Reviewerly/exHarmony huggingface dataset:
Selective A<i>n</i>DE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI)

Attribute selection has been proved to be an effective trick to strengthen the classification capability of Bayesian network classifiers, such as Averaged n-Dependence Estimators (AnDE). However, conventional mutual information-based attribute ranking considers only the correlation between the attribute and the class, regardless of the redundancies among the attributes. In this paper, we propose a new ranking approach, called Maximin Conditional Mutual Information (MMCMI), which first minimises the conditional mutual information for any unsorted attribute with regard to the sorted attribute sequence, and then maximise the minimal conditional mutual information within all unsorted attributes. When ranking the very first attribute, the mutual information with the class is maximised within all attributes. Extensive empirical results demonstrate that the MMCMI ranking approach together with attribute selection framework achieves significantly superior classification performance and less classification time with respect to regular AnDE and the mutual information counterparts.
An Improved Multibranch Convolutional Neural Network with a Compensator for Crowd Counting

Image-based crowd counting has extremely important applications in public safety issues. Most of the previous studies focused on extremely dense crowds. However, as the number of webcams increases, a crowd with extremely high density can obtain less error by summing the images of multiple close-range webcams, but there are still some problems such as heavy occlusions and large-scale variation. To solve the above problems, this paper proposes a new type of multibranch neural network with a compensator, in which features are extracted through multibranch subnetworks of different scales. The weights between the branches are adjusted by the compensator, and the captured features are distinguished among different branches. To avoid learning nearly the same features in each branch and reducing the training deviation, the dataset is labeled with head scale, and the adaptive grading loss function is used to calculate the estimated loss of the subregions. The experimental results show that the accuracy of the network proposed in this paper is about 10% higher than that of the comparison network.
Quantum Cooperative Multicast in a Quantum Hybrid Topology Network

Quantum multicast is a significant transmission mode in a multiparty communication scenario. Multisource collaboration can further enhance the efficient multicast. However, it remains a challenge to realize quantum multicast with a cooperative way in a complex topology network. In this article, we propose a scheme of quantum cooperative multicast in a hybrid topology network. It provides information aggregation and simultaneous multipoint transmission services. First, collaborative information aggregation allows central network data to be integrated into the aggregation node. By exploiting the quantum multicast mode, the aggregation node can simultaneously deliver integrated quantum states to multiple targets. Second, our scheme is feasible for dynamic network expansion. It is capable of extending the network architecture iteratively, while the peer network requests can be handled in parallel. Finally, the new scheme shows great application potential in the distributed quantum network. It is a promising candidate for the implementation of quantum data disaster backup in future.
Research on Blockchain Technology and Media Industry Applications in the Context of Big Data

With the everyday use of big data technology, using big data resources effectively has become a key area of research. Media consumption creates enormous amounts of data in people’s digital lives. This data is not only very valuable to the company, but it also presents a number of concerns, including privacy breaches and data exploitation. Along with the continuous improvement of blockchain technology, the rational use of big data resources has become possible. Blockchain technology has expanded from the initial Bitcoin to the financial field and has become more widely used in the legal and media industries. This study catches the actual cases of blockchain technology in the media industry through Qingbo Big Data, analyzes the current global blockchain and media industry’s overall development status and the existing problems, and puts forward corresponding countermeasures to accelerate the integration and development of blockchain and media industry in the context of big data. The results show that the technical features of blockchain can solve the painful problems of copyright risk, algorithmic black box, and weak trust mechanism in the media industry, and the combination of the two can form a new burst of media productivity. The article discusses the broad application prospects of blockchain in the media industry, which is essential for big data and blockchain research. This paper composes the pioneering literature on the blockchain, catches the information on blockchain by keywords, analyzes the application prospect of blockchain in the media industry, and promotes the research and application of blockchain technology.
The Design of Intelligent Tutoring Systems Using College Students’ Innovation and Entrepreneurship Education under the Background of Online Teaching

Internet-based computer online teaching, as a new teaching method, has attracted extensive attention with the rapid development of Internet technology. The purpose is to explore the new education mode of college students’ innovation and entrepreneurship under the background of online teaching and to design and study the intelligent tutoring system (ITS). The prototype of the teaching model is constructed at the beginning. The study employed a probability model for analyzing tutoring system and entrepreneurship education in the context of online teaching. In the later stage, the main body, platform, and links in the teaching process and the connection and aggregation among them are reexamined by integrating teaching practice. The study results revealed that the proposed algorithm helps in identifying the importance and perceptions of ITS and entrepreneurship education.
Few-shot object detection based on positive-sample improvement

Traditional object detectors based on deep learning rely on plenty of labeled samples, which are expensive to obtain. Few-shot object detection (FSOD) attempts to solve this problem, learning detection objects from a few labeled samples, but the performance is often unsatisfactory due to the scarcity of samples. We believe that the main reasons that restrict the performance of few-shot detectors are: (1) the positive samples is scarce, and (2) the quality of positive samples is low. Therefore, we put forward a novel few-shot object detector based on YOLOv4, starting from both improving the quantity and quality of positive samples. First, we design a hybrid multivariate positive sample augmentation (HMPSA) module to amplify the quantity of positive samples and increase positive sample diversity while suppressing negative samples. Then, we design a selective non-local fusion attention (SNFA) module to help the detector better learn the target features and improve the feature quality of positive samples. Finally, we optimize the loss function to make it more suitable for the task of FSOD. Experimental results on PASCAL VOC and MS COCO demonstrate that our designed few-shot object detector has competitive performance with other state-of-the-art detectors.
Hierarchical Planning With Annotated Skeleton Guidance

We present a hierarchical skeleton-guided motion planning algorithm to guide mobile robots. A good skeleton maps the connectivity of the subspace of c-space containing significant degrees of freedom and is able to guide the planner to find the desired solutions fast. However, sometimes the skeleton does not closely represent the free c-space, which often misleads current skeleton-guided planners. The hierarchical skeleton-guided planning strategy gradually relaxes its reliance on the workspace skeleton as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {C}_{space}$</tex-math></inline-formula> is sampled, thereby incrementally returning a sub-optimal path, a feature that is not guaranteed in the standard skeleton-guided algorithm. Experimental comparisons to the standard skeleton guided planners and other lazy planning strategies show significant improvement in roadmap construction run time while maintaining path quality for multi-query problems in cluttered environments.
Partially Concatenated Calderbank-Shor-Steane Codes Achieving the Quantum Gilbert-Varshamov Bound Asymptotically

In this paper, we utilize a concatenation scheme to construct new families of quantum error correction codes achieving the quantum Gilbert-Varshamov (GV) bound asymptotically. We concatenate alternant codes with any linear code achieving the classical GV bound to construct Calderbank-Shor-Steane (CSS) codes. We show that the concatenated code can achieve the quantum GV bound asymptotically and can approach the Hashing bound for asymmetric Pauli channels. By combing Steane's enlargement construction of CSS codes, we derive a family of enlarged stabilizer codes achieving the quantum GV bound for enlarged CSS codes asymptotically. As applications, we derive two families of fast encodable and decodable CSS codes with parameters $\mathscr{Q}_1=[[N,\Omega(\sqrt{N}),\Omega( \sqrt{N})]],$ and $\mathscr{Q}_2=[[N,\Omega(N/\log N),\Omega(N/\log N)/\Omega(\log N)]].$ We show that $\mathscr{Q}_1$ can be encoded very efficiently by circuits of size $O(N)$ and depth $O(\sqrt{N})$. For an input error syndrome, $\mathscr{Q}_1$ can correct any adversarial error of weight up to half the minimum distance bound in $O(N)$ time. $\mathscr{Q}_1$ can also be decoded in parallel in $O(\sqrt{N})$ time by using $O(\sqrt{N})$ classical processors. For an input error syndrome, we proved that $\mathscr{Q}_2$ can correct a linear number of ${X}$-errors with high probability and an almost linear number of ${Z}$-errors in $O(N )$ time. Moreover, $\mathscr{Q}_2$ can be decoded in parallel in $O(\log(N))$ time by using $O(N)$ classical processors.
Computationally Efficient Auto-Weighted Aggregation for Heterogeneous Federated Learning

Federated Learning (FL) offers a privacy-preserving massively distributed Machine Learning (ML) paradigm where many clients cooperatively work together towards training a shared machine learning model. FL, however, is susceptible to data heterogeneity problems as the FL clients have diverse data sources. Prior works employ auto-weighted model aggregation to mitigate the heterogeneity issue to minimize the impact of unfavorable model updates. However, existing approaches require extensive computation for statistical analysis of clients’ model updates. To circumvent this, we propose, FedASL (Federated Learning with Auto-weighted Aggregation based on Standard Deviation of Training Loss) which uses only the local training loss of FL clients for auto-weighting the model aggregation. Our evaluation under three different datasets and various data corruption scenarios reveals that FedASL can effectively thwart data corruption from bad clients while causing as little as one-tenth of the computation cost of existing approaches.
A Smart Contract Approach to Cyber Threat Intelligence Sharing in Kenya

The current practice of informal cyber threat intelligence (CTI) sharing among organizations is characterized with the use of emails and social media exchanges among individuals. This model is highly subjective and dependent on a specific individual’s social networks. Financial institutions have CTI at their disposal that could protect each other from computer hacks and fraud. The effective sharing of this intelligence among financial institutions could reduce the high income leakages that is brought about by cyber-attacks. The challenge is how to share this intelligence confidentially and anonymously since the financial institutions are competitors, have a huge reputation to protect and thrive on business secrecy. This research proposes a new way of sharing cyber threat intelligence by using Ethereum smart contract blockchain technology. This will be accomplished by hashing the device identity and replacing the hashed device identity by an on-chain verifiable random function to protect the identity of the participating nodes or the financial institutions in the blockchain network when passing information.
Research on Cross-Platform Image Recommendation Model Fusing Text Information

The user data from different types of network platforms are often presented in different modalities, such as text, image, or audio. Many researches have shown that fusing the data information displayed by users on different platforms can better reflect the interest characteristics of users. Hence, this paper proposes a cross-platform image recommendation model (FITIFCIR), which fuses text and image information to achieve cross-platform data recommendation. Furthermore, it realizes the semantic information fusion of text and image, so as to recommend the images collected by users on the image sharing platform to the text to be published. Compared with baseline image recommendation models, the experimental results indicate that the FITIFCIR outperforms baseline models. The proposed model is effective to recommend appropriate images for users to better illustrate their ideas.
WooIR

In this work we presentWooIR, an open realistic benchmark for Page Stream Segmentation (PSS), the task of recovering document boundaries from aggregatedstreams of pages. Our dataset consists of over 200 streams of scanned in documents, 7K documents, 45K pages and 10M words, originating from documents released by the Dutch government in response to requests made under the Freedom of Information Act. Apart from the introduction of the dataset we perform several baseline experiments on the dataset and compare six metrics for the PSS task, in an attempt to unify the field in the usage of evaluation metrics more suited to the task. Analysis of the six metrics on the WooIR dataset shows that the dataset contains a good balance of easy and hard samples. The Panoptic Quality metric from the image segmentation field seems the most appropriate evaluation metric for the PSS task.
Underwater Imaging Formation Model-Embedded Multiscale Deep Neural Network for Underwater Image Enhancement

Underwater image enhancement (UIE) plays an essential role in improving the quality of raw images captured in an underwater environment. Existing UIE methods can be categorized into two types: handcraft-designed and deep learning-based methods. Generally, the handcraft-designed methods are more explainable due to the leverage of knowledge-based image priors, while the deep learning-based methods are usually criticized for their weak interpretability. In this study, we address this issue by integrating the merits of both handcraft-designed and deep learning-based methods. Specifically, a physical underwater imaging model-inspired deep CNN for UIE is designed. Instead of estimating a global background light magnitude and a transmission matrix separately in traditional image restoration-based UIE methods, we directly generate a single variable as the joint estimation of these two parameters within a deep CNN and directly recover the enhanced image as an output according to a reformulated physical underwater imaging model. The whole network is trained in an end-to-end manner and more importantly has good interpretability. The proposed method has been validated for the UIE task on a real-world underwater image dataset and the experimental results well demonstrate the superiority of our method over the existing ones for UIE.
Formal Constraints for Structured Document Retrieval

The formalization of retrieval constraints for traditional (atomic) retrieval was a major milestone in information retrieval (IR) research. The aim of these constraints was to formalize IR heuristics which most retrieval models rely upon. In a similar fashion, this paper introduces constraints for structured document retrieval (SDR). Out of the many possible constraints, we focus on three that are shown to produce intuitive rankings in simple, but informative retrieval scenarios. It is shown that none of the widely used SDR models (BM25F, MLM, linear score aggregation) satisfy all three constraints. The underlying reason for this is shown to be the failure of existing models to balance between assuming independence of term occurrences across fields and considering the documents as atomic, rather than structured. The constraints introduced in this paper, together with the analysis of how they are satisfied by existing models, can be used to analytically reason about the behaviour of any SDR model in a variety of ranking scenarios.
BCubed Revisited: Elements Like Me

BCubed is a mathematically clean, elegant and intuitively well behaved external performance metric for clustering tasks. BCubed compares a predicted clustering to a known ground truth through elementwise precision and recall scores. For each element, the predicted and ground truth clusters containing the element are compared, and the mean over all elements is taken. We argue that BCubed overestimates performance, for the intuitive reason that the clustering gets credit for putting an element in its own cluster. This is repaired, and we investigate the repaired version, called "Elements Like Me (ELM)". We extensively evaluate ELM and conclude that it retains all positive properties of BCubed and gives a minimum 0 zero score when it should.
SigGAN: Adversarial Model for Learning Signed Relationships in Networks

Signed link prediction in graphs is an important problem that has applications in diverse domains. It is a binary classification problem that predicts whether an edge between a pair of nodes is positive or negative. Existing approaches for link prediction in unsigned networks cannot be directly applied for signed link prediction due to their inherent differences. Furthermore, signed link prediction must consider the inherent characteristics of signed networks, such as structural balance theory. Recent signed link prediction approaches generate node representations using either generative models or discriminative models. Inspired by the recent success of Generative Adversarial Network (GAN) based models in several applications, we propose a GAN based model for signed networks, SigGAN. It considers the inherent characteristics of signed networks, such as integration of information from negative edges, high imbalance in number of positive and negative edges, and structural balance theory. Comparing the performance with state-of-the-art techniques on five real-world datasets validates the effectiveness of SigGAN.
Renal Ultrasound Image Segmentation Based on U-Net and Generative Adversarial Nets

Ultrasound images are quite useful for doctors to diagnose the kidney diseases nowadays. Since the U-Net network was proposed, it has been applied in the field of ultrasonic image segmentation. We propose a method that combine the U-Net with the generic advantageous networks, creating a more efficient approach of the segmentation of renal ultrasound images. By introducing GAN's supervision mechanism, the segmented network has a better performance. We constructed a training set of 200 renal ultrasound images and a test set composed of 40 images, and carried out experiments on them. Experiments show that compared with the traditional U-Net, the method proposed in this paper improves the accuracy on the same test set. The accuracy of our network reaches 97.83%, which is higher than 97.00% of U-Net.
Rejection Sampling from Arbitrary Multivariate Distributions Using Generalized Fibonacci Lattices

We present a quasi-Monte Carlo acceptance-rejection sampling method for arbitrary multivariate continuous probability density functions. The method employs either a uni-form or a Gaussian proposal distribution. The proposal samples are provided by optimal deterministic sampling based on the generalized Fibonacci lattice. By using low-discrepancy samples from generalized Fibonacci lattices, we achieve a more locally homogeneous sample distribution than random sampling meth-ods for arbitrary continuous densities such as the Metropolis-Hastings algorithm or slice sampling, or acceptance-rejection based on state-of-the-art quasi-random sampling methods like the Sobol or Halton sequence.
Accumulated State Densities Filter for Better Separability of Group-Targets

In some defence applications, it is required to identify targets separated by a certain distance as group-targets. This allows the system to use a suitable tracking and mitigation strategy for a group different from what is used for a point-target. A natural choice to identify a group of this type is the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The DBSCAN algorithm uses the available track information to identify the groups/clusters. Information on these tracks are, in the vast majority of tracking systems, based on the Kalman filter estimate. In this work, we present a scenario where the out-group target is inseparable from the group-target using a Kalman filter. Thereafter, we show that the separability could be significantly improved using the estimates of the joint probability density of the kinematic target states accumulated over a certain time window, up to the present time, given the time series of all sensor data. These densities are known as Accumulated State Densities (ASDs).
Note on Autocorrelation of the Residuals of the NCV Kalman Filter Tracking a Maneuvering Target - Part 2

When a target is not maneuvering, the residuals from the nearly constant velocity (NCV) Kalman filter are inde-pendent, zero mean, and Gaussian. When the target maneuvers, the residuals are no longer independent, unbiased or Gaussian. A chi-squared (χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) test is a common method to detect maneuvering targets, however, the correlation between consecutive residuals is lost by the calculation of the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> statistic. In this work, two methods that utilize the correlation between consecutive residuals are explored for the purpose of detecting weak maneuvers. First, a recursion is developed to compute the full cross correlation for an arbitrary number of residuals under the hypothesis that the target is maneuvering. This result is compared in a hypothesis test to the null hypothesis that the target is not maneuvering. Second, an arbitrary size window of residual errors is used to form a least squares estimate of the bias (i.e. acceleration), and the significance of that estimate is tested against the corresponding covariance. Both methods are shown to be an improvement over a standard χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> test.