Reviewerly/exHarmony huggingface dataset:
Novel View Synthesis from a Single Unposed Image via Unsupervised Learning

Novel view synthesis aims to generate novel views from one or more given source views. Although existing methods have achieved promising performance, they usually require paired views with different poses to learn a pixel transformation. This article proposes an unsupervised network to learn such a pixel transformation from a single source image. In particular, the network consists of a token transformation module that facilities the transformation of the features extracted from a source image into an intrinsic representation with respect to a pre-defined reference pose and a view generation module that synthesizes an arbitrary view from the representation. The learned transformation allows us to synthesize a novel view from any single source image of an unknown pose. Experiments on the widely used view synthesis datasets have demonstrated that the proposed network is able to produce comparable results to the state-of-the-art methods despite the fact that learning is unsupervised and only a single source image is required for generating a novel view. The code will be available upon the acceptance of the article.
CSIE: Coded strip-patterns image enhancer embedded in structured light-based methods

When a coded strip-patterns image (CSI) is captured in a structured light system (SLs), it often suffers from low visibility at low exposure settings. Besides degrading the visual perception of the CSI, this poor quality also significantly affects the performance of 3D model reconstruction. Most of the existing image-enhanced methods, however, focus on processing natural images but not CSI. In this paper, we propose a novel and effective CSI enhancer (CSIE) designed for SLs. More concretely, a bidirectional perceptual consistency (BPC) criterion, including relative grayscale (RG), exposure, and texture level priors, is first introduced to ensure visual consistency before and after enhancement. Then, constrained by BPC, the optimization function estimates solutions of illumination with piecewise smoothness and reflectance with detail preservation. With well-refined solutions, CSIE results can be achieved accordingly and further improve the details performance of 3D model reconstruction. Experiments on multiple sets of challenging CSI sequences show that our CSIE outperforms the existing used for natural image-enhanced methods in terms of 2D enhancement, point clouds extraction (at least 17% improvement), and 3D model reconstruction.
The max-out min-in problem: A tool for data analysis

Consider a graph with vertex set V and non-negative weights on the edges. For every subset of vertices S, define ϕ(S) to be the sum of the weights of edges with one vertex in S and the other in V∖S, minus the sum of the weights of the edges with both vertices in S. We consider the problem of finding S⊆V for which ϕ(S) is maximized. We call this combinatorial optimization problem the max-out min-in problem (MOMIP). In this paper we (i) present a linear 0/1 formulation and a quadratic unconstrained binary optimization formulation for MOMIP; (ii) prove that the problem is NP-hard; (iii) report results of computational experiments on simulated data to compare the performances of the two models; (iv) illustrate the applicability of MOMIP for two different topics in the context of data analysis, namely in the selection of variables in exploratory data analysis and in the identification of clusters in the context of cluster analysis; and (v) introduce a generalization of MOMIP that includes, as particular cases, the well-known weighted maximum cut problem and a novel problem related to independent dominant sets in graphs.
Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning

Abstract The determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients’ responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. In this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. To reduce the impacts of the heterogeneity in patients’ responses on analysis results, the method conducts risk analysis by mining historical similar patients from Electronic Health Records (EHRs) in multiple hospitals using federated learning (FL). For this, the Recursive Feature Elimination based on the Support Vector Machine (SVM) and Deep Learning Important FeaTures (DeepLIFT) are extended into the FL framework to select key features and determine key feature weights for identifying historical similar patients. Then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. According to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. The related data are valuable for the doctor and patient to make their decisions. Experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method.
GUI-Based, Efficient Genetic Programming and AI Planning for Unity3D

We present a GUI-driven and efficient Genetic Programming (GP) and AI Planning framework designed for agent-based learning research. Our framework, ABL-Unity3D, is built in Unity3D, a game development environment. ABL-Unity3D addresses challenges entailed in co-opting Unity3D: making the simulator serve agent learning rather than humans playing a game, lowering fitness evaluation time to make learning computationally feasible, and interfacing GP with an AI Planner to support hybrid algorithms. We achieve this by developing a Graphical User Interface (GUI) using the Unity3D editor’s programmable interface and performance optimizations. These optimizations result in at least a 3x speedup. In addition, we describe ABL-Unity3D by explaining how to use it for an example experiment using GP and AI Planning. We benchmark ABL-Unity3D by measuring the performance and speed of the AI Planner alone, GP alone, and the AI Planner with GP.
Avoiding Obstacles with Geometric Constraints on LiDAR Data for Autonomous Robots

Obstacle avoidance continues to be a major component of any motion planning algorithms for autonomous mobile robots operating in previously unknown environment. We present a novel approach of designing obstacle avoidance algorithms with two-dimensional LiDAR data. We derive mathematical expressions for the region of interest (RoI) using conic section of an ellipse on the two-dimensional data points collected from the LiDAR. The derivation is based on the physical constraints from the robot's body, and actuator limits for successfully avoiding any obstacle on the path. One of the major challenges while designing an obstacle avoidance algorithm using the greedy approach of avoiding the closest obstacle from the LiDAR data is the freezing robot problem. Most often the robot freezes as it gets stuck at a local minima between two closely placed obstacles. We propose to mitigate this problem by considering an elliptical field of view (FoV) for the LiDAR data and giving importance to all the obstacles inside the elliptical section proportional to their respective distances from the robot. We test our algorithm in the simulated environment of ROS-Gazebo and show how elliptical FoV is better at navigating narrow passages compared to conventional circular FoV based algorithms.
Genetic Programming for Interpretable and Explainable Machine Learning

Increasing demand for human understanding of machine decision-making is deemed crucial for machine learning (ML) methodology development and further applications. It has inspired the emerging research field of interpretable and explainable ML/AI. Techniques have been developed to either provide additional explanations to a trained ML model or learn innately compact and understandable models. Genetic programming (GP), as a powerful learning instrument, holds great potential in interpretable and explainable learning. In this chapter, we first discuss concepts and popular methods in interpretable and explainable ML, and review research using GP for interpretability and explainability. We then introduce our previously proposed GP-based framework for interpretable and explainable learning applied to bioinformatics.
Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups

Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from EHRs is heterogeneous and temporally irregular. Therefore, traditional machine learning methods like PCA are ill-suited for analysis of EHR-derived patient data. We propose to address these issues with a new methodology based on training a gated recurrent unit (GRU) autoencoder directly on health record data. Our method learns a low-dimensional feature space by training on patient data time series, where the time of each data point is expressed explicitly. We use positional encodings for time, allowing our model to better handle the temporal irregularity of the data. We apply our method to data from the Medical Information Mart for Intensive Care (MIMIC-III). Using our data-derived feature space, we can cluster patients into groups representing major classes of disease patterns. Additionally, we show that our feature space exhibits a rich substructure at multiple scales.
Who are the evil backstage manipulators: Boosting graph attention networks against deep fraudsters

Telecommunications fraud causes vast economic losses all over the world every year. Although Graph Neural Networks (GNNs) brings new possibilities to the solution of fraud detection problem, their performance in telecom fraud detection is not satisfying. An important reason is that little prior work has noticed the deep fraudster phenomenon, which makes it difficult to be eradicated. Another reason may be the shortage of available datasets for researchers. After all, few operators are willing to publish telecom fraud detection benchmark datasets due to the sensitivity and privacy of subscriber data, resulting in less related research work. In this paper, we rearrange and publish two real-world telecom fraud detection datasets based on publicly available sources on the Internet. And we propose a GNN-based semi-supervised telecom fraud detection method by Boosting graph Attention Networks Against Deep frAudsters (BANADA). Specifically, we first learn different neighbor weights with the help of inner-layer attention, and then design simplified inter-layer attention to achieve inter-layer feature aggregation. Finally, we use GNNs with different depths as different base classifiers and perform ensemble learning through AdaBoost to discriminate deep fraudsters. Extensive experiments on two real-world telecom fraud datasets demonstrate the effectiveness of our proposed BANADA, which outperforms all state-of-the-art GNNs and GNN-based fraud detectors. The BANADA code and datasets are available at https://github.com/xxhu94/BANADA.
Synthetic Undecidability and Incompleteness of First-Order Axiom Systems in Coq

Abstract We mechanise the undecidability of various first-order axiom systems in Coq, employing the synthetic approach to computability underlying the growing Coq Library of Undecidability Proofs. Concretely, we cover both semantic and deductive entailment in fragments of Peano arithmetic (PA) as well as ZF and related finitary set theories, with their undecidability established by many-one reductions from solvability of Diophantine equations, i.e. Hilbert’s tenth problem (H10), and the Post correspondence problem (PCP), respectively. In the synthetic setting based on the computability of all functions definable in a constructive foundation, such as Coq’s type theory, it suffices to define these reductions as meta-level functions with no need for further encoding in a formalised model of computation. The concrete cases of PA and the considered set theories are supplemented by a general synthetic theory of undecidable axiomatisations, focusing on well-known connections to consistency and incompleteness. Specifically, our reductions rely on the existence of standard models, necessitating additional assumptions in the case of full ZF, and all axiomatic extensions still justified by such standard models are shown incomplete. As a by-product of the undecidability of set theories formulated using only membership and no equality symbol, we obtain the undecidability of first-order logic with a single binary relation.
Contrast based background and foreground channel prior for single image dehazing

To reduce haze in an image, a contrast-based background and foreground channel prior (BFCP) is proposed in this work. The haze will affect both colour and contrast, and the majority of haze reduction techniques rely on the dark channel prior (DCP) as well as the dark and bright channel priors (DBCP). Even though the DCP and DBCP produced accurate results, they provide a less precise estimate of the haze depth because they do not take into account the local contrast and spikes in the haze image histogram. The goal of this BFCP is to improve the estimation of the transmission map and to correct the spikes in the histogram of the hazed image. By utilizing contrast differences in the background and foreground, BFCP improves transmission map estimates. This method is tested on images of (i) indoor haze, (ii) outdoor haze, (iii) dense haze, (iv) night haze, and (v) fog.
Deep reinforcement learning for data-efficient weakly supervised business process anomaly detection

Abstract The detection of anomalous behavior in business process data is a crucial task for preventing failures that may jeopardize the performance of any organization. Supervised learning techniques are impracticable because of the difficulties of gathering huge amounts of labeled business process anomaly data. For this reason, unsupervised learning techniques and semi-supervised learning approaches trained on entirely labeled normal data have dominated this domain for a long time. However, these methods do not work well because of the absence of prior knowledge of true anomalies. In this study, we propose a deep weakly supervised reinforcement learning-based approach to identify anomalies in business processes by leveraging limited labeled anomaly data. The proposed approach is intended to use a small collection of labeled anomalous data while exploring a huge set of unlabeled data to find new classes of anomalies that are outside the scope of the labeled anomalous data. We created a unique reward function that combined the supervisory signal supplied by a variational autoencoder trained on unlabeled data with the supervisory signal provided by the environment’s reward. To further reduce data deficiency, we introduced a sampling method to allow the effective exploration of the unlabeled data and to address the imbalanced data problem, which is a common problem in the anomaly detection field. This approach depends on the proximity between the data samples in the latent space of the variational autoencoder. Furthermore, to efficiently model the sequential nature of business process data and to handle the long-term dependences, we used a long short-term memory network combined with a self-attention mechanism to develop the agent of our reinforcement learning model. Multiple scenarios were used to test the proposed approach on real-world and synthetic datasets. The findings revealed that the proposed approach outperformed five competing approaches by efficiently using the few available anomalous examples.
Incremental estimation of low-density separating hyperplanes for clustering large data sets

An efficient unsupervised method for obtaining low-density hyperplane separators is proposed. The method is based on a modified stochastic gradient descent applied on a convolution of the empirical distribution function with a smoothing kernel. Low-density hyperplanes are motivated by the fact that they avoid intersecting high density regions, and so tend to pass between high density clusters, thus separating them from one another, while keeping the individual clusters intact. Multiple hyperplanes can be combined in a hierarchical model to obtain a complete clustering solution. A simple post-processing of solutions induced by large collections of hyperplanes yields an efficient and accurate clustering method, capable of automatically selecting the number of clusters. Experiments show that the proposed method is highly competitive in terms of both speed and accuracy when compared with relevant benchmarks. Code is available in the form of an R package at https://github.com/DavidHofmeyr/iMDH.
DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language

Abstract Object Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry.
The University's "Third Mission": Exploring the Potential of University Campuses in Interaction with Urban Communities

City universities play a leading role in the formation of the educational, socio-cultural, intellectual environment of the city. At the same time, a new paradigm of interaction between universities and citizens is being formed as part of the implementation of the idea of "cities as communities of citizens", when urban communities become full-fledged subjects of city development along with city authorities. In this regard, the subject of the study was to identify the potential of the university in interaction with urban communities. Research methods: survey, focus group interview with elements of brainstorming. The study involved representatives of various urban communities of Moscow (N=170), students, teachers of the Moscow State Pedagogical University (N=358). The results of the study. The priority areas of interaction between university campuses and urban communities can be identified as: educational (counseling and support of citizens, courses, clubs, webinar programs, clubs for children and adults, lectures; popularization of topics at the request of communities; supervision and care); diagnostic; research; volunteer; sports and recreation; infrastructure; cultural and leisure; expert; development of the city's infrastructure. When building such interaction, it is important to take into account: regulatory and legal aspects of providing university infrastructure to urban communities and attracting teachers and students to implement educational programs for citizens of different ages; the financial side of interaction with urban communities; requests from urban communities; psychological and organizational aspects of interaction with urban communities; interaction with municipalities and representatives of district administrations, educational and other organizations working with the public and interested in the resources of the university.
Operational Domain Name Classification: From Automatic Ground Truth Generation to Adaptation to Missing Values

With more than 350 million active domain names and at least 200,000 newly registered domains per day, it is technically and economically challenging for Internet intermediaries involved in domain registration and hosting to monitor them and accurately assess whether they are benign, likely registered with malicious intent, or have been compromised. This observation motivates the design and deployment of automated approaches to support investigators in preventing or effectively mitigating security threats. However, building a domain name classification system suitable for deployment in an operational environment requires meticulous design: from feature engineering and acquiring the underlying data to handling missing values resulting from, for example, data collection errors. The design flaws in some of the existing systems make them unsuitable for such usage despite their high theoretical accuracy. Even worse, they may lead to erroneous decisions, for example, by registrars, such as suspending a benign domain name that has been compromised at the website level, causing collateral damage to the legitimate registrant and website visitors. In this paper, we propose novel approaches to designing domain name classifiers that overcome the shortcomings of some existing systems. We validate these approaches with a prototype based on the COMAR (COmpromised versus MAliciously Registered domains) system focusing on its careful design, automated and reliable ground truth generation, feature selection, and the analysis of the extent of missing values. First, our classifier takes advantage of automatically generated ground truth based on publicly available domain name registration data. We then generate a large number of machine-learning models, each dedicated to handling a set of missing features: if we need to classify a domain name with a given set of missing values, we use the model without the missing feature set, thus allowing classification based on all other features. We estimate the importance of features using scatter plots and analyze the extent of missing values due to measurement errors. Finally, we apply the COMAR classifier to unlabeled phishing URLs and find, among other things, that 73% of corresponding domain names are maliciously registered. In comparison, only 27% are benign domains hosting malicious websites. The proposed system has been deployed at two ccTLD registry operators to support their anti-fraud practices.