Reviewerly/exHarmony huggingface dataset:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint

In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multimodality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constrain block, a feature fusion block, and a decoding path. The model independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the linear correlation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.
Using a Combination of LiDAR, RADAR, and Image Data for 3D Object Detection in Autonomous Vehicles

One of the topics that is highly regarded and researched in the field of artificial intelligence and machine learning is object detection. Its use is especially important in autonomous vehicles. The various methods used to detect objects are based on different types of data, including image, radar, and lidar. Using a point clouds is one of the new methods for 3D object detection proposed in some recent work. One of the recently presented efficient methods is PointPillars network. It is an encoder that can learn from data available in a point cloud and then organize it as a representation in vertical columns (pillars). This representation can be used for 3D object detection. in this work, we try to develop a high performance model for 3D object detection based on PointPillars network exploiting a combination of lidar, radar, and image data to be used for autonomous vehicles perception. We use lidar, radar, and image data in nuScenes dataset to predict 3D boxes for three classes of objects that are car, pedestrian, and bus. To measure and compare results, we use nuScenes detection score (NDS) that is a combined metric for detection task. Results show that increasing the number of lidar sweeps, and combining them with radar and image data, significantly improve the performance of the 3D object detector. We suggest a method to combine different types of input data (lidar, radar, image) using a weighting system that can be used as the input for the encoder.
Selecting a JVM Garbage Collector for Big Data and Cloud Services

Memory management is responsible for allocating and releasing the memory used by applications (e.g., in the Java Virtual Machine-JVM). There are several garbage collectors (GCs) each designed to target different performance metrics, making it very hard for developers to decide which GC to use for a particular application. We start with a review of existing GC algorithms. Then, we intend to evaluate throughput, pause time, and memory usage in existing JVM GCs using benchmark suites like DaCapo and Renaissance. The goal is to find the trade-offs between the above mentioned performance metrics to have a better understanding of which GC helps fulfilling certain application requirements.
Cross-domain access control based on trusted third-party and attribute mapping center

With the development of informatization, smart city has become a new concept for the next generation of urbanization globally. In the face of intensive data requests and constrained resources, it is particularly important to design a safe and energy-efficient computing system.In this paper, we propose a multi-domain access control scheme suitable for smart city construction. We divide the huge equipment group into multiple domains for parallel distributed management to improve data processing efficiency. Based on attribute mapping, we show a safe and efficient cross-domain access control method. We introduce a trusted third-party (TTP) and an attribute mapping center (MC), using attribute certificates, digital mapping tables, B+ tree index, and buffer to improve mapping efficiency. In the complex one-to-many data transmission process, the outsourced ciphertext-policy attribute-based encryption (CP-ABE) is used to provide users with personalized services while reducing the computing burden. Finally, through analysis and simulation, it is found that this scheme has higher safety and efficiency.
Algebraic geometry codes over abelian surfaces containing no absolutely irreducible curves of low genus

We provide a theoretical study of Algebraic Geometry codes constructed from abelian surfaces defined over finite fields. We give a general bound on their minimum distance and we investigate how this estimation can be sharpened under the assumption that the abelian surface does not contain low genus curves. This approach naturally leads us to consider Weil restrictions of elliptic curves and abelian surfaces which do not admit a principal polarization.
CQNN: a CGRA-based QNN Framework

Quantized Neural Networks (QNNs) have drawn tremendous attention since - when compared with Convolution Neural Networks (CNNs) - they often dramatically reduce computation, communication, and storage demands with negligible loss in accuracy. To find an optimal balance between performance and accuracy, developers use different data-widths for different layers and channels. Given this large parameter space, it is challenging to design a QNN accelerator which is generally efficient for various and flexible model configurations. In this paper we propose CQNN, a novel Coarse-Grained Reconfigurable Architecture-based (CGRA) QNN acceleration framework. CQNN has a large number of basic components for binary functions. By programming CQNN at runtime according to the target QNN model, these basic components are integrated to support QNN functions with any data-width and hyperparameter requirements. The result is an optimal QNN for the target model. The framework includes compiler, hardware design, simulator, and RTL generator. Experimental results show CQNNs can complete the inference of AlexNet and VGG-16 within 0.13ms and 2.63ms, respectively. We demonstrate the design on an FPGA platform; however, this is only for showcasing the method: the approach does not rely on any FPGA-specific features and can thus be implemented as ASIC as well.
Trajectory Outlier Detection Based on DBSCAN and Velocity Entropy

Pedestrian trajectory outlier detection is of great significance in the field of video surveillance and public safety. However, due to various behavior patterns of pedestrians and complex video scenes, the trajectories obtained by real-time tracking algorithm are often complicated and accompanied by missing data. In this paper, a trajectory outlier detection algorithm based on DBSCAN and velocity entropy is proposed to detect outliers from real-time tracking trajectories. On the one hand, an improved DBSCAN algorithm is used to detect abnormal sub-trajectories, which identifies trajectory outliers with local features. On the other hand, by comparing the velocity entropy of the trajectories, trajectory outliers are detected from the overall perspective. The concept of trajectory confidence is proposed to evaluate the reliability of the results of trajectory outlier detection, thereby further reducing the false detection rate and improving accuracy. Finally, an experiment is carried out and shows that our method has a better performance than TRACLUS.
Short text classification based on bidirectional TCN and attention mechanism

Abstract The context-related semantic information of the text in the traditional short text classification algorithms are not fully captured, a text classification model based on bidirectional temporal convolutional network and attention mechanism (BTCA) was proposed. Multi-layer dilated convolution was used to increase the receptive field and better capture long distance dependent information. At the same time, attention mechanism was used to increase the attention to the local key feature in the text, and the bidirectional temporal convolution network was used to extract contextual multi-scale semantic information to enrich semantics, the problem of sparse short text features was solved to a certain extent, and text classification effect was improved. The public corpus of the THUCNews was used to conduct a comparative experiment. It is pointed out that effect of short text classification is improved by using BTCA model, with an accuracy rate of 91.47%, which is better than commonly used models.
NIR image colorization with graph-convolutional neural networks

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.
What Can We Learn from Noun Substitutions in Revision Histories?

In community-edited resources such as wikiHow, sentences are subject to revisions on a daily basis.Recent work has shown that resulting improvements over time can be modelled computationally, assuming that each revision contributes to the improvement.We take a closer look at a subset of such revisions, for which we attempt to improve a computational model and validate in how far the assumption that 'revised means better' actually holds.The subset of revisions considered here are noun substitutions, which often involve interesting semantic relations, including synonymy, antonymy and hypernymy.Despite the high semantic relatedness, we find that a supervised classifier can distinguish the revised version of a sentence from an original version with an accuracy close to 70%, when taking context into account.In a human annotation study, we observe that annotators identify the revised sentence as the 'better version' with similar performance.Our analysis reveals a fair agreement among annotators when a revision improves fluency.In contrast, noun substitutions that involve common lexical-semantic relationships are often perceived as being equally good or tend to cause disagreements.While these findings are also reflected in classification scores, a comparison of results shows that our model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity.
Robust twin extreme learning machines with correntropy-based metric

In this paper, we propose a novel distance metric based on correntropy and kernel learning. Some properties of the proposed metric are demonstrated such as nonnegativity, non-convexity, boundedness and approximation behaviors. The proposed metric includes and extends classical metrics such as L0-norm and L1-norm metrics. Moreover, we develop a fraction loss function satisfying the Bayes rule, and demonstrate its robustness from the perspective of M-estimation. With the proposed robust metric and loss function, a new robust twin extreme learning machines framework (called LCFTELM) is presented to reduce the negative effect of noises and outliers. The proposed LCFTELM retains the advantages of twin extreme machines (TELM) and promotes the robustness. However, the non-convexity of the proposed model makes it difficult to optimize. Furthermore, an effective iterative algorithm for LCFTELM is designed, we present theoretical analysis on the convergence of the proposed algorithm. Following that, we evaluate the proposed algorithm on real-world datasets and artificial dataset under different noise settings. Experimental results show that the proposed method achieves better generalization than the state-of-the-art methods in most cases, which demonstrates the feasibility and robustness of the proposed LCFTELM.
Hawkes binomial topic model with applications to coupled conflict-Twitter data

We consider the problem of modeling and clustering heterogeneous event data arising from coupled conflict event and social media data sets.In this setting conflict events trigger responses on social media, and, at the same time, signals of grievance detected in social media may serve as leading indicators for subsequent conflict events.For this purpose we introduce the Hawkes Binomial Topic Model (HBTM) where marks, Tweets and conflict event descriptions are represented as bags of words following a Binomial distribution.When viewed as a branching process, the daughter event bag of words is generated by randomly turning on/off parent words through independent Bernoulli random variables.We then use expectation-maximization to estimate the model parameters and branching structure of the process.The inferred branching structure is then used for topic cascade detection, short-term forecasting, and investigating the causal dependence of grievance on social media and conflict events in recent elections in Nigeria and Kenya.
Attributing and Detecting Fake Images Generated by Known GANs

The quality of GAN-generated fake images has improved significantly, and recent GAN approaches, such as StyleGAN, achieve near indistinguishability from real images for the naked eye.As a result, adversaries are attracted to using GAN-generated fake images for disinformation campaigns and fraud on social networks.However, training an image generation network to produce realistic-looking samples remains a timeconsuming and difficult problem, so adversaries are more likely to use published GAN models to generate fake images.In this paper, we analyze the frequency domain to attribute and detect fake images generated by a known GAN model.We derive a similarity metric on the frequency domain and develop a new approach for GAN image attribution.We conduct experiments on four trained GAN models and two real image datasets.Our results show high attribution accuracy against real images and those from other GAN models.We further analyze our method under evasion attempts and find the frequency-based approach is comparatively robust.
Empirical autopsy of deep video captioning encoder-decoder architecture

Contemporary deep learning based video captioning methods adopt encoder-decoder framework. In encoder, visual features are extracted with 2D/3D Convolutional Neural Networks (CNNs) and a transformed version of those features is passed to the decoder. The decoder uses word embeddings and a language model to map visual features to natural language captions. Due to its composite nature, the encoder-decoder pipeline provides the freedom of multiple choices for each of its components, e.g., the choices of CNN models, feature transformations, word embeddings, and language models etc. Component selection can have drastic effects on the overall video captioning performance. However, current literature is void of any systematic investigation in this regard. This article fills this gap by providing the first thorough empirical analysis of the role that each major component plays in a widely adopted video captioning pipeline. We perform extensive experiments by varying the constituent components of the video captioning framework, and quantify the performance gains that are possible by mere component selection. We use the popular MSVD dataset as the test-bed, and demonstrate that substantial performance gains are possible by careful selection of the constituent components without major changes to the pipeline itself. These results are expected to provide guiding principles for research in the fast growing direction of video captioning.
A New Modified Red Deer Algorithm for Multi-level Image Thresholding

This paper presents a modified evolution strategy based meta-heuristic, named Modified Red Deer Algorithm (MRDA), which can be effectively and methodically applied to solve single-objective optimization problems. Recently, the actions of red deers have been analysed during their breading time, that in turn inspired the researchers to develop a popular meta-heuristic, called Red Deer Algorithm (RDA). The RDA has been designed to deal with different combinatorial optimization problems in a variety of real-life applications. This paper introduces few adaptive approaches to modify the inherent operators and parameters of RDA to enhance its efficacy. As a comparative study, the performance of MRDA has been evaluated with RDA and Classical Genetic Algorithm (CGA) by utilizing some real-life gray-scale images. At the outset, the results of these competitive algorithms have been assessed with respect to optimum fitness, worst fitness, average fitness, standard deviation, convergence time at best case and average convergence time at three distinct level of thresholding for each test image. Finally, t-test and Friedman Test have been conducted among themselves to check out the superiority. This comparative analysis establishes that MRDA outperforms others in all facets and furnish exceedingly competitive results.
A Lightweight AEAD encryption core to secure IoT applications

The Internet of Things (IoT) with the advancements of many technologies opens a wide range of new applications such as smart appliances, smart cities and smart grids. Despite its popularity and usability, it also creates a new attack surface for the hackers especially on highly constrained devices which have limited memory footprints and processing power. These constrained devices often use Authenticated Encryption with Associated Data (AEAD) to secure data stored in the devices and transmitted over the network. In this work, we design a lightweight data encryption core in hardware with the support for AEAD to secure IoT applications on highly constrained devices. The design achieves a low area cost with only 23kGEs in TSMC 65nm technology and an encryption throughput of 123Mbps at 60MHz.
Analysis and Synthesis of the Modified MRAC-MIT System and the MRAC-Lyapunov System

The analysis and synthesis of automatic control systems of a thermal object with reference adaptive control models based on the modified MIT rule and the Lyapunov rule using MATLAB-Simulink software is carried out. The developed algorithms have successfully passed laboratory tests on a thermal object and provide the required quality of the transition process in a thermal object.
Compact Certificates of Collective Knowledge

We introduce compact certificate schemes, which allow any party to take a large number of signatures on a message M, by many signers of different weights, and compress them to a much shorter certificate. This certificate convinces the verifiers that signers with sufficient total weight signed M, even though the verifier will not see—let alone verify—all of the signatures. Thus, for example, a compact certificate can be used to prove that parties who jointly have a sufficient total account balance have attested to a given block in a blockchain.After defining compact certificates, we demonstrate an effi-cient compact certificate scheme. We then show how to implement such a scheme in a decentralized setting over an unreliable network and in the presence of adversarial parties who wish to disrupt certificate creation. Our evaluation shows that compact certificates are 50–280× smaller and 300–4000 cheaper to verify than a natural baseline approach.
Topic Detection and Tracking Based on Windowed DBSCAN and Parallel KNN

Topic Detection and Tracking technique (TDT) has been commonly used to identify the hot topics from the huge volume of Internet news information and keep up with the hot news. However, traditional topic detection and tracking methods have shown low accuracy and low efficiency. In this paper, a topic detection system driven by big data is built on the Spark platform, which aims at improving the efficiency of news collecting from the Internet and improving the accuracy and efficiency of topic detection and tracking tasks. This system can be easily employed in a distributed architecture and work as a parallelized news collecting and topic detection system. An improved density-based spatial clustering of application with noise (DBSCAN) clustering algorithm based on the time window is proposed to achieve accurate topic detection with the auxiliary advantage of reducing the time complexity. A parallel KNN based topic tracking algorithm is proposed for the topic tracking task. Experiments including comparison with some baseline algorithms and quantitative and qualitative analyses are conducted on pseudo-distributed Spark platform, which demonstrates the effectiveness and efficiency of the parallelized topic detection system.
Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors

In the past decade, many Bayesian shrinkage models have been developed for linear regression problems where the number of covariates, p, is large. Computation of the intractable posterior is often done with three-block Gibbs samplers (3BG), based on representing the shrinkage priors as scale mixtures of Normal distributions. An alternative computing tool is a state of the art Hamiltonian Monte Carlo (HMC) method, which can be easily implemented in the Stan software. However, we found both existing methods to be inefficient and often impractical for large p problems. Following the general idea of Rajaratnam et al., we propose two-block Gibbs samplers (2BG) for three commonly used shrinkage models, namely, the Bayesian group lasso, the Bayesian sparse group lasso, and the Bayesian fused lasso models. We demonstrate with simulated and real data examples that the Markov chains underlying 2BG’s converge much faster than that of 3BG’s, and no worse than that of HMC. At the same time, the computing costs of 2BG’s per iteration are as low as that of 3BG’s, and can be several orders of magnitude lower than that of HMC. As a result, the newly proposed 2BG is the only practical computing solution to do Bayesian shrinkage analysis for datasets with large p. Further, we provide theoretical justifications for the superior performance of 2BG’s. We establish geometric ergodicity of Markov chains associated with the 2BG for each of the three Bayesian shrinkage models. We also prove, for most cases of the Bayesian group lasso and the Bayesian sparse group lasso model, the Markov operators for the 2BG chains are trace-class. Whereas for all cases of all three Bayesian shrinkage models, the Markov operator for the 3BG chains is not even Hilbert–Schmidt. Supplementary materials for this article are available online.