’,5’-cyclic monophosphate (1 µm). CNP also reduced IFN-γ mediated kynurenine generation by the IFN-γ regulated enzyme indoleamine-2,3-deoxygenase (IDO). We conclude that CNP downmodulates IFN-γ induced pro-inflammatory gene expression in human endothelial cells via a cGMP-mediated pathway. Thus, CNP may have a protective role in vascular inflammation and novel therapeutic strategies for CVD based on upregulation of endothelial CNP expression could reduce chronic EC inflammation.
Role of Knowledge Towards the Outcome of Information Systems Development Projects

Information Systems Development Projects (ISDPs) have a reputation higher failure rate for both practice and research which attracted lot of attention from different researchers from different disciplines. They are conducted by human beings who formulate ISDP team and makes them to be human oriented in nature. ISDP are knowledge intensive in nature and the knowledge that is required by members of ISDP team includes both tacit and explicit knowledge. The human beings which are part of ISDP team contribute and apply that knowledge within the ISDP. Knowledge have impact towards different issues such as ISDP team performance and ISDP team innovation, and those issues have influence on the outcome of ISDPs. It is essential for all the stakeholders of ISDP to understand the role of knowledge towards the outcome of ISDPs. In this study, the researchers review existing literature that focus on the role of knowledge towards the outcome of ISDPs. The results indicated that knowledge improves the performance of the entire ISDP team, innovation of ISDP team and contributes significantly towards delivery of a successful ISDP.
Association Between Logical Reasoning Ability and Quality of Relevance Judgments in Crowdsourcing

Human assessors are in charge of creating relevance judgments set in a typical test collection. Nevertheless, this approach is not that efficient as it is expensive and time consuming while scales deficiently. Crowdsourcing as a recent technique for data acquisition is a low-cost and fast method for building relevance judgments. One of the most important issues for using crowdsourcing instead of human expert assessors is the quality of crowdsourcing in building relevance judgments. In order to assess this issue, factors that may have significant effects on the quality of crowdsourcing relevance judgments should be identified. The main objective of this study is to find out whether cognitive characteristics of crowdsourced workers significantly associated with quality of crowdsourced judgments, and to evaluate the effect(s) that each of those characteristics may have on judgment quality, as compared with the gold standard dataset (i.e. human assessment). Thus, the judgments of the crowdsourced workers is compared to that of a human judgment, as the overlap between relevance assessments, and by comparing the system effectiveness evaluation provided by human judgment and from worker assessors. In this study, we assess the effects of the cognitive ability namely logical reasoning ability on quality of relevance judgment. The experiment shows that logical reasoning ability of individuals is remarkably correlated with quality of relevance judgments.
A study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance

Face frontalization is the process of synthesizing frontal facing views of faces given its angled poses. We implement a generative adversarial network (GAN) with spherical linear interpolation (Slerp) for frontalization of unconstrained facial images. Our special focus is intended towards the generation of approximate frontal faces of the side posed images captured from surveillance cameras. Specifically, the present work is a comprehensive study on the implementation of an auto-encoder based Boundary Equilibrium GAN (BEGAN) to generate frontal faces using an interpolation of a side view face and its mirrored view. To increase the quality of the interpolated output we implement a BEGAN with Slerp. This approach could produce a promising output along with a faster and more stable training for the model. The BEGAN model additionally has a balanced generator-discriminator combination, which prevents mode collapse along with a global convergence measure. It is expected that such an approximate face generation model would be able to replace face composites used in surveillance and crime detection.
An Optimal Two-Stage Launch Plan of a Satellite Constellation to Maintain Mission Reliability Requirement

This paper presents an optimization strategy for a two-stage launch plan for a satellite constellation with an arbitrary number of satellites. The objective is to minimize the overall cost of the constellation accrued from manufacturing and launching satellites in different stages while maintaining compliance to the constellation reliability design requirement throughout the entire mission life. Two cost models are considered: (1) the cost with a constant satellite price for both stages, and (2) the cost with increasing satellite price from the first stage to the second. The methodology for solving for the optimal solutions is discussed. Examples are presented with the pros and cons of the proposed optimization strategy. The results indicate clear advantages of using the optimization strategy for two-stage launch planning. In conclusion, a path-forward is outlined.
Frame-by-Frame Closed-Form Update for Mask-Based Adaptive MVDR Beamforming

Beamforming approaches using time-frequency masks have recently been investigated and have shown promising results for noise robust automatic speech recognition (ASR) in many tasks. The time-frequency masks are estimated to compute the spatial statistics of target speech and noise signals, and then the statistics are used to derive a beamformer. Although its effectiveness has been clearly shown in batch and blockwise processing, it has not been well extended to frame-by-frame processing, which is a very important procedure for many actual applications. In this paper, we derive a frame-by-frame update rule for a mask-based minimum variance distortion-less response (MVDR) beamformer, which enables us to obtain enhanced signals without a long delay by combining it with uni-directional recurrent neural network-based mask estimation. Based on the Woodbury matrix identity, our algorithm achieves a closed-form solution of the mask-based MVDR beamformer at every time frame without any matrix inversion. Experimental results show that our frame-by-frame beamformer outperforms baseline block-wise beamforming on the CHiME-3 simulation dataset even with a shorter time delay.
Design of Frictional 2D-Anisotropy Surface for Wriggle Locomotion of Printable Soft-Bodied Robots

Soft-bodied and continuum robots have shown great adaptability to the environment thanks to its flexibility of the body. They have great potential in environment exploring or rescuing mission. One of those robots is snake-like soft-bodied robots. A snake robot is often made by attaching passive wheels along a long body to achieve frictional anisotropy. This anisotropic structure helps to propel the body with serpentine locomotion and prevents it from sliding laterally. However, with a snake-like soft-bodied robot, attaching wheels is not only clumsy but also adding weight to the robot. In this paper, being inspired by the scales on the skin of a snake, we propose a designing scheme to achieve an all-printed wriggle soft-bodied robot by patterning high and low friction material to the ventral side of the robot. Compared to a totally flat ventral, we are able to speed-up the serpentine locomotion 2.8 times. Besides, by changing the configuration of high/low friction material, our wriggle soft-bodied robot can easily move forward or backward just by switching the controlling signal. The fabrication time is just less than 1 hour and the robot can achieve the speed of 26 mm/s.
Evaluation and Optimization of an Image Acquisition System for Dual-Energy Cargo Inspections

An optimization study on an image acquisition system was conducted to improve the performance of a dual-energy cargo inspection system that enables material decomposition. The image acquisition system in this paper used a line scan method that acquired images while moving cargo vehicles by the moving system. Also, dual-energy X-ray images were obtained by controlling the X-ray source and detector simultaneously with an external signal generator. The image acquisition system was optimized through gain and offset corrections, signal sensitivity optimization, and beam filtration optimization. In order to evaluate the performance of the cargo inspection system, a penetration test was conducted based on the American National Standards Institute, ANSI 42.46, and the contrast-to-noise ratio (CNR) was measured to analyze the acquired images more quantitatively. As a result of the signal sensitivity optimization, the CNR was improved by 28.5%. We also measured the attenuation coefficient of dual-energy X-rays based on the thickness of the filter for beam filtration optimization. As the thickness of the lead filter was increased, the difference in the attenuation coefficient of dual-energy X-rays increased, and the attenuation coefficient difference was saturated in the 4-mm lead filter. Therefore, the lead filter with a thickness of 4 mm was selected as a filter that can sufficiently broaden the average energy difference of the dual-energy X-rays without impairing the image quality, which ultimately improves the material decomposition ability.
University of Dundee Dynamic multi-scale CNN forest learning for automatic cervical cancer segmentation

Deep-learning based labeling methods have gained unprecedented popularity in different computer vision and medical image segmentation tasks. However, to the best of our knowledge, these have not been used for cervical tumor segmentation. More importantly, while the majority of innovative deeplearning works using convolutional neural networks (CNNs) focus on developing more sophisticated and robust architectures (e.g., ResNet, U-Net, GANs), there is very limited work on how to aggregate different CNN architectures to improve their relational learning at multiple levels of CNN-to-CNN interactions. To address this gap, we introduce a Dynamic Multi-Scale CNN Forest (CK+1DMF), which aims to address three major issues in medical image labeling and ensemble CNN learning: (1) heterogeneous distribution of MRI training patches, (2) a bidirectional flow of information between two consecutive CNNs as opposed to cascading CNNs —where information passes in a directional way from current to the next CNN in the cascade, and (3) multiscale anatomical variability across patients. To solve the first issue, we group training samples into K clusters, then design a forest with (K + 1) trees: a principal tree of CNNs trained using all data samples and subordinate trees, each trained using a cluster of samples. As for the second and third issues, we design each dynamic multiscale tree (DMT) in the forest such that each node in the tree nests a CNN architecture. Two successive CNN nodes in the tree pass bidirectional contextual maps to progressively improve the learning of their relational non-linear mapping. Besides, as we traverse a path from the root node to a leaf node in the tree, the architecture of each CNN node becomes shallower to take in smaller training patches. Our CK+1DMF significantly (p<0.05) outperformed several conventional and ensemble CNN architectures, including conventional CNN (improvement by 10.3%) and CNN-based DMT (improvement by 5%).
Exploring Hashing and Cryptonet Based Approaches for Privacy-Preserving Speech Emotion Recognition

The outsourcing of machine learning classification and data mining tasks can be an effective solution for those parties that need machine learning services, but lack the appropriate resources, knowledge and/or tools to carry them out, in their own premises. This solution, however, raises major privacy concerns, in particular, when irrevocable biometric data such as speech is involved. In this work, we focus on the development of privacy-preserving schemes in a speech emotion recognition task, as a proof of concept that could be extended to other speech analytics tasks. Our aim is to prove that the implementation of privacy-preserving speech mining schemes in challenging tasks involving paralinguistic features is not only feasible, but also accurate. Using distance-preserving hashing techniques in a first approach, and homomorphic encryption in a second approach, we successfully protect sensitive data with little degradation costs regarding the accuracy of the predictive models.
Cross-Class Sample Synthesis for Zero-shot Learning

Zero-shot learning (ZSL) aims to recognize unseen classes which have no available training samples, through establishing an association with seen classes. Existing approaches mostly learn a comparability function to predict the class of an image. Different from previous approaches, we put forward a novel method, Cross-Class Sample Synthesis (CCSS), to directly synthesize samples of unseen classes from speciﬁc seen classes in the visual feature space. We adopt class-graph to measure inter-class similarity and propose class entropy to select classes as the synthesis source of target classes. An end-to-end network is constructed to realize sample synthesis from source classes to target classes. Specially, rule of attribute guiding cross-class transfer is built into the network, to which various samples of different source classes can be used to synthesize samples of each target class according. The synthesized samples are used as training data of un-seen classes and it turns ZSL into a supervised learning problem. Experiments on ﬁve benchmark datasets efﬁciently demonstrate the advantage of our proposed method.
Visually-Driven Semantic Augmentation for Zero-Shot Learning

In this paper, we tackle the zero-shot learning (ZSL) classiﬁcation problem and analyse one of its key ingredients, the semantic embedding. Despite their fundamental role, semantic embeddings are not learnt from the visual data to be classiﬁed, but, instead, they either come from manual annotation (attributes) or from a linguistic text corpus (distributed word embeddings, DWEs). Hence, there is no guarantee that visual and semantic information could ﬁt well, and as to bridge this gap, we propose to augment the semantic information of attributes/DWEs with semantic representations directly extracted from visual data by means of soft labels. When combined in a novel ZSL paradigm based on latent attributes, our approach achieves favourable performances on three public benchmark datasets.
Variational Deep Learning for Low-Dose Computed Tomography

In this work, we propose a learning-based variational network (VN) approach for reconstruction of low-dose 3D computed tomography data. We focus on two methods to decrease the radiation dose: (1) x-ray tube current reduction, which reduces the signal-to-noise ratio, and (2) x-ray beam interruption, which undersamples data and results in images with aliasing artifacts. While the learned VN denoises the current-reduced images in the first case, it reconstructs the undersampled data in the second case. Different VNs for denoising and reconstruction are trained on a single clinical 3D abdominal data set. The VNs are compared against state-of-the-art model-based denoising and sparse reconstruction techniques on a different clinical abdominal 3D data set with 4-fold dose reduction. Our results suggest that the proposed VNs enable higher radiation dose reductions and/or increase the image quality for a given dose.
Hierarchical Image Link Selection Scheme for Duplicate Structure Disambiguation

Repetitive and duplicate structures in urban areas have been a persistent problem in structure from motion (SfM). The resulting non-existent epipolar geometries (EGs) can signiﬁcantly bias and complicate averaging algorithms and lead to incorrect camera positions and structures in 3D reconstructions. We propose a lightweight pre-processing link selection scheme that produces an error-free camera trace which is used as direct inputs to a SfM pipeline. Images from local areas often share strong visual resemblance, however, correct view links bridging duplicate components are buried among many false links. The proposed scheme allows for independent expansions of local areas through a bottom up iterative grouping algorithm that exploits local resemblance. Independent components are then merged through links contributing to a global structure expansion selected by small scale reconstructions at joint positions. We demonstrate the effectiveness of our method on multiple laboratory and Internet-based image sets.
Continuous Contact Models for Space Manipulators Capturing Free-Floating Objects

This paper presents a comparative study of various existing continuous contact models for impact phase between space manipulator and free-floating target. Contact and impact are always present in space robotic missions that occur inevitably and frequently like the capture operation scenario. Due to the fact that the impact might cause damage or failure in space missions, the necessary precision should be taken into consideration so as to prevent undesired forces. To appraise accurately the produced forces, the authors employ various contact and impact models, including linear and nonlinear in the same situation in order to examine impact between space manipulator and target. Then, in order to draw a comparison between the proposed models for investigating the potential effects on manipulator joints and robot's base as well as target, the MATLAB simulations are done and the results are verified with MSC ADAMS software. The simulation results indicate the main differences of various models that can provide useful information for designing the impact controller.
Control an SMA-actuated rotary actuator by fractional order PID controller

The purpose of this paper is to design an appropriate controller for a rotary actuator actuated by Shape Memory Alloy (SMA) wires. The actuator is designed in such a way to be able to rotate in both clockwise and counterclockwise directions using a pair of SMA wires installed on it. The SMA wire is modeled by the Brinson model. Also dynamic and kinematic models of the system are considered in simulation. The convection coefficient of heat transfer equation is resulted from simulating the model and identifying input-output data of the system. According to the nonlinear behavior of the system and due to the nonlinearity of the SMA wires, a Fractional Order PID (FOPID) as a nonlinear controller is designed. The simulation results of trajectory tracking control for a reference input is compared with ones of a traditional PID controller. The better performance of the FOIPD controller with respect to PID controller is stablished.
Distributed Analytical Graph Identification

An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.