Reviewerly/exHarmony huggingface dataset:
ADA-AT/DT: An Adversarial Approach for Cross-Domain and Cross-Task Knowledge Transfer

We deal with the problem of cross-task and cross-domain knowledge transfer in the realm of scene understanding for autonomous vehicles. We consider the scenario where supervision is available for a pair of tasks in a source domain while it is available for only one of the tasks in the target domain. Given that, the goal is to perform inference for the task in the target which is devoid of any training information. We argue that the only reported work in learning across tasks and domains (AT/DT) [26] faces the problem of domain shift between the source and target domains, hindering predictions on the target domain when the transfer of knowledge is learned on a statistically different yet related source domain. As a remedy, we develop a novel framework called ADA-AT/DT based on the adversarial training strategy to ensure that the domain-gaps are minimized for the common cross-domain supervised task. This, in effect, helps in realizing a domain-independent task-transfer function that eventually helps in performing improved inference in the target domain. We demonstrate that our proposed method significantly outperforms [26] by using models with 81% fewer trainable parameters. In addition, we perform experiments on a transformation mapping similar to U-Net to ensure maximum exploitation of features for task transfer. Extensive experiments have been performed on four different domains (Synthia, CityScapes, Carla, and KITTI) for two visual tasks (depth estimation and semantic segmentation) to confirm the superiority of our method.
Controllable and Progressive Image Extrapolation

Image extrapolation aims at expanding the narrow field of view of a given image patch. Existing models mainly deal with natural scene images of homogeneous regions and have no control of the content generation process. In this work, we study conditional image extrapolation to synthesize new images guided by the input structured text. The text is represented as a graph to specify the objects and their spatial relation to the unknown regions of the image. Inspired by drawing techniques, we propose a progressive generative model of three stages, i.e., generating a coarse bounding-boxes layout, refining it to a finer segmentation layout, and mapping the layout to a realistic output. Such a multi-stage design is shown to facilitate the training process and generate more controllable results. We validate the effectiveness of the proposed method on the face and human clothing dataset in terms of visual results, quantitative evaluations, and flexible controls.
Low-rank Kalman filtering under model uncertainty

We consider a robust filtering problem where the nominal state space model is not reachable and different from the actual one. We propose a robust Kalman filter which solves a dynamic game: one player selects the least-favorable model in a given ambiguity set, while the other player designs the optimum filter for the least-favorable model. It turns out that the robust filter is governed by a low-rank risk sensitive-like Riccati equation. Finally, simulation results show the effectiveness of the proposed filter.
Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks

The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method can return a large amount of false detections. Our objectives in this paper were to design a training method that would reduce training time, decrease the number of false positives and alleviate the fine-tuning effort of an image classifier in a context of animal surveys. We acquired two highly unbalanced datasets of deer images with a UAV and trained a Resnet-18 classifier using hard-negative mining and a series of recent techniques. Our method achieved sub-decimal false positive rates on two test sets (1 false positive per 19,162 and 213,312 negatives respectively), while training on small but relevant fractions of the data. The resulting training times were therefore significantly shorter than they would have been using the whole datasets. This high level of efficiency was achieved with little tuning effort and using simple techniques. We believe this parsimonious approach to dealing with highly unbalanced, large datasets could be particularly useful to projects with either limited resources or extremely large datasets.
Ellipsoidal one-class constraint acquisition for quadratically constrained programming

We propose Ellipsoidal One-Class Constraint Acquisition (EOCCA), a fast and scalable algorithm for the acquisition of constraints for Mixed-Integer Quadratically Constrained Programming (MIQCP) models from data. EOCCA acquires a well-formed MIQCP model using solely the examples of the feasible solutions to this model. It combines x-means partitioning, standardization, and principal components analysis to preprocess the training set and then wraps the preprocessed data into several hyper-ellipsoids expressed using MIQCP constraints. These MIQCP constraints are projected back to the space of the original training set, and their further use does not require data preprocessing. Experimental evaluation shows that EOCCA scores better than a state-of-the-art algorithm in terms of fidelity of the acquired constraints to ground-truth constraints and achieves this in few orders of magnitude shorter time. We demonstrate the practical use case of EOCCA in a fully automated workflow of modeling and optimization of a rice farm using real-world data.
On Metrics Assessing the Information Content of Datasets for Population-Based Structural Health Monitoring

Within databases designed for population-based structural health monitoring, diagnostic information can be transferred between structures allowing inferences to be made across them. Information metrics can be developed for this case, where similarities and differences between data collected for monitoring of structures can be evaluated easily without relying on an in-depth, physics-based understanding of the data. By doing so, feature extraction for monitoring will be faster and more informed than through current methods.
Estimating the cognitive load in physical spatial navigation

Navigation is an essential skill that helps one to be aware of where they are in space and ambulate from a location to others.Many cognitive processes are involved in navigation tasks, even in the simplest scenario, such as landmarks encoding, cognitive map anchoring, goal-oriented planning, and motor executing.Engaging multiple tasks simultaneously could lead to higher cognitive load and attenuated navigation performance.In this study, we investigate the cognitive load of participants while they perform a navigation task.We demonstrated the ability to extract neural features from complex physical movement tasks, such as navigation.We found that retrosplenial complex (RSC) shows a distinct features for mental workload related task.We further evaluated participant's cognitive load with different machine learning algorithm and found that CNN is able to classify with 93% accuracy.The results provided a potential approach to study cognitive load in a more naturalistic scenario.
Handwritten Digit Recognition Based on Classification Functions

As a model of a machine learning, an incompletely specified classification function is used. As a benchmark problem, data for handwritten digits with 28 × 28 images were used. This data was converted into one with 14×14 = 196 pixels using a space filter. Also, the value of each pixel was binarized. With this operation, the original data was converted into a 196variable classification function that takes values from 0 to 9. For the training data, we had k = 58191 samples. Using a linear transformation, the 196-variable classification function was converted into a 25-variable function. We applied the testing data consisting of 9569 samples. The reduced classification function produced correct answers for 97.3% of the recognized test data. For unrecognized test data, the circuit for the reduced classification function produced "unrecognized" signals. The recognition circuit for handwritten digits can be implemented by a simple architecture: a cascade of a linear circuit and a memory. To increase the recognition rate, we also present methods using multiple classification functions and voters.
Motion detection using Passive Infrared Sensor using IoT

Abstract An IoT is the recent trend today which is applicable to all the real time applications. It is applied in all the sectors like smart city, home, appliance, Security as most important one which reduces the human operation and do all the works are done in automation. This will useful in all the areas and easy implementation. In that sense, in this paper I suggested the concept of Infrared radiation for motion detection by using PIR sensor. That sensor is implemented with the microcontroller which identifies the intruders movement which represented by high and low. This represents the motion detection, if the value is high there is a motion of intrusion, otherwise if the value is low it shows does not intruded by anyone. This technique is very compatible because only it requires three components it is detected by mixed signal processor and line transceiver.
Anomaly Detection Aided Budget Online Classification for Imbalanced Data Streams

Learning from imbalanced data streams differs from the traditional learning paradigm due to the issues of imbalanced classes. It has significant implications in a myriad of real-world applications, ranging from financial risk, network security, to medical diagnosis. Moreover, outliers usually appear in data streams. The issue of class imbalance or anomaly itself could negatively affect the performance of the underlying learning algorithms, and their combination makes the learning problem harder. In this work, we propose an anomaly detection aided budget online weighted learning method (BOW-LM) to identify positive and negative instances from imbalanced data streams. BOW-LM is based on the widely used Feedforward Networks with Random Weights. An agile lightweight anomaly detector is designed based on the nonlinear mapping of the network. To reduce computational complexity and to response promptly, BOW-LM employs a matrix correction technique to update the learning model by only <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) operations for each data chunk with LL hidden layer nodes. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness of BOW-LM.
Understanding Compositional Structures in Art Historical Images Using Pose and Gaze Priors

Image compositions as a tool for analysis of artworks is of extreme significance for art historians. These compositions are useful in analyzing the interactions in an image to study artists and their artworks. Max Imdahl in his work called Ikonik, along with other prominent art historians of the 20 $$^\mathrm{th}$$ century, underlined the aesthetic and semantic importance of the structural composition of an image. Understanding underlying compositional structures within images is challenging and a time consuming task. Generating these structures automatically using computer vision techniques (1) can help art historians towards their sophisticated analysis by saving lot of time; providing an overview and access to huge image repositories and (2) also provide an important step towards an understanding of man made imagery by machines. In this work, we attempt to automate this process using the existing state of the art machine learning techniques, without involving any form of training. Our approach, inspired by Max Imdahl's pioneering work, focuses on two central themes of image composition: (a) detection of action regions and action lines of the artwork; and (b) pose-based segmentation of foreground and background. Currently, our approach works for artworks comprising of protagonists (persons) in an image. In order to validate our approach qualitatively and quantitatively, we conduct a user study involving experts and non-experts. The outcome of the study highly correlates with our approach and also demonstrates its domain-agnostic capability. We have open-sourced the code: https://github.com/image-compostion-canvas-group/image-compostion- canvas
Generalization ability of online pairwise support vector machine

Pairwise classification depends on two input examples instead of one example and predicts whether the two input examples belong to the same class or to different classes. In this paper we investigate online pairwise support vector machine (OPSVM) algorithm with independently and identically distributed (i.i.d.) and non-i.i.d. samples. We first establish the convergence rates of the last iteration for OPSVM algorithm and obtain the fast convergence rates of OPSVM algorithm with strongly mixing sequence or i.i.d. samples for the case of polynomially decaying step size. We also introduce a novel OPSVM algorithm based on Markov selective sampling (OPSVM-MSS). The experimental results based on benchmark repository display that the classifier induced by OPSVM-MSS algorithm not only has smaller misclassification rate, but also the sampling and training total time is shorter compared to the classifier induced by the classical OPSVM algorithm based on randomly independent sampling (OPSVM-RIS).
Interclass Interference Suppression in Multi-Class Problems

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.
Novelty Detection of Beverage Bottle Images Based on Transfer Learning

The recent advances in image recognition are commonly based on the convolution neural network (CNN). Many CNN architectures have been investigated with a great success to build models that can recognize images correctly corresponding to the known classes. However, many applications require a model that can reject a novelty item that is not part of the known classes. Our goal is to solve the novelty detection problem by utilizing a pre-trained model approach. The pre-trained CNN models from the four well-known CNN architectures are used to extract the training features. The OC-SVM and Isolation Forest are implemented to train novelty detection models and to be investigated for performance evaluations. The F1 and F2 score are adopted as evaluation metrics and show that OC-SVM model trained on the features from NASNetLarge armed with a feature reduction achieves the best results in terms of detecting the novelty item comparing to other experimented CNN architectures.
Piracy and the Impaired Cyborg

This paper examines software piracy in the Global South from an accessibility lens, using the bio-technical metaphor of the 'cyborg.' Drawing on qualitative interviews with people with visual impairment (VI) from India and Peru, the paper interrogates the intimate relationships that users have with assistive technologies (ATs). It outlines the effectiveness of ATs in allowing users to actively control and shape their own lives and identities, and describes the various modalities that regulate the human body, technology, and human body-technology linkages. The paper argues that software piracy, when looked through the lens of the 'cyborg,' is an act of self-making that is motivated by a desire to gain autonomy and independence, i.e., it can be understood as a way to overcome the barriers that undermine access to the technological self. Further, software piracy allows a shift in the distribution of power from those who control and regulate the assistive technologies to the cyborgs themselves.
Learning to predict the quality of distorted-then-compressed images via a deep neural network

Being captured by amateur photographers, reciprocally propagated through multimedia pipelines, and compressed with different levels, real-world images usually suffer from a wide variety of hybrid distortions. Faced with this scenario, full-reference (FR) image quality assessment (IQA) algorithms can not deliver promising predictions due to the inferior references. Meanwhile, existing no-reference (NR) IQA algorithms remain limited in their efficacy to deal with different distortion types. To address this obstacle, we explore a NR-IQA metric by predicting the perceptual quality of distorted-then-compressed images using a deep neural network (DNN). First, we propose a novel two-stream DNN to handle both authentic distortions and synthetic compressions and adopt effective strategies to pre-train the two branches of the network. Specifically, we transfer the knowledge learned from in-the-wild images to account for authentic distortions by utilizing a pre-trained deep convolutional neural network (CNN) to provide meaningful initializations. Meanwhile, we build a CNN for synthetic compressions and pre-train it on a dataset including synthetic compressed images. Subsequently, we bilinearly pool these two sets of features as the image representation. The overall network is fine-tuned on an elaborately-designed auxiliary dataset, which is annotated by a reliable objective quality metric. Furthermore, we integrate the output of the authentic-distortion-aware branch with that of the overall network following a two-step prediction manner to boost the prediction performance, which can be applied in the distorted-then-compressed scenario when the reference image is available. Extensive experimental results on several databases especially on the LIVE Wild Compressed Picture Quality Database show that the proposed method achieves state-of-the-art performance with good generalizability and moderate computational complexity.
Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.
Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis

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Collaborative Data Anonymization for Privacy-Preserving Vehicular Ad-hoc Network

Optimization of data usability and privacy protection is a challenging goal. Although the released data are sanitized with the upgraded model of data semantic changer, always face privacy and scalability issues. On the other hand, local anonymization may not be suitable in a multi-nodal environment such as a vehicular network. Alternatively, hiding sensitive attributes are essential alongside discreet data privacy. In this paper, we proposed a collaborative privacy-preserving data anonymization technique for the vehicular network. Moreover, the method is capable of achieving a desirable level of data sanitization in a group. The use of k-anonymity, l-diversity and t-closeness make the proposed technique more strong to protect data privacy in different stages of communication. Furthermore, the performed analysis of the proposed scheme ensures usability and practicality. Lastly, the future direction of the stipulated field shows the research path in collaborative data anonymization in the vehicular environment.
Plausible 3D Face Wrinkle Generation Using Variational Autoencoders

Realistic 3D facial modeling and animation have been increasingly used in many graphics, animation, and virtual reality applications. However, generating realistic fine-scale wrinkles on 3D faces, in particular, on animated 3D faces, is still a challenging problem that is far away from being resolved. In this article we propose an end-to-end system to automatically augment coarse-scale 3D faces with synthesized fine-scale geometric wrinkles. By formulating the wrinkle generation problem as a supervised generation task, we implicitly model the continuous space of face wrinkles via a compact generative model, such that plausible face wrinkles can be generated through effective sampling and interpolation in the space. We also introduce a complete pipeline to transfer the synthesized wrinkles between faces with different shapes and topologies. Through many experiments, we demonstrate our method can robustly synthesize plausible fine-scale wrinkles on a variety of coarse-scale 3D faces with different shapes and expressions.
Shadowing in Many-Valued Nested Structures

Belief shadowing is a relatively recent approach to belief change. In essence, shadowing depends on accepting beliefs of others at the expense of dismissing, perhaps temporarily, some of the own ones. As a transient belief change, it is useful when an agent, acting as a group member or playing a particular role, has to adopt adequate "external" beliefs. So far two forms of shadowing, single and multiple, have been considered. While the former specifies shadowing when an agent belongs to a single group or plays a single role, multiple shadowing relaxes this restriction. In the paper we generalize shadowing to arbitrary finitely many-valued logics and consider more complex semantical structures allowing arbitrarily nested sets of worlds. We show that in this general setting multiple shadowing can be represented by single shadowing. The complexity of queries involving such generic shadowing operators is also analyzed.