Reviewerly/exHarmony huggingface dataset:
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning

Solving complex real-world tasks, e.g., autonomous fleet control, often involves a coordinated team of multiple agents which learn strategies from visual inputs via reinforcement learning. Many existing multi-agent reinforcement learning (MARL) algorithms however don’t scale to environments where agents operate on visual inputs. To address this issue, algorithmically, recent works have focused on non-stationarity and exploration. In contrast, we study whether scalability can also be achieved via a disentangled representation. For this, we explicitly construct an object-centric intermediate representation to characterize the states of an environment, which we refer to as ‘semantic tracklets.’ We evaluate ‘semantic tracklets’ on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment. ‘Semantic tracklets’ consistently outperform baselines on VMPE, and achieve a +2.4 higher score difference than baselines on GFootball. Notably, this method is the first to successfully learn a strategy for five players in the GFootball environment using only visual data. For more, please see our project page: https://ioujenliu.github.io/SemanticTracklets
Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts

Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achieved by 'black-box' policies in simulated domains. When deploying RL to the real world, several concerns regarding the use of a 'black-box' policy might be raised. In order to make the learned policies more transparent, we propose in this paper a policy iteration scheme that retains a complex function approximator for its internal value predictions but constrains the policy to have a concise, hierarchical, and human-readable structure, based on a mixture of interpretable experts. Each expert selects a primitive action according to a distance to a prototypical state. A key design decision to keep such experts interpretable is to select the prototypical states from trajectory data. The main technical contribution of the paper is to address the challenges introduced by this non-differentiable prototypical state selection procedure. Experimentally, we show that our proposed algorithm can learn compelling policies on continuous action deep RL benchmarks, matching the performance of neural network based policies, but returning policies that are more amenable to human inspection than neural network or linear-in-feature policies.
Examining the ultraviolet optical screening tool as a viable means for delineating a contaminated organic sediment

Rapid and accurate delineation of contaminated sediments in marine environments is critical for the effective assessment of site risks and the development of appropriate remedial action plans. In this study, a new application of the ultraviolet optical screening tool (UVOST) equipped with electrical conductivity measurement (UVOST-EC) is proposed to delineate a water-covered sediment contaminated with dioxins and furans in a decommissioned pulp and paper wastewater stabilization basin. Bench scale experiments are presented that were used to develop a UVOST-EC interpretation method for delineating between two different sediment types present in the basin: an anthropogenically derived organic rich contaminated sediment ("black sediment") and a naturally occurring grey organic silt sediment with marine provenance ("grey sediment"). The method involves comparative analysis of fluorescence and electrical conductivity signatures between the two sediments. Results indicate that each sediment type presents unique "signatures" related to fluorescence and electrical signals which corresponds to variability in their physio-chemical structure. Almost 100 UVOST-EC tests performed at the study site were paired with ex situ physical gravity core measurements of the black sediment to test the accuracy of the UVOST-EC-based method. A statistical analysis at seven sample "cluster" sites (i.e. multiple sub-samples within a defined area) indicated that the mean of sediment thickness obtained by the UVOST-EC measurement technique at a given site were not significantly different (p = 0.05) from measurements derived from sediment gravity core measurements. The UVOST-EC-based sediment thickness delineation method reliably determined the thickness of the dioxin and furan contaminated sediments as compared to gravity core determination for the sediment in this study. Application of this approach to other studies should be assessed in a similar manner. The UVOST-EC method offers health and safety, cost, logistics, and data interpretation benefits.
Art Image Processing and Color Objective Evaluation Based on Multicolor Space Convolutional Neural Network

A convolutional neural network's weight sharing feature can significantly reduce the cumbersome degree of the network structure and reduce the number of weights that need to be trained. The model can directly input the original image, without the process of feature extraction and data reconstruction in common classification algorithms. This kind of network structure has got a good performance in image processing and recognition. Based on the color objective evaluation method of the convolutional neural network, this paper proposes a convolutional neural network model based on multicolor space and builds a convolutional neural network based on VGGNet (Visual Geometry Group Net) in three different color spaces, namely, RGB (Red Green Blue), LAB (Luminosity a b), and HSV (Hue Saturation Value) color spaces. We carry out research on data input processing and model output selection and perform feature extraction and prediction of color images. After a model output selection judger, the prediction results of different color spaces are merged and the final prediction category is output. This article starts with the multidimensional correlation for visual art image processing and color objective evaluation. Considering the relationship between the evolution of artistic painting style and the color of artistic images, this article explores the characteristics of artistic image dimensions. In view of different factors, corresponding knowledge extraction strategies are designed to generate color label distribution, provide supplementary information of art history for input images, and train the model on a multitask learning framework. In this paper, experiments on multiple art painting data sets prove that this method is superior to single-color label classification methods.
Improving neural network’s performance using Bayesian inference

Neural networks can be used as a data-driven model for system identification. But the probability properties of the training data are not included. The Bayesian approach can model the input and output probability distribution, but it cannot give points estimation. In this paper, we propose a special neural model, which combines the neural model and Bayesian inference. The probability distributions of input and output are obtained by the Bayesian approach. This statistical model changes the neural network's structure and improve the accuracy of the neural modeling. We also propose a neural network training method using Bayesian inference. The approach capabilities are analyzed. We use three examples to compare our method with the other black box methods. The results show that this new model is much better, when there are large noises, and the dynamics of the system is complex.
Statistical Fault Analysis of the Simeck Lightweight Cipher in the Ubiquitous Sensor Networks

With the development of wireless technology, the ubiquitous sensor networks have a profound effect on the way human interacts with computers, devices and environment. In order to reduce the potentially serious risks in the interaction, applying lightweight ciphers is effective to balance security, efficiency and convenience. Simeck is such a lightweight cipher that provides data confidentiality, authentication and integrity. It is significant to explore whether Simeck remains robust security. Up to now, the attacking assumptions of the previous security analysis of Simeck focus on the known-plaintext attack and the chosen-plaintext attack. There is no literature about Simeck against the ciphertext-only attack, which represents the weakest attacking capability of the attackers. On the assumption of the ciphertext-only attack, this paper proposes the security analysis of Simeck against the statistical fault analysis with a series of novel distinguishers of KDE, MME and MME-GF. The experimental results show that the proposed distinguishers can recover the secret key of Simeck in both decreasing faults and increasing reliability and accuracy. Thus, Simeck cannot resist against the statistical fault analysis with the proposed distinguishers. Furthermore, the good performance of these novel distinguishers can be applied on the PRESENT lightweight cipher. It offers the valuable reference for the design and analysis of the lightweight ciphers in the ubiquitous sensor networks.
Auto-encoder neural network based prediction of Texas poker opponent's behavior

Imperfect information game is the difficulty in the game field. In the study of imperfect information games, an accurate and effective opponent modeling method is essential. Texas Hold 'em is a typical game with imperfect information. The main form of opponent modeling is to predict the opponent's behavior. This article takes Texas Hold 'em as the specific research object and proposes an opponent's behavior prediction method based on an auto-encoding neural network. We use the International Computer Poker Tournament historical data as experimental data to model Texas Hold 'em poker players' behavior and train an auto-encoding neural network model that can be applied to different stages of the game. In the article, the auto-encoding network model's structure and parameters are experimentally studied, and the quantized conjugate gradient convergence algorithm (SCG) and softmax classifier are used. Experimental test results show that this method can accurately predict the opponent's behavior information in a short time, which will provide precious help for the decision-making of the Texas Hold 'em agent.
A hinge-loss based codebook transfer for cross-domain recommendation with non-overlapping data

Recommender systems, especially collaborative filtering (CF) based recommender systems, has been playing an important role in many e-commerce applications. As the information being searched over the internet is rapidly increasing, users often face the difficulty of finding items of his/her own interest and recommender systems often provides help in such tasks. Recent studies show that, as the item space increases, and the number of items rated by the users become very less, issues like sparsity arise. To mitigate the sparsity problem, transfer learning techniques are being used wherein the data from dense domain (source) is considered in order to predict the missing entries in the sparse domain (target). In this paper, we propose a novel transfer learning approach called Transfer of Codebook via Hinge loss or TCH for cross-domain recommendation when both domains have no overlap of users and items. In our approach constructing the codebook and transferring the same knowledge from source to target domain is done in a novel way. We employ a similar formulation of co-clustering technique to obtain the codebook (cluster-level rating pattern) of source domain. By making use of hinge loss function we transfer the learnt codebook of the source domain to target. The use of hinge loss as a loss function is novel and has not been tried before in transfer learning. We demonstrate that our technique improves the approximation of the target matrix on benchmark datasets.
Automatic brain extraction and hemisphere segmentation in rat brain MR images after stroke using deformable models

Experimental ischemic stroke models play an essential role in understanding the mechanisms of cerebral ischemia and evaluating the development of pathological extent. An important precursor to the investigation of ischemic strokes associated with rodents is the brain extraction and hemisphere segmentation in rat brain diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) images. Accurate and reliable image segmentation tools for extracting the rat brain and hemispheres in the MR images are critical in subsequent processes, such as lesion identification and injury analysis. This study is an attempt to investigate rat brain extraction and hemisphere segmentation algorithms that are practicable in both DWI and T2WI images.To automatically perform brain extraction, the proposed framework is based on an efficient geometric deformable model. By introducing an additional image force in response to the rat brain characteristics into the skull stripping model, we establish a unique rat brain extraction scheme in DWI and T2WI images. For the subsequent hemisphere segmentation, we develop an efficient brain feature detection algorithm to approximately separate the rat brain. A refinement process is enforced by constructing a gradient vector flow in the proximity of the midsurface, where a parametric active contour is attracted to achieve hemisphere segmentation.Extensive experiments with 55 DWI and T2WI subjects were executed in comparison with the state-of-the-art methods. Experimental results indicated that our rat brain extraction and hemisphere segmentation schemes outperformed the competitive methods and exhibited high performance both qualitatively and quantitatively. For rat brain extraction, the average Dice scores were 97.13% and 97.42% in DWI and T2WI image volumes, respectively. Rat hemisphere segmentation results based on the Hausdorff distance metric revealed average values of 0.17 and 0.15 mm for DWI and T2WI subjects, respectively.We believe that the established frameworks are advantageous to facilitate preclinical stroke investigation and relevant neuroscience research that requires accurate brain extraction and hemisphere segmentation using rat DWI and T2WI images.
Extended cyclic codes, maximal arcs and ovoids

We show that extended cyclic codes over $$\mathbb {F}_q$$ with parameters $$[q+2,3,q]$$ , $$q=2^m$$ , determine regular hyperovals. We also show that extended cyclic codes with parameters $$[qt-q+t,3,qt-q]$$ , $$1<t<q$$ , q is a power of t, determine (cyclic) Denniston maximal arcs. Similarly, cyclic codes with parameters $$[q^2+1,4,q^2-q]$$ are equivalent to ovoid codes obtained from elliptic quadrics in PG(3, q). Finally, we give simple presentations of Denniston maximal arcs in PG(2, q) and elliptic quadrics in PG(3, q).
Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification

• Taxonomy of labels helps classification when training data are limited. • The label taxonomy can be leveraged in multi-task and transfer learning setting. • Combining multi-task and transfer learning improves classification accuracy. • Top levels of the International Patent Classification help patent classification. When labels are organized into a meaningful taxonomy, the parent-child relationship between labels at different levels can give the classifier additional information not deducible from the data alone, especially with limited training data. As a case study, we illustrate this effect on the task of patent classification—the task of categorizing patent documents based on their technical content. Existing approaches do not take into consideration this additional information. Experiments on two patent classification datasets, WIPO-alpha and USPTO-2M, show that our regularized Gated Recurrent Unit (GRU) architecture already gives a performance improvement with a micro-averaged precision score using the top prediction of 0.5191 and 0.5740 on the two datasets, respectively. However, knowledge transfer along the label hierarchy gives further significant improvement on WIPO-alpha, raising the score to 0.5376, and a small improvement on USPTO-2M to 0.5743. Our analyses reveal that incorporating label information improves performance on classes with fewer examples and makes model robust to errors that result from predicting closely related labels.
S4: Self-Supervised Learning of Spatiotemporal Similarity

We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.
Gender recognition using thermal images from UAV

Gender recognition is one of the issues that computer vision deals with. It is useful for analysing human behaviour, intelligent tracking, or human-robot interaction. The aim of this paper is to recognise the gender of people in outdoor areas, where it is very difficult or impossible to guard all access roads to the place, even in poor lighting conditions or in the dark. In this paper, a model will be designed and tested using a controlled UAV flight, during which images of people were obtained. The sensor is a thermal camera located on the UAV, which is not dependent on ambient lighting, and deep learning methods are used for subsequent image processing and classification. These are convolutional neural networks (AlexNet, GoogLeNet), which will be used to solve binary classification. Optimized networks achieve classification accuracy of 81.6 %% (GoogLeNet) and 82.3% (AlexNet). A freely available database [21] was used to learn CNNs, and a self-created database (images obtained with a thermal camera attached to a UAV) was used to test the networks.
Comprehensive Evaluation of Power Grid Planning in Coastal Regions of China in the Context of Power Internet of Things (PIoT)

Ai, X. and Zhao, L., 2021. Comprehensive evaluation of power grid planning in coastal regions of China in the context of power internet of things (PIoT). Journal of Coastal Research, 37(4), 761–770. Coconut Creek (Florida), ISSN 0749-0208.With the proposal and promotion of the concept "New Infrastructure Construction" in China, the construction of Power Internet of Things (PIoT) has become one major task of China's power grid development. Coastal regions have the characteristics of developed economy, high electricity demand, and variable climate, which are the focus of power grid construction. Hence, the construction of PIoT has put forward new requirements for power grid planning in coastal regions, and a comprehensive evaluation of power grid planning must be performed. However, the existing power grid evaluation index system cannot meet the comprehensive requirements of PIoT. Moreover, the current comprehensive evaluation method cannot solve problems on similar correlations and cannot perform dynamic prediction. This study proposes a comprehensive evaluation method for power grid planning in coastal regions in the context of PIoT. This method uses the requirements for developing PIoT as a criterion in constructing a seven-dimensional index system with 30 indexes. It also adopts the matter element analysis model, which can obtain more accurate correlations and change the evaluation method from static to dynamic. Taking a coastal province as an example, this paper performs comprehensive evaluation of current situation, development trend, and weak links of power grids and has satisfactory evaluation performance and prediction effect. It also proved that this method can clarify key points for improving and establishing development policies and provides guiding significance for power grid planning in coastal regions.
Quality-Of-Experience Evaluation of 8K Ultra-High-Definition Television

When evaluating extremely high-quality videos that reach maximum human perceptual limits, the conventional perceptual-level evaluation methods may seem insufficient. Hence, a paradigm shift in the evaluation methods is required. In this study, we propose a cognitive-level evaluation method for evaluating high-quality videos using impression strength as an index for the quality of experience (QoE) of an 8K ultrahigh-definition television (UHDTV). Based on the evaluation scheme, we quantitatively demonstrated the difference in the quality of high-quality images close to the perceptual limit of human vision, and the results validated the hypothesis of viewers experiencing strong psychological effects when watching 8K videos. In addition, the strength of higher-order impressions such as freshness and deliciousness change with changes in the signal bandwidth and luminance level, indicating that the QoE of a UHDTV can be modulated using signal processing techniques.
Improving Chinese Word Representation Using Four Corners Features

Intuitively, word representation for logographic languages like Chinese can be enhanced by its internal characteristics. Several research endeavors tried to learn Chinese word embeddings with characters, radicals, or subcharacters containing rich semantic information. In this paper, motivated by Four-Corner Method for Character Indexation, we extract features from four corners of characters with important morphological charactertics. Based on the features from four corners, we propose a model to utilize characters and four corner features of words to capture both semantic and morphological information. Moreover, we apply an attention scheme to integrate internal information dynamically, which includes two strategies to assign different weights for elements according to the word frequency. Experimental results on social news corpus and Chinese Wikipedia Dump show exploiting the four corner morphological features is crucial for capturing the meanings of Chinese words. Meanwhile, the results on word analogy, word similarity, and text classification tasks demonstrate that our approach obtains better results than state-of-the-art approaches.
Truss Decomposition on Large Probabilistic Networks using H-Index

Truss decomposition is a popular approach for discovering cohesive subgraphs. However, truss decomposition on probabilistic graphs is challenging. State-of-the-art either do not scale to large graphs or use approximation techniques to achieve scalability. We present an exact and scalable algorithm for truss decomposition of probabilistic graphs. The algorithm is based on progressive tightening of the estimate of the truss value of each edge based on h-index computation and novel use of dynamic programming. Our proposed algorithm (1) is significantly faster than state-of-the-art and scales to much larger graphs, (2) is progressive by allowing the user to see near-results along the way, (3) does not sacrifice the exactness of final result, and (4) achieves all these while processing only an edge and its immediate neighbors at a time, thus resulting in smaller memory footprint. Our extensive experimental results confirm the scalability and efficiency of our algorithm.