Reviewerly/exHarmony huggingface dataset:
Enhancing Density Peak Clustering via Density Normalization

Clustering is able to find out implicit data distribution and is especially useful in data driven machine learning. Density based clustering has an attractive property of detecting clusters of arbitrary structures. The density peak algorithm makes use of two assumptions to detect cluster centers and then groups the other data. This approach is simple to implement and shown to be promising in many experiments. However, we find its clustering results are dependent on density kernel types and kernel parameters, and density difference across clusters also influences the results significantly. In this paper, we make a detailed study of the density peak algorithm and attribute the problems to the local density criterion in detecting cluster centers. We then use density normalization to relieve the influence of the problems, and present a density kernel to further improve clustering results. We conduct experiments with different types of datasets to demonstrate the performance of our approach.
Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning

In this work, we mainly study the mechanism of learning the steganographic algorithm as well as combining the learning process with adversarial learning to learn a good steganographic algorithm. To handle the problem of embedding secret messages into the specific medium, we design a novel adversarial module to learn the steganographic algorithm, and simultaneously train three modules called generator, discriminator and steganalyzer. Different from existing methods, the three modules are formalized as a game to communicate with each other. In the game, the generator and discriminator attempt to communicate with each other using secret messages hidden in an image. While the steganalyzer attempts to analyze whether there is a transmission of confidential information. We show that through unsupervised adversarial training, the adversarial model can produce robust steganographic solutions, which acts like an encryption. Furthermore, we propose to utilize supervised adversarial training method to train a robust steganalyzer, which is utilized to discriminate whether an image contains secret information. Extensive experiments demonstrate the effectiveness of the proposed method on publicly available datasets.
Quantum speedups for Exponential-Time dynamic programming algorithms

In this paper we study quantum algorithms for NP-complete problems whose best classical algorithm is an exponential time application of dynamic programming. We introduce the path in the hypercube problem that models many of these dynamic programming algorithms. In this problem we are asked whether there is a path from 0n to 1n in a given subgraph of the Boolean hypercube, where the edges are all directed from smaller to larger Hamming weight. We give a quantum algorithm that solves path in the hypercube in time O*(1.817n). The technique combines Grover's search with computing a partial dynamic programming table. We use this approach to solve a variety of vertex ordering problems on graphs in the same time O*(1.817n), and graph bandwidth in time O*(2.946n). Then we use similar ideas to solve the travelling salesman problem and minimum set cover in time O*(1.728n).
Structure Identification of Difference Equations with Interval Estimates of their Parameters

Validate the method of structure identification with the use parameter identification methods in order to find single model and approbation this method on the example of constructing a model of the dynamics concentration of harmful vehicles emissions.
Automatic Understanding and Formalization of Plane Geometry Proving Problems in Natural Language: A Supervised Approach

Automatically understanding natural language problems is a long-standing challenging research problem in automatic solving. This paper models the understanding of geometry problems as a problem of relation extraction, instead of as the problem of semantic understanding of natural language. Then it further proposes a supervised machine learning method to extract geometric relations, targeting to produce a group of relations to represent the given geometry problem. This method identifies the actual geometric relations from the relation candidates using a classifier trained from the labelled examples. The formalized geometric relations can then be transformed into the target system-native representations for manipulation in various tasks. Experiments conducted on the test problem dataset show that the proposed method can extract geometric relations at high F 1 scores. The comparisons also demonstrate that the proposed method can achieve good performance against the baseline methods. Integrating the automatic understanding method with different geometry systems will greatly enhance the efficiency and intelligence in geometry tutoring.
Direct normal irradiance modeling: Evaluating the impact on accuracy of worldwide gridded aerosol databases

Clear-sky solar irradiance is required in multiple solar applications. In the general case, it has to be simulated with a clear-sky solar radiation model with inputs from synthetic data sources such as atmospheric reanalyses or similar numerical weather products. Given the high sensitivity of direct normal irradiance (DNI) to aerosol optical depth (AOD), it is expected that the use of such synthetic sources for AOD results in a non-negligible change in the DNI simulation error as compared to the use of AOD values retrieved at ground stations. This study evaluates the DNI simulation error of the high-performance SMARTS clear-sky solar radiation model using AOD from two well-known atmospheric reanalyses comparatively to the use of AOD values retrieved at ground sites. The study is conducted at four arid sites with one year of collocated DNI observations from the Baseline Surface Radiation Network (BSRN) and AOD observations from the Aerosol Robotic Network (AERONET) at each site. It is found that both DNI bias and dispersion using synthetic AOD databases may increase up to 6 times compared to when AOD values are retrieved from AERONET, thus confirming the general need for site-adaptation post-processing techniques to obtain low-uncertainty solar resource assessments.
Multi-source sequential knowledge regression by using transfer RNN units

Transfer learning has achieved a lot of success in deep neural networks to reuse useful knowledge from source domains. However, most of the existing transfer learning strategies on neural networks are for classification tasks or based on simple training strategies, which have limited use in multi-source knowledge regression due to the ineffectiveness of learning common latent features and source information loss in regression. In this paper, we propose transferable Recurrent Neural Network (RNN) units on the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to adapt source knowledge in multi-source regression scenarios. Specifically, two knowledge adaptation methods are proposed, the first one utilizes similarity weights as the transfer coefficients of each source, and the other defines a transfer-gate to control the flow of source knowledge. By using the proposed methods, useful source knowledge embedded in both internal state and output is adapted. Extensive experiments on both synthetic data and human motion prediction tasks on the Human 3.6M dataset demonstrate the superiority of our transfer RNN units compared with conventional models.
A Crowdsensing-based Cyber-physical System for Drone Surveillance Using Random Finite Set Theory

Given the popularity of drones for leisure, commercial, and government (e.g., military) usage, there is increasing focus on drone regulation. For example, how can the city council or some government agency detect and track drones more efficiently and effectively, say, in a city, to ensure that the drones are not engaged in unauthorized activities? Therefore, in this article, we propose a crowdsensing-based cyber-physical system for drone surveillance. The proposed system, CSDrone, utilizes surveillance data captured and sent from citizens’ mobile devices (e.g., Android and iOS devices, as well as other image or video capturing devices) to facilitate jointly drone detection and tracking. Our system uses random finite set (RFS) theory and RFS-based Bayesian filter. We also evaluate CSDrone’s effectiveness in drone detection and tracking. The findings demonstrate that in comparison to existing drone surveillance systems, CSDrone has a lower cost, and is more flexible and scalable.
Cooperative Thinking: Analyzing a new framework for software engineering education

Computational Thinking (CT) and Agile Values (AV) focus respectively on the individual capability to think algorithmically, and on the principles of collaborative software development. Although these two dimensions of software engineering education complement each other, very few studies explored their interaction. In this paper we use an exploratory Structural Equation Modeling technique to introduce and analyze Cooperative Thinking (CooT), a model of team-based computational problem solving. We ground our model on the existing literature and validate it through Partial Least Square modeling. Cooperative Thinking is new competence which aim is to support cooperative problem solving of technical contents suitable to deal with complex software engineering problems. This article suggests to tackle the CooT construct as an education goal, to train students of software development to improve both their individual and teaming performances.
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces

We consider differentially private algorithms for reinforcement learning in continuous spaces, such that neighboring reward functions are indistinguishable. This protects the reward information from being exploited by methods such as inverse reinforcement learning. Existing studies that guarantee differential privacy are not extendable to infinite state spaces, as the noise level to ensure privacy will scale accordingly to infinity. Our aim is to protect the value function approximator, without regard to the number of states queried to the function. It is achieved by adding functional noise to the value function iteratively in the training. We show rigorous privacy guarantees by a series of analyses on the kernel of the noise space, the probabilistic bound of such noise samples, and the composition over the iterations. We gain insight into the utility analysis by proving the algorithm's approximate optimality when the state space is discrete. Experiments corroborate our theoretical findings and show improvement over existing approaches.
Implicit Posterior Variational Inference for Deep Gaussian Processes

A multi-layer deep Gaussian process (DGP) model is a hierarchical composition of GP models with a greater expressive power. Exact DGP inference is intractable, which has motivated the recent development of deterministic and stochastic approximation methods. Unfortunately, the deterministic approximation methods yield a biased posterior belief while the stochastic one is computationally costly. This paper presents an implicit posterior variational inference (IPVI) framework for DGPs that can ideally recover an unbiased posterior belief and still preserve time efficiency. Inspired by generative adversarial networks, our IPVI framework achieves this by casting the DGP inference problem as a two-player game in which a Nash equilibrium, interestingly, coincides with an unbiased posterior belief. This consequently inspires us to devise a best-response dynamics algorithm to search for a Nash equilibrium (i.e., an unbiased posterior belief). Empirical evaluation shows that IPVI outperforms the state-of-the-art approximation methods for DGPs.
Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection

We address the problem of Duplicate Question Detection (DQD) in low-resource domainspecific Community Question Answering forums.Our multi-view framework MV-DASE combines an ensemble of sentence encoders via Generalized Canonical Correlation Analysis, using unlabeled data only.In our experiments, the ensemble includes generic and domain-specific averaged word embeddings, domain-finetuned BERT and the Universal Sentence Encoder.We evaluate MV-DASE on the CQADupStack corpus and on additional low-resource Stack Exchange forums.Combining the strengths of different encoders, we significantly outperform BM25, all singleview systems as well as a recent supervised domain-adversarial DQD method.
Safe Exploration for Interactive Machine Learning

In interactive machine learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function. While IML methods, e.g., Bayesian optimization and active learning, have been successful in applications, on real-world systems they must provably avoid unsafe decisions. To this end, safe IML algorithms must carefully learn about a priori unknown constraints without making unsafe decisions. Existing algorithms for this problem learn about the safety of all decisions to ensure convergence. This is sample-inefficient, as it explores decisions that are not relevant for the original IML objective. In this paper, we introduce a novel framework that renders any existing unsafe IML algorithm safe. Our method works as an add-on that takes suggested decisions as input and exploits regularity assumptions in terms of a Gaussian process prior in order to efficiently learn about their safety. As a result, we only explore the safe set when necessary for the IML problem. We apply our framework to safe Bayesian optimization and to safe exploration in deterministic Markov Decision Processes (MDP), which have been analyzed separately before. Our method outperforms other algorithms empirically.
Towards Generalizable Forgery Detection with Locality-aware AutoEncoder.

With advancements of deep learning techniques, it is now possible to generatesuper-realistic fake images and videos. These manipulated forgeries could reachmass audience and result in adverse impacts on our society. Although lots ofefforts have been devoted to detect forgeries, their performance dropssignificantly on previously unseen but related manipulations and the detectiongeneralization capability remains a problem. To bridge this gap, in this paperwe propose Locality-aware AutoEncoder (LAE), which combines fine-grainedrepresentation learning and enforcing locality in a unified framework. In thetraining process, we use pixel-wise mask to regularize local interpretation ofLAE to enforce the model to learn intrinsic representation from the forgeryregion, instead of capturing artifacts in the training set and learningspurious correlations to perform detection. We further propose an activelearning framework to select the challenging candidates for labeling, to reducethe annotation efforts to regularize interpretations. Experimental resultsindicate that LAE indeed could focus on the forgery regions to make decisions.The results further show that LAE achieves superior generalization performancecompared to state-of-the-arts on forgeries generated by alternativemanipulation methods.
Game-theoretic analysis of development practices: Challenges and opportunities

Developers continuously invent new practices, usually grounded in hard-won experience, not theory. Game theory studies cooperation and conflict; its use will speed the development of effective processes. A survey of game theory in software engineering finds highly idealised models that are rarely based on process data. This is because software processes are hard to analyse using traditional game theory since they generate huge game models. We are the first to show how to use game abstractions, developed in artificial intelligence, to produce tractable game-theoretic models of software practices. We present Game-Theoretic Process Improvement (GTPI), built on top of empirical game-theoretic analysis. Some teams fall into the habit of preferring “quick-and-dirty” code to slow-to-write, careful code, incurring technical debt. We showcase GTPI’s ability to diagnose and improve such a development process. Using GTPI, we discover a lightweight intervention that incentivises developers to write careful code: add a singlecode reviewer who needs to catch only 25% of kludges. This 25% accuracy is key; it means that a reviewer does not need to examine each commit in depth, making this process intervention cost-effective.
A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration

Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such transformations is expected to capture the underlying image and shape variations, and hence these transformations are required to produce anatomically feasible correspondences. This is usually enforced through some smoothness-based generic metric or regularization of the deformation field. Alternatively, population-based regularization has been shown to produce anatomically accurate correspondences in cases where anatomically unaware (i.e., data independent) regularization fail. Recently, deep networks have been used to generate spatial transformations in an unsupervised manner, and, once trained, these networks are computationally faster and as accurate as conventional, optimization-based registration methods. However, the deformation fields produced by these networks require smoothness penalties, just as the conventional registration methods, and ignores population-level statistics of the transformations. Here, we propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration. This regularization is in the form of a bottleneck autoencoder, which learns and adapts to the population of transformations required to align input images by encoding the transformations to a low dimensional manifold. The proposed architecture produces deformation fields that describe the population-level features and associated correspondences in an anatomically relevant manner and are statistically compact relative to the state-of-the-art approaches while maintaining computational efficiency. We demonstrate the efficacy of the proposed architecture on synthetic data sets, as well as 2D and 3D medical data.
A deep learning approach for quality enhancement of surveillance video

The growing number of surveillance cameras imposes great demand on high efficiency video coding. Although modern video coding standards have significantly improved the video coding efficiency, they are designed for general video rather than surveillance video. The special characteristics of surveillance video leave a large space for further performance improvement. In this paper, we leverage a deep learning approach to enhance the quality of compressed surveillance video. More specifically, we formulate the problem of frame enhancement as a regression problem and design a Residual Squeeze-and-Excitation Network (RSE-Net), to address it. RSE-Net extensively exploits the non-linear mapping between the reconstructed frame and the ground truth, with only a small number of parameters. Moreover, By improving You Only Look Once (YOLO) network, we successfully detect the grouped vehicles within a frame. A novel model training scheme is then developed through learning from the grouped vehicles. With the proposed scheme, we train a global model for both foreground and background of surveillance video. Experimental results show that our method achieves average 0.40 dB, 0.22 dB and 0.24 dB PSNR gains over H.265/HEVC anchor in AI, LDP and RA configurations, and produces visually pleasing results when applied to compressed surveillance video.
Design of Optimal Two-Dimensional FIR Filters with Quadrantally Symmetric Properties Using Vortex Search Algorithm

This research paper presents a new evolutionary technique named vortex search optimization (VSO) to design digital 2D finite impulse response (FIR) filter for improved performance both in pass-band and stop-band regions. Optimum filter coefficients are calculated by minimizing the deviation of actual frequency response from specified or desired response. Efficiency of the designed filter is measured by several parameters, such as maximum pass-band ripple, maximum stop-band ripple, mean attenuation in stop band and time taken, to execute the code. Analysis of the performance of designed filter is correlated with various different algorithms like real coded genetic algorithm, particle swarm optimization, genetic search algorithm and hybrid particle swarm optimization gravitational algorithm. Comparative study shows significant reduction in pass-band error, stop-band error and execution time.
TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image systhesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant model based on the deformation-invariant CycleGAN (DicycleGAN) architecture and the spatial transformation network (STN) using thin-plate-spline (TPS). The proposed method can be trained with unpaired and unaligned data, and generate synthesised images aligned with the source data. Robustness to the presence of relative deformations between data from the source and target domain has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
Joint extraction of entities and relations by a novel end-to-end model with a double-pointer module

Joint extraction of entities and relations is to detect entities and recognize semantic relations simultaneously. However, some existing joint models predict relations on words, instead of entities. These models cannot make full use of the entity information when predicting relations, which will affect relation extraction. We propose an end-to-end model with a double-pointer module that can jointly extract whole entities and relations. The double-pointer module is combined with multiple decoders to predict the start and end positions of the entity in the input sentence. In addition, in order to learn the relevance between long-distance entities effectively, the multi-layer convolution and self-attention mechanism are used as an encoder, instead of Bi-RNN. We conduct experiments on two public datasets and our models outperform the baseline methods significantly.