Reviewerly/exHarmony huggingface dataset:
Few-shot Weakly-Supervised Object Detection via Directional Statistics

Detecting novel objects from few examples has become an emerging topic in computer vision recently. However, current methods need fully annotated training images to learn new object categories which limits their applicability in real world scenarios such as field robotics. In this work, we propose a probabilistic multiple-instance learning approach for few-shot Common Object Localization (COL) and few-shot Weakly Supervised Object Detection (WSOD). In these tasks, only image-level labels, which are much cheaper to acquire, are available. We find that operating on features extracted from the last layer of a pretrained Faster-RCNN is more effective compared to previous episodic learning based few-shot COL methods. Our model simultaneously learns the distribution of the novel objects and localizes them via expectation-maximization steps. As a probabilistic model, we employ von Mises-Fisher (vMF) distribution which captures the semantic information better than Gaussian distribution when applied to the pre-trained embedding space. When the novel objects are localized, we utilize them to learn a linear appearance model to detect novel classes in new images. Our extensive experiments show that the proposed method, despite being simple, outperforms strong baselines in few-shot COL and WSOD, as well as large-scale WSOD tasks.
Edge-Promoting Adaptive Bayesian Experimental Design for X-ray Imaging

\bfA \bfb \bfs \bft \bfr \bfa \bfc \bft .This work considers sequential edge-promoting Bayesian experimental design for (discretized) linear inverse problems, exemplified by X-ray tomography.The process of computing a total variation--type reconstruction of the absorption inside the imaged body via lagged diffusivity iteration is interpreted in the Bayesian framework.Assuming a Gaussian additive noise model, this leads to an approximate Gaussian posterior with a covariance structure that contains information on the location of edges in the posterior mean.The next projection geometry is then chosen through A-or D-optimal Bayesian design, which corresponds to minimizing the trace or the determinant of the updated posterior covariance matrix that accounts for the new projection.Two-and three-dimensional numerical examples based on simulated data demonstrate the functionality of the introduced approach.\bfK \bfe \bfy \bfw \bfo \bfr \bfd \bfs .X-
A novel defect detection method with eliminating dust for specular surfaces based on structured-light modulation analysis technique

In modern industrial manufacturing, the demand for the surface inspection of specular objects has become more and more urgent. In this paper, a novel defect detection method with eliminating dust is systematically proposed to inspect surface defects of specular objects. The proposed approach can be applied to the defect detection of silicon wafers, optical lens, cell phone cover glass, etc. Structured-Light Modulation Analysis Technique (SMAT) is adopted to detect defects and dust simultaneously. Meanwhile, according to the discrepancy of polarization states between dust and other places, polarized illumination and a linear polarizer are exploited to photograph dust image exclusively. The dust image is used to remove dust from the modulation image; thus, the purified defect distribution can be obtained. Relevant experiments are also performed to prove the effectiveness of this proposed approach.
Evaluation of Correctness in Unsupervised Many-to-Many Image Translation

Given an input image from a source domain and a guidance image from a target domain, unsupervised many-to-many image-to-image (UMMI2I) translation methods seek to generate a plausible example from the target domain that preserves domain-invariant information of the input source image and inherits the domain-specific information from the guidance image. For example, when translating female faces to male faces, the generated male face should have the same expression, pose and hair color as the input female image, and the same facial hairstyle and other male-specific attributes as the guidance male image. Current state-of-the art UMMI2I methods generate visually pleasing images, but, since for most pairs of real datasets we do not know which attributes are domain-specific and which are domain-invariant, the semantic correctness of existing approaches has not been quantitatively evaluated yet. In this paper, we propose a set of benchmarks and metrics for the evaluation of semantic correctness of these methods. We provide an extensive study of existing state-of-the-art UMMI2I translation methods, showing that all methods, to different degrees, fail to infer which attributes are domain-specific and which are domain-invariant from data, and mostly rely on inductive biases hard-coded into their architectures. Our code can be found at https://github.com/dbash/umi2i_correctness.
Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis

Abstract The goal of this paper is to develop an effective approach allowing to capture accurately the intrinsic nature of data using an infinite shifted‐scaled Dirichlet mixture model (InSSDMM). This article extends the finite statistical model to a more efficient multidimensional infinite mixture. The flexibility of the developed framework is demonstrated via some challenging medical applications that concern diabetic retinopathy detection in eye images and pneumonia identification in chest X‐ray scans. The obtained results demonstrate the merits of such approach as compared to many other generative and discriminative models.
Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

Abstract Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).
Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival Prediction

Accurate brain tumor segmentation and survival prediction are two fundamental but challenging tasks in the computer aided diagnosis of gliomas. Traditionally, these two tasks were performed independently, without considering the correlation between them. We believe that both tasks should be performed under a unified framework so as to enable them mutually benefit each other. In this paper, we propose a multi-task deep learning model called segmentation then prediction (STP), to segment brain tumors and predict patient overall survival time. The STP model is composed of a segmentation module and a survival prediction module. The former uses 3D U-Net as its backbone, and the latter uses both local and global features. The local features are extracted by the last layer of the segmentation encoder, while the global features are produced by a global branch, which uses 3D ResNet-50 as its backbone. The STP model is jointly optimized for two tasks. We evaluated the proposed STP model on the BraTS 2020 validation dataset and achieved an average Dice similarity coefficient (DSC) of 0.790, 0.910, 0.851 for the segmentation of enhanced tumor core, whole tumor, and tumor core, respectively, and an accuracy of 65.5% for survival prediction.
Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint

The authors propose an adaptive, general and data-driven curvature penalty for signal denoising via the Schrödinge operator. The term is derived by assuming noise to be generally Gaussian distributed, a widely applied assumption in most 1D signal denoising applications. The proposed penalty term is simple and in closed-form, and it can be adapted to different types of signals as it depends on data-driven estimation of the smoothness term. Combined with semi-classical signal analysis, we refer this method as C-SCSA in the context. Comparison with existing methods is done on pulse shaped signals. It exhibits higher signal-to-noise ratio and also preserves peaks without much distortion, especially when noise levels are high. ECG signal is also considered, in scenarios with real and non-stationary noise. Experiments validate that the proposed denoising method does indeed remove noise accurately and consistently from pulse shaped signals compared to some of the state-of-the-art methods.
Matching Recruiters and Jobseekers on Twitter

An efficient job recommendation framework needs to recommend an appropriate jobseeker to a recruiter and vice-versa. Prior studies have mostly considered datasets from commercial job portals such as LinkedIn or CareerBuilder. However, these datasets are proprietary and not publicly available. Moreover, these portals charge their clients for offering customized services. Hence, we explore whether publicly available Twitter data can be a viable alternative to commercial job portals. We have extracted 0.76 million job-related tweets. We have manually annotated tweet-pairs from recruiters and jobseekers in the domain of computer science jobs. Next, we have employed Siamese architecture and considered multiple artificial neural network models with different word embeddings. We have achieved around 97% accuracy for some of our models. Our study demonstrates the potential of the Twitter platform for job recommendations.
Consensus-based Distributed Target Tracking in the Presence of Active False Targets

Multi-station radar fusion of detecting and tracking targets is currently a promising research direction, especially in the presence of active false targets. However, the existing researches are mainly based on the centralized radar network. In this regard, this paper proposes a target tracking algorithm based on data-level fusion in distributed network. First, unscented Kalman filter (UKF) algorithm is used to generate local state estimations. Second, according to the spatial position correlation of real targets, all of the local estimated values from multiple stations are associated, which is aimed to eliminate false targets by nearest neighbor (NN) algorithm. Third, covariance intersection (CI) algorithm and consensus algorithm are combined, named as consensus CI algorithm, in order to track the real target and improve the fusion accuracy. Finally, the effectiveness of the algorithm is verified through simulation.
WITHDRAWN: Nonlinear regression framework for geomagnetic data restoration analysis through machine learning techniques

To understand the developmental mechanism of the earth’s magnetic field, the credibility of geomagnetic evidence is important as it provides knowledge to be used for near-surface investigations. This paper proposes a technique for earth’s magnetic fields data restoration centered upon methods of machine learning. The conventional approach to linear interpolation is vulnerable to timely inadequacy and high costs, although there is a substantial change in the proposed process. Three conventional learning models, vector support, random forests, and gradient boosts have been developed in this paper. Also, a repetitive, deep study algorithm, the neural network has been studied to further enhance preparation. To describe hyperplane hide away from the consequences of instruction, the proposed learning models were developed. TThe mentioned regression hyperplane that maps the relationship between the incomplete data and the meaning. The defined hyperplane of regression is a relationship mapping between the incomplete and the preserved mock-up details information. Then the educated models, primarily the hyperplanes, have been used to restore the lost geomagnetic traces can be used for further restoration new field information has been obtained. Lastly, there have been statistical analysis analysis derived, y then the informed models have been reconstructed and recovered for more field data for validation with the lack of geomagnetic traces, especially hyperplanes. The trained models were then used for the reconstruction of lost geomagnetic traces, especially hyperplanes, and can be used to reconstruct new information obtained. Finally, numerical analyses have taken place, the results showed that the performance of our approaches was more effective than the traditional approach, as reconstruction accuracy has been updated, and that the reconstruction accuracy efficiency of the proposed method was increased byincrea increased 20 percent.
Kapur’s Entropy based Hybridised WCMFO Algorithm for Brain MR Image Segmentation

This article proposes a Kapur-based hybridized Water Cycle and Moth-Flame Optimisation (WCMFO) algorithm that combines a water cycle algorithm (WCA) and moth flame optimisation (MFO) in multilevel thresholding of brain MR image segmentation. The WCMFO algorithm, proposed by Khalilpourazari and Khalilpourazary, gives both WCA and MFO advantages, while avoiding some of the drawbacks of either approach on its own, as demonstrated by faster convergence with broader exploration and exploitation capabilities. Experiments on 10 axial, T2-weighted test images were performed using Kapur entropy as the objective function at a threshold level of m = 2–5. The spiral movement of the behaviour of the moths is used for better exploitation in the WCA to find the global optimum values. WCMFO results, such as objective function value, peak signal to noise ratio, Central processing unit time and standard deviation, are collated and compared with other existing adaptive wind-driven optimization algorithm, adaptive bacterial foraging and particle swarm optimization algorithms. Experimental findings and comparison demonstrated that hybridized WCMFO algorithm was superior to the other algorithms. Moreover, the best segmentation is achieved on grey matter, white matter and cerebrospinal fluid that allows for better clinical decision-making and diagnosis in the medical field. Therefore, the proposed multilevel thresholding-based hybridized WCMFO algorithm is believed to be the most prominent preference for segmenting such complex brain images.
Disparate Impact in Item Recommendation: A Case of Geographic Imbalance

Recommender systems are key tools to push items' consumption. Imbalances in the data distribution can affect the exposure given to providers, thus affecting their experience in online platforms. To study this phenomenon, we enrich two datasets and characterize data imbalance w.r.t. the country of production of an item (geographic imbalance). We focus on movie and book recommendation, and divide items into two classes based on their country of production, in a majority-versus-rest setting. To assess if recommender systems generate a disparate impact and (dis)advantage a group, we introduce metrics to characterize the visibility and exposure a group receives in the recommendations. Then, we run state-of-the-art recommender systems and measure the visibility and exposure given to each group. Results show the presence of a disparate impact that mostly favors the majority; however, factorization approaches are still capable of capturing the preferences for the minority items, thus creating a positive impact for the group. To mitigate disparities, we propose an approach to reach the target visibility and exposure for the disadvantaged group, with a negligible loss in effectiveness.
Recognition of Handwritten Digit in Free Space

In the area of computer vision, the handwritten digit recognition is gaining massive popularity. It showcases our work through this paper by putting a better and accurate method to understand manually written digit from 0 (zero) to 9 (nine) by a pen in mid-air/free space (3D). Our project identifies the nib of the pen in the user’s hand and tracks the flow of the nib of the pen [which writes the digit in mid-air/free space (3D)]. Then, it matches the dataset to recognize the digit written by the user. Several experiments on the MNIST dataset have been done to obtain specified results. A whooping training set of 60,000 primary examples along with a test set of 10,000 examples has been obtained from the MNIST’s database. Normalized size centred in fixed-size imaging is obtained from the database. The validation and effectiveness of the algorithm for handwritten digit inscribed in mid-air/free space (3D) with the use of the pen have been successfully obtained from our experiment outcomes. The concept of these projects can be effectively used in computer games, automatic teller machine (ATM), televisions, etc.
Trajectory clustering using mixed classification models

Trajectory classification has become frequent in clinical research to understand the heterogeneity of individual trajectories. The standard classification model for trajectories assumes no between‐individual variance within groups. However, this assumption is often not appropriate, which may overestimate the error variance of the model, leading to a biased classification. Hence, two extensions of the standard classification model were developed through a mixed model. The first one considers an equal between‐individual variance across groups, and the second one considers unequal between‐individual variance. Simulations were performed to evaluate the impact of these considerations on the classification. The simulation results showed that the first extended model gives a lower misclassification percentage (with differences up to 50%) than the standard one in case of presence of a true variance between individuals inside groups. The second model decreases the misclassification percentage compared with the first one (up to 11%) when the between‐individual variance is unequal between groups. However, these two extensions require high number of repeated measurements to be adjusted correctly. Using human chorionic gonadotropin trajectories after curettage for hydatidiform mole, the standard classification model classified trajectories mainly according to their levels whereas the two extended models classified them according to their patterns, which provided more clinically relevant groups. In conclusion, for studies with a nonnegligible number of repeated measurements, the use, in first instance, of a classification model that considers equal between‐individual variance across groups rather than a standard classification model, appears more appropriate. A model that considers unequal between‐individual variance may find its place thereafter.
Gaze dynamics with spatiotemporal guided feature descriptor for prediction of driver’s maneuver behavior

At different levels of driving automation, driver’s gaze maintains great indispensable importance on semantic perception of the surround. In this work, we model gaze dynamics and clarify its relationship with driver’s maneuver behaviors from personalized driving style. Firstly, this paper proposes an Occlusion-immune Face Detector (OFD) for facial landmark detection, which can adaptively solve the facial occlusion introduced by the body and glasses frame in the real-world driving scenarios. Meanwhile, an Eye-head Coordination Model is brought up to bridge the error gap in gaze direction through determining eye pose and head pose fused pattern. Then, a vectorized spatiotemporal guidance feature (STGF) descriptor combining gaze accumulation and gaze transition frequency is proposed to construct gaze dynamics within a time window. Finally, we predict driver’s maneuver behavior through STGF descriptor considering different driving styles to clarify the relationship between gaze dynamics and driving maneuver task. Natural driving data are sampled in a dedicated instrumented vehicle testbed, on which 15 drivers with three kind of driving styles participated. Experimental results show that the prediction model achieves the best performance, estimating driver’s behavior an average of 1 s ahead of actual behavior with 83.6% accuracy considering driving style, compared with other approaches.
Examining readiness of e-learning implementation using Aydin and Tasci model: A rural university case study in Indonesia

Humankind throughout the world are currently in a state of collapse due to the outbreak of Corona Virus Disease (COVID-19) which began in Wuhan, China. Later on, it was announced as the pandemic situation according to WHO. Overall countries have implemented social distancing, while Indonesia has implemented a policy of learning from home, working from home, and worship at home or better known as work from home (WFH). This study aimed to determine the level of readiness to implement e-learning during WFH due to the pandemic COVID-19 spreads along with the challenges in the 4.0 revolution in millennials. The model that used was the Aydin & Tasci model which measures four main factors namely technology, innovation, people, and self-development. Data collection was conducted online using Google Form, while data processing uses a descriptive statistical method mapped based on the Aydin & Tasci version of the e-learning readiness index. The results showed that the overall e-learning readiness index might indicate that the system was ready to be implemented, but required a slight increase in several factors, namely innovation increase, human, and self-development factors. The results and discussion of the study showed the readiness of e-learning to follow the demands of Revolution 4.0, even though it was done during WFH because of the COVID-19 pandemic outbreak.
Bearing-Only Active Sensing Under Merged Measurements

In this letter we propose an algorithm to actively track multiple moving targets using a bearing-only sensor in the presence of merged measurements. Merged measurements arise from sensor resolution constraints and therefore targets that are close in relative bearing to the sensor get reported as a single group measurement. We employ a merged measurement model in a nonlinear joint probabilistic data association filter for tracking multiple targets through merging events. We also propose an online adaptive planning algorithm that maneuvers the sensor in order to increase tracking performance. We introduce a novel method based on forward value iteration that incorporates the merged measurement information into the planning strategy. The resulting trajectory is biased away from situations where merged measurements occur, as this leads to more uncertainty in the target state estimates. We demonstrate our algorithm both in simulation as well as onboard real unmanned ground vehicles. This is the first time bearing-only tracking with merged measurements has been accomplished with a mobile sensor in practice. Furthermore, to the best of our knowledge, this is the first time merged measurement data association information has been utilized to effectively plan for such situations.