Reviewerly/exHarmony huggingface dataset:
Identification method of airport violence sorting based on deep learning

Airport baggage transportation is prone to violence. This paper proposes a deep learning-based airport violence sorting identification method to replace the traditional manual detection. The Retinanet target detection model is selected to realize the detection of people and luggage at the sorting site. We select live images for training and constructing them to form a dedicated data set. The methods of constructing a data set include data set selection, data enhancement, and data annotation. Then we use the selected model for training. At the same time, AP value is calculated by Precision and Recall as the result to verify the applicability of the deep learning model to our data set. Finally, the selected video is detected by the proposed method.The whole detecting process includes video preprocessing, preliminary detection, and detailed detection etc. In the process of tracking luggage, the color of the luggage is chosen as the main tracking feature. Based on the idea of meanshift. The final experimental results show that the proposed method is stable and efficient in detecting violent sorting behavior.
Learning Knowledge Embeddings with Prior Weights for Sparse Interaction Recommendation

Knowledge-based recommendation models have exhibited their excellent performance in recent years. Most of these models encode knowledge into item embeddings through a graph embedding algorithm, which are useful for uncovering correlations between users and items. However, the graph embedding algorithms in these models neglect the different weights of various relations between items (entities), thus imprecise embeddings are learned resulting in unsatisfactory recommendation results. To address this problem, we propose a deep knowledge-based recommendation model which incorporates a novel graph embedding algorithm with prior relation weights, to learn precise item embeddings. Specifically, an HIN is first constructed based on the entities and relations from open knowledge graphs (KGs). Then, the embeddings of item vertices in the HIN are learned through seeking similar items in terms of various attributes (relations) with different prior weights. Next, the user representations are learned through user-tag-item relationships, based on which recommendation results are obtained by a multi-layer perceptron (MLP) fed with user presentations and item representations (embeddings). All the embeddings learned in our model are regarded as knowledge embeddings. The extensive experiments show that, our model outperforms the previous KG-based recommendation models with help of precise knowledge embeddings. Furthermore, it owns robust performance in the scenario of sparse user-item interactions, since it captures user preferences mainly based on the knowledge rather than observed user-item interactions.
Multilevel Pipelined Processing for Aerial Image Restoration

Aerial images can get distorted due to various factors. During the acquisition of the images, it may get corrupted by distinct types of noises, camera misalignment etc. The restoration of degraded images has been a very important factor in the fields of medical, surveillance etc. Many algorithms in image processing do exist to restore degraded images but the efficiency of those in restoring the images is less. In this work, the aerial images are processed through various stages such as color stabilization, saturation enrichment and contrast improvement. In color stabilization, the color intensities (green, red, and blue) are globally adjusted. In saturation enrichment, to increase the saturation of an input aerial image, color of each pixel is projected onto bisectors of an green, red and blue color cube. And in contrast improvement, it is used to enrich the image quality by using the principle of unsharp masking. Real world aerial images were used to obtain the restored images. This work is verified for various input aerial images and the obtained restored images are verified qualitatively and quantitatively.
A Novel Consensus Algorithm for Alliance Chain

Block-chain is a decentralized billing technique. It has the advantages of: no corrupted, enhanced security, distributed ledgers, having consensus, and faster settlement. There are lots of application using this technique to deal with various problems. There are three kinds of block-chain which are public blockchain, alliance block-chain, and private block-chain. From other researchers’ statements, we get that the note in the alliance chain need rules to limit write permissions. Alliance chain can provide security management functions, and these functions can meet the management between the members, certification, authorization, monitoring, auditing. So the consensus algorithm is the main technical implementation of the alliance chain. This paper mainly analyzes some requirements realization which applies to the alliance chain. It mainly includes the following parts: local data sharing, work order decentralizing transaction consensus, and regulatory compliance.
Poster: Determining a Network and Pedagogical Efficient Approach to Learning in Disruptive Environments

Context The peculiar technological challenges of developing countries have impaired E-learning. Poor supply of bandwidth for E-learning due to cost has led to delayed response times across the internet. Technical knowledge in developing E-learning systems does not suffice for an understanding in pedagogical approach to effective learning. Purpose or Goal To leverage the ubiquitous nature of mobile devices, which has overcome the challenges of E-learning on traditional desktop computing environments, this research developed an intelligent mobile learning system (IMLS) using multi-agents. Ubiquitous learning (u-learning) provides the learner the freedom from learning environments, learning devices and learning content format and rather emphasize on the constructivist and behaviourist learning processes and cognitive development among learners. The ubiquitous learning environment (ULE) evolves more context awareness to provide most adaptive content for learners. M-learning application based on mobile devices fits into the u-learning paradigm. Approach A multi-agent framework known as the Java Agent Development Environment (JADE) was employed in the development of the M-learning system. Three agents were deployed; authentication, tutorial, and the assessment agents, respectively. For performance evaluation, the study also developed a similar non-agent system, fed with the same database and both monitored with network monitoring tools. The study also evaluated different pedagogical approaches through online survey in order to gain insight into users’ preferred learning style. Actual or Anticipated Outcomes Results of the response times of the IMLS when compared with a non-agent counterpart showed the IMLS being five times faster. The results from the survey suggested that majority of the respondents were split between constructivist and behaviorist approaches of learning. This result is significant as the multi-agents drastically reduce bandwidth demand, thereby reducing cost and at the same time ensuring that throughput was maintained.
Adhesive Handwritten Digit Recognition Algorithm Based on Improved Convolutional Neural Network

Traditional machine learning algorithms are susceptible to many factors in the recognition of handwritten adhesion numbers, such as different people's digital writing habits, different degrees of adhesion, and low image quality, which could lead to lower digital recognition accuracy. To solve these problems, an improved convolutional neural network algorithm for handwritten adhesion digital recognition is proposed in this paper. First, an improved convolutional neural network model is provided for the large number of adhesion in handwritten digital pictures. Multilevel feature extraction is performed on the experimental images using convolution kernels of different scales, and then, the feature frame filtering algorithm is optimized to improve the recognition accuracy of handwritten adhesion numbers while enhancing the robustness of the neural network to background noise. The experimental results show that the average recognition accuracy of the improved convolution model on the experimental data set is 94%. The proposed algorithm reduces the parameter size with ensuring high recognition accuracy, and improves the recognition efficiency of the system, which is better than most state-of-the-art algorithms.
An Explainable Statistical Learning Algorithm to Support Data Fusion

This paper presents a statistical learning algorithm called the relevance vector machine that is currently under development to support data fusion applications. The algorithm is applicable to classification and regression problems and has been shown to be capable of learning complex, explainable behaviors in real engineering problems. This article summarizes construction of the learning algorithm and provides an example application to demonstrate some of the capabilities of the relevance vector machine with feature fusion. Finally, the possibilities are presented for using the relevance vector machine to support multi-modal data fusion by exploiting the statistically consistent outputs given by the model to extend binary label fusion to continuous label fusion.
MultiTLS: Secure Communication Channels with Cipher Suite Diversity

TLS ensures confidentiality, integrity, and authenticity of communications. However, design, implementation, and cryptographic vulnerabilities can make TLS communication channels insecure. We need mechanisms that allow the channels to be kept secure even when a new vulnerability is discovered. We present MultiTLS, a middleware based on diversity and tunneling mechanisms that allows keeping communication channels secure even when new vulnerabilities are discovered. MultiTLS creates a secure communication channel through the encapsulation of k TLS channels, where each one uses a different cipher suite. We evaluated the performance of MultiTLS and concluded that it has the advantage of being easy to use and maintain since it does not modify any of its dependencies.
The complexity of computing (almost) orthogonal matrices with ε-copies of the Fourier transform

The complexity of computing the Fourier transform is a longstanding open problem. Very recently, Ailon (2013, 2014, 2015) showed in a collection of papers that, roughly speaking, a speedup of the Fourier transform computation implies numerical ill-condition. The papers also quantify this tradeoff. The main method for proving these results is via a potential function called quasi-entropy, reminiscent of Shannon entropy. The quasi-entropy method opens new doors to understanding the computational complexity of the important Fourier transformation. However, it suffers from various obvious limitations. This paper is motivated by one such limitation, related to the computation of near-orthogonal matrices that have the Fourier transform ‘hidden’ in low-order bits. While partly overcoming this limitation, the paper sheds light on new interesting, open problems on the intersection of computational complexity and group theory. The paper also explains why this research direction, if fruitful, has a chance of solving much bigger questions about the complexity of the Fourier transform.
Variational Compensation Based Nonlinear Filter for Continuous-Discrete Stochastic Systems

In this paper, a novel variational compensation based nonlinear filter (VCNF) is proposed to cope with the nonlinear filtering problem in continuous-discrete systems. The core of VCNF is to construct a variational state compensation model with variational compensation parameters for accurately describing uncertain continuous state. The role of variational compensation parameters is to adaptively compensate the unpredictable approximation and discretization errors of system states. In the variational Bayesian framework, through iteratively and alternatively achieving the fitting of the state priori model and the compensation of approximation and discretization errors, estimation accuracy and adaptiveness can be enhanced gradually. The superior performance of VCNF is demonstrated in the simulation of target tracking.
A Novel Blockchain Network Structure Based on Logical Nodes

With the continuous maturity and evolution of blockchain, applying blockchain to edge computing has increasingly become a trend, and also brings many great challenges. For the traditional blockchain structure, each peer node of blockchain network stores exactly the same content, which leads to strong data redundancy and excessive storage capacity is wasted. In this paper, we propose a novel blockchain network structure to reduce the storage occupation for blockchain node, which combine multiple physical nodes into one logical node and try to ensure that the storage capacity between the logical nodes is similar. The experimental simulation results show that the proposed new blockchain network structure can effectively reduce the storage usage of physical nodes and decrease the difficulty to deploy blockchain to the edge network.
A Lightweight Framework for Fast Image Retrieval on Large-Scale Image Datasets

Content-based image retrieval (CBIR) has attracted substantial attention over the past decade. Instead of taking textual keywords as input, CBIR techniques directly take a visual query and try to return its similar images from a given database. In this paper, we introduce a lightweight framework to fast locate the images on large-scale datasets. To achieve this, we first group the images with the high similarity into one cluster by using the deep neural network. Then, we extract the eigenvalues and eigenvectors from the images. Based on the eigenvalues and eigenvectors, we propose CB-tree (clustering binary tree) to fast locate the image clusters. Compared with the baseline schemes, the proposed framework can enhance the searching speed by up to 33%.
Dashcam based wildlife detection and classification using fused data sets of digital photographic and simulated imagery

In this paper, data from a simulated data set were fused with a significantly smaller measured data set for the purpose of training a deep neural network to identify animals from the footage of a dash cam. The trained networks were used to detect wildlife in an environment similar to game reserves in South Africa. To enable the automatic collection of data for the experiment, a simulated environment was created to simulate four classes of wildlife found in South Africa: buffalo, elephants, rhino and zebra. The network structure for the detector network selected was an adapted version of the tiny YOLOv3 network. It was discovered that using transfer learning and fine tuning resulted in two models with higher accuracy of 82.59% and 86.64% mAP@0.5 respectively than models where no transfer learning was used. The results were achieved when tested on a testing set of digital photographic images. These networks, initialised using transfer learning, were also faster and easier to train than training using a combined data set of photographic and simulated images from scratch. The simulated environment can however not replace real-life data, as was proven by an accuracy of no better than chance for the model trained using only simulated data.
Automatic Matching of White Matter Hyperintensity Lesions in Longitudinal Brain Magnetic Resonance Imaging

Compared with elderly people with normal cognition, Alzheimer's disease (AD) type dementia patients have more extensive white matter hyperintensities (WMH) lesions, suggesting their role in the pathogenesis of dementia and neurocognitive dysfunction. In the process of the analysis, lesion markers and matches were primarily assessed by experts, which was time-consuming and limited the analysis of WMH or related symptoms for large number of whole brain images. In this study, we proposed a systematic procedure based on high order Zernike transform to align the images of one subject at different scan times. To estimate the rotation angle between the aligned images, we extract the keypoints and their corresponding features from the images based on Scale-Invariant Feature Transform (SIFT) to. Then, the matching white matter hyperintensities in the pre and post stages is performed after all the post images are rotated and translated to the coordinates of the first scan image.
Enhancement of MRI Brain Images with Histogram Equalization Techniques

In modern days, in digital image processing, the computerized sector needs an effective, low noise MRI picture and enhances image contrast. The main method verified and looked at multiple enrichment methods based on Histogram. Equalization of magnetic resonance imaging examines and determines on basis of MRI. In relation to calculating the parameter metrics of the histogram -techniques. Image advancement is a technique for adjusting or changing the picture to make it increasingly suitable for specific applications and is utilized for enhancing or improving differentiation proportions and picture brilliance, expelling commotion from the picture and making recognizing simpler. Magnetic resonance imaging (MRI) is a fantastic medicinal innovation that offers better data about human brain-soft tissue, stroke and numerous other illnesses. Doctors dissect the disease effectively through MRI. MRI has a little difference proportion to improve the differentiation of MRI. Picture we use Histogram-Equalization procedures. In that LHE, CLAHE, HE and AHE techniques are correlated.
Key-Aggregate Proxy Re-Encryption with Dynamic Condition Generation Using Multilinear Map

With the growing accessibility of the internet, many people store files in the cloud storage. In consideration of privacy preserving, users tent to encrypt their files before uploading them to the cloud. Under certain conditions, a file owner may want to share the encrypted files directly on the cloud. In such cases, the file owner will delegate the re-encryption ability to a proxy with re-encryption keys. However, Chen et al. found that the size of the re-encryption key (or condition key) would increase with the number of the conditions, which motivated them in proposing the concept of key-aggregate proxy re-encryption. Unfortunately, we found that the conditions cannot be changed arbitrarily once we set the conditions at the beginning. To solve the problem, we apply the multilinear maps to the key-aggregate proxy re-encryption to extend the number of conditions to be exponentially large, while keeping the size of public parameters small and practical.
A Novel Method for Enhanced Image Based Reversible Data Hiding

Reversible data hiding (RDH) technology has been widely used due to its function on copyright protection and content integrity authentication. However, most conventional RDH schemes focus on its performance, i.e., embedding capacity and distortion, instead of its security. It means those RDH schemes could be easily inspect by most steganalysis methods, i.e., SPAM [44], to show some suspicious secret data hidden in it. Thus, the practicability of RDH is limited. In this paper, we creatively choose enhanced image as carrier to develop a secure RDH framework. On one hand, it is found that the statistical property of enhanced image is suit for reversible data hiding and thus achieve desired performance. On the other hand, enhanced image as carrier could provide more embedding space for security improvement. Thus, some security improvement skills from steganography field, such as multi-feature sorting, local clustering and so on, are introduced into above framework. Experimental results show the superiority of proposed scheme in the aspect of high performance and security level compared with other related algorithms.
IWPT 2020 Shared Task Data and System Outputs

This package contains data used in the IWPT 2020 shared task. It contains training, development and test (evaluation) datasets. The data is based on a subset of Universal Dependencies release 2.5 (http://hdl.handle.net/11234/1-3105) but some treebanks contain additional enhanced annotations. Moreover, not all of these additions became part of Universal Dependencies release 2.6 (http://hdl.handle.net/11234/1-3226), which makes the shared task data unique and worth a separate release to enable later comparison with new parsing algorithms. The package also contains a number of Perl and Python scripts that have been used to process the data during preparation and during the shared task. Finally, the package includes the official primary submission of each team participating in the shared task.
Performance Prediction for Power-Capped Applications based on Machine Learning Algorithms

Growing high performance computing (HPC) clusters are encountering a power wall due to limitations in the surrounding infrastructure. Maximizing a cluster's performance in the presence of a limited power budget is an open problem with high relevance and requires a deep understanding of application performance and power draw.Hardware components with the same technical specification have distinct power efficiencies and applications running on those components have diverse power profiles. Enforcing a power limit on individual components changes the performance characteristics. In this work, we investigate and quantity power- and performance-characteristics of various applications. Further, we present a systematic methodology to collect corresponding monitoring data and apply machine learning (ML) techniques to predict the performance under particular power caps. The observed prediction error is under 3% in most cases, which is in the same range of performance variation as application runs without a power cap.
Thai Word Segmentation

Thai sentences consist of many words that are written consecutively with complicated rules that make word segmentation to be an arduous task. Thai word segmentation has been researched since the 1980s. Beginning with defining the scope of syllables, then developing techniques and algorithms to word segmentation using various methods that divided into three types which are Rules-based technique, Dictionary-based technique, and Learning-based technique. Details of these algorithms will be presented in the following sections.
A new ‐map mechanism for mobility traces privacy

Summary A major concern of the deployment of location based services (LBSs) is the safeguards of the user's location data collected by service providers, since personal location data may imply sensitive private information. However, most available works proposed so far rely on syntactic privacy models such as k ‐anonymity and location perturbation, which are proved to quiver in the balance with privacy and usability requirements. In this article, we provide a new ‐map mechanism to help users better understand the privacy/accuracy tradeoff process and preserve location data. In our ‐map model, the user can specify a geographic region to hide her precise location and suppress the following queries in the same area to meet her privacy requirement, while maintaining and understanding its usability. In addition, we propose a new notion of ‐privacy based on differential privacy to account for the temporal‐spatial correlation and history correlation in the case of crossing region, which is the major privacy concern of a moving user's trace. Finally, we evaluate our framework by using an online LBS with real‐world data sets. The results not only indicate that the ‐map is significantly useful for identifying the privacy and utility tradeoffs but also show the effectiveness and practicality of the proposed ‐privacy.