Reviewerly/exHarmony huggingface dataset:
Optimizing Features Quality: A Normalized Covariance Fusion Framework for Skeleton Action Recognition

Action recognition based on 3D skeleton sequences has gained considerable attention in recent years. Due to effectively representing the spatial and the temporal characters of skeleton sequences, the Covariance Matrix (CM) features combined with the Long Short-Term Memory (LSTM) network is an effective and reasonable roadmap to enhance the action recognition accuracy. However, the CM features in the existing recognition models are computed from the raw data without normalization or with static normalization. Moreover, a CM feature is calculated from all coordinates in one frame, treating all coordinates in three axes identically and neglecting the relationship of the coordinates in the same axe. In this paper, an end to end deep learning framework is proposed that includes a normalization layer dynamically adapting to data distribution and inference procedure. After normalization, the three covariance feature sequences from the coordinates in three axes are produced from the sliding windows and are fused into one fusion matrix using a convolution layer. Finally, the fusion matrix is sequentially fed into an LSTM network to recognize skeleton action. The novelty of the proposed framework is combining the adaptive preprocessing and the features fusion to the LSTM network and improving the recognition accuracy by optimizing the quality of the features rather than network construction. In the experiments, the proposed framework is verified on the public datasets and one student action dataset collected from a real classroom. The experimental results demonstrate that the proposed method achieves a significant improvement in accuracy compared to the state-of-the-art methods. It can be concluded that the proposed framework can not only accurately capture the correlation of joints in the same frame but can also effectively express the dependences of sequential frames.
Path Planning of UAV Delivery Based on Improved APF-RRT* Algorithm

Abstract The application demand for UAV in logistics distribution is vast and its accurate and rapid path planning research has practical value. Nowadays, the RRT algorithm is one of the most popular path planning methods for UAV delivery. Although the search of RRT is fast, the planned path is generally not the optimal path. The improved RRT* algorithm improves the way, but the convergence time is long. In this paper, we proposed an improved 3d path planning method based on RRT* based on APF. We introduced gravitation and repulsion in the APF algorithm based on the RRT* algorithm in this paper, and then we added the random point generated sphere to carry out random sampling of local space. Finally, we used MATLAB to compare the simulation results of the three algorithms and verify that the improved 3d path planning algorithm is improved in the aspects of path optimization and operation time.
Automatic brain extraction from MRI of human head scans using Helmholtz free energy principle and morphological operations

In this article, two novel methods are proposed to detect the brain boundary in a magnetic resonance image (MRI) by making an analogy between Helmholtz free energy (HFE) principle in thermodynamics and the intensity of pixels in an image. In the first method, an intensity transformation equation is derived using HFE. This transformation is applied to pixels in each slice of the MRI of human head and is used to detect boundaries of all objects present in it. To identify the brain portion among these objects, a sequence of two dimensional (2D) morphological operations and the largest connected component (LCC) analysis are done. These operations produced a mask for the brain using which the brain is extracted from the T1-W MRI slices. However, the LCC process failed in some cases to identify the brain region correctly. To address this issue, a second method which makes use of information in the adjacent slices is developed. This extended scheme performed well in extracting the brain portion from each slice of a MRI volume. Experiments are conducted by applying this schemes on 38 volumes of MRI of human head scans collected from the Internet Brain Segmentation Repository (IBSR). The proposed methods produced competitive or better results than the existing popular methods Brain Extraction Tool (BET), Brain Surface Extractor (BSE) and other similar recent methods.
Image retrieval via topic modelling of Instagram hashtags

The following topics are dealt with: social networking (online); learning (artificial intelligence); Internet; text analysis; computer aided instruction; pattern classification; history; graph theory; mobile computing; smart phones.
A Proposed Access Control-Based Privacy Preservation Model to Share Healthcare Data in Cloud

Healthcare data in cloud computing facilitates the treatment of patients efficiently by sharing information about personal health data between the healthcare providers for medical consultation. Furthermore, retaining the confidentiality of data and patients' identity is a another challenging task. This paper presents the concept of an access control-based (AC) privacy preservation model for the mutual authentication of users and data owners in the proposed digital system. The proposed model offers a high-security guarantee and high efficiency. The proposed digital system consists of four different entities, user, data owner, cloud server, and key generation center (KGC). This approach makes the system more robust and highly secure, which has been verified with multiple scenarios. Besides, the proposed model consisted of the setup phase, key generation phase, encryption phase, validation phase, access control phase, and data sharing phase. The setup phases are run by the data owner, which takes input as a security parameter and generates the system master key and security parameter. Then, in the key generation phase, the private key is generated by KGC and is stored in the cloud server. After that, the generated private key is encrypted. Then, the session key is generated by KGC and granted to the user and cloud server for storing, and then, the results are verified in the validation phase using validation messages. Finally, the data is shared with the user and decrypted at the user-end. The proposed model outperformPs other methods with a maximal genuine data rate of 0.91.
Characterizing multipartite entanglement classes via higher-dimensional embeddings

Witness operators are a central tool to detect entanglement or to distinguish among the different entanglement classes of multiparticle systems, which can be defined using stochastic local operations and classical communication (SLOCC). We show a one-to-one correspondence between general SLOCC witnesses and a class of entanglement witnesses in an extended Hilbert space. This relation can be used to derive SLOCC witnesses from criteria for full separability of quantum states; moreover, given SLOCC witness can be viewed as entanglement witnesses. As applications of this relation we discuss the calculation of overlaps between different SLOCC classes, and the SLOCC classification in $2\times 3\times 3$-dimensional systems.
Improving real-world vulnerability characterization with vulnerable slices

Vulnerability detection is an important challenge in the security community. Many different techniques have been proposed, ranging from symbolic execution to fuzzing in order to help in identifying vulnerabilities. Even though there has been considerable improvement in these approaches, they perform poorly on a large scale code basis. There has also been an alternate approach, where software metrics are calculated on the overall code structure with the hope of predicting code segments more likely to be vulnerable. The logic has been that more complex code with respect to the software metrics, will be more likely to contain vulnerabilities.
Self-error-rejecting quantum state transmission of entangled photons for faithful quantum communication without calibrated reference frames

We propose an alignment-free two-party polarization-entanglement transmission scheme for entangled photons by using only linear-optical elements, requiring neither ancillary photons nor calibrated reference frames. The scheme is robust against both the random channel noise and the instability of reference frames, and it is subsequently extended to multi-party Greenberger-Horne-Zeilinger state transmission. Furthermore, the success probabilities for two- and multi-party entanglement transmission are, in principle, improved to unity when active polarization controllers are used. The distinct characters of a simple structure, easy to be implemented, and a high fidelity and efficiency make our protocol very useful for long-distance quantum communications and distributed quantum networks in practical applications.
Implications of Programming Language Selection for Serverless Data Processing Pipelines

Serverless computing platforms have emerged offering software engineers an option for application hosting without the need to configure servers or manage scaling while guaranteeing high availability and fault tolerance. In the ideal scenario, a developer should be able to create a microservice, deploy it to a serverless platform, and never have to manage or configure anything; a truly serverless platform. The current implementation of serverless computing platforms is known as Function-as-a-Service or FaaS. Adoption of FaaS platforms, however, requires developers to address a major question- what programming language should functions be written in? To investigate this question, we implemented identical multi-function data processing pipelines in Java, Python, Go, and Node.js. Using these pipelines as a case study, we ran experiments tailored to investigate FaaS data processing performance. Specifically, we investigate data processing performance implications: for data payloads of varying size, with cold and warm serverless infrastructure, over scaling workloads, and when varying the available function memory. We found that Node.js had up to 94% slower runtime vs. Java for the same workload. In other scenarios, Java had 20% slower runtime than Go resulting from differences in how the cloud provider orchestrates infrastructure for each language with respect to the serverless freeze/thaw lifecycle. We found that no single language provided the best performance for every stage of a data processing pipeline and the fastest pipeline could be derived by combining a hybrid mix of languages to optimize performance.
A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities

Abstract Motivation Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology. Results In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces. Availability and implementation https://gitlab.com/agiannoula/diseasetrajectories. Supplementary information Supplementary data are available at Bioinformatics online.
Proposed Approach to Sensitive Association Rules Protection based on Compressing and Cryptography: (SARP-CC)

Privacy-preserving data mining is a modern technology that has been studied by many researchers. There are two types of Privacy-preserving association rules regarding the type of sensitive dataset to be protected, either protect the raw data before performing the data mining process or protect the sensitive knowledge extracted after performing the data mining process. In this research, we will suggest a technique called (SARP-CC) for privacy-preserving data mining. It will be used to protect sensitive knowledge (sensitive association rules) by changing the representation of knowledge from its known form to another, where it is more compressed and encrypted so that it can be sent over a network. The results show that this new compressed and encrypted form reduces storage space and transmission time and facilitates the transmission process over the network, and very beneficial especially for insecure networks. It can also be used as part of secure distributed association rules mining algorithms.
ReDro: Efficiently Learning Large-Sized SPD Visual Representation

Symmetric positive definite (SPD) matrix has recently been used as an effective visual representation. When learning this representation in deep networks, eigen-decomposition of covariance matrix is usually needed for a key step called matrix normalisation. This could result in significant computational cost, especially when facing the increasing number of channels in recent advanced deep networks. This work proposes a novel scheme called Relation Dropout (ReDro). It is inspired by the fact that eigen-decomposition of a block diagonal matrix can be efficiently obtained by decomposing each of its diagonal square matrices, which are of smaller sizes. Instead of using a full covariance matrix as in the literature, we generate a block diagonal one by randomly grouping the channels and only considering the covariance within the same group. We insert ReDro as an additional layer before the step of matrix normalisation and make its random grouping transparent to all subsequent layers. Additionally, we can view the ReDro scheme as a dropout-like regularisation, which drops the channel relationship across groups. As experimentally demonstrated, for the SPD methods typically involving the matrix normalisation step, ReDro can effectively help them reduce computational cost in learning large-sized SPD visual representation and also help to improve image recognition performance.
Unit of measurement libraries, their popularity and suitability

Abstract In scientific applications, physical quantities, and units of measurement are used regularly. If the inherent incompatibility between these units is not handled properly it can lead to potentially catastrophic problems. Although the risk of a miscalculation is high and the cost equally so, almost none of the major programming languages has support for physical quantities. We employed a systematic approach to examine and analyse available units of measurement (UoM) libraries. The search results were condensed into 38 libraries. These were the most comprehensive and well‐developed, open‐source libraries, chosen from approximately 3700 search results across seven repository hosting sites. Most libraries are implemented in a similar manner, but with varying features and evaluation strategies. Three developers and a scientist were interviewed and 91 practitioners of varying experiences from on‐line forums were surveyed to explain their impressions of UoM libraries and their suitability. Our findings show several reasons for nonadoption, including insufficient awareness of UoM libraries, cumbersome in practice, specific performance concerns, and usage of development processes that exclude unit information We conclude with recommendations to UoM library creators derived from these observations. We also argue that so long as units are not part of the language, or not supported through an IDE extension, their use will be limited. Native language support allows for efficient unit conversion and static checking. While lightweight methods provide many benefits of UoM libraries with minimal overheads. Libraries are perhaps best suited to applications in which unit of measurement checking is desirable at run‐time.
Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in CNNs

Convolutional neural networks (CNNs) are a powerful tool for pattern recognition and computer vision, but they do not scale well to higher-dimensional inputs, because of the associated memory demands for storing and manipulating high-dimensional tensors. This work starts from the observation that higher-dimensional data, like for example 3D voxel volumes, are sparsely populated. CNNs naturally lend themselves to densely sampled data, and sophisticated, massively parallel implementations are available. On the contrary, existing frameworks by and large lack the ability to efficiently process sparse data. Here, we introduce a suite of tools that exploit sparsity in both the feature maps and the filter weights of a CNN, and thereby allow for significantly lower memory footprints and computation times than the conventional dense framework, when processing data with a high degree of sparsity. Our scheme provides (i) an efficient GPU implementation of a convolution layer based on direct, sparse convolution, as well as sparse implementations of the ReLU and max-pooling layers; (ii) a filter step within the convolution layer, which we call attention, that prevents fill-in, i.e., the tendency of convolution to rapidly decrease sparsity, and guarantees an upper bound on the computational resources; and (iii) an adaptation of back-propagation that makes it possible to combine our approach with standard learning frameworks, while still benefitting from sparsity in the data as well as the model.
Character-Preserving Coherent Story Visualization

Story visualization aims at generating a sequence of images to narrate each sentence in a multi-sentence story. Different from video generation that focuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across different story pictures, which is very challenging since story sentences only provide sparse signals for generating images. Therefore, we propose a new framework named Character-Preserving Coherent Story Visualization (CP-CSV) to tackle the challenges. CP-CSV effectively learns to visualize the story by three critical modules: story and context encoder (story and sentence representation learning), figure-ground segmentation (auxiliary task to provide information for preserving character and story consistency), and figure-ground aware generation (image sequence generation by incorporating figure-ground information). Moreover, we propose a metric named Fréchet Story Distance (FSD) to evaluate the performance of story visualization. Extensive experiments demonstrate that CP-CSV maintains the details of character information and achieves high consistency among different frames, while FSD better measures the performance of story visualization.
Novel Postquantum MQ-Based Signature Scheme for Internet of Things With Parallel Implementation

Internet of Things (IoT) is a paradigm shifting technology that enables many innovative applications in the near future. Proactive measures are required to protect such architecture from cyber attacks. One of the most important security issues in this architecture is the authentication of edge nodes, which can be resolved through the deployment of digital signatures. However, existing standardized digital signatures are vulnerable to attacks from quantum computers, which can be unsafe in the near future. In this article, we propose a new signature scheme based on multivariate polynomials with efficient key and signature sizes, which is resistant to quantum computer attacks. The proposed scheme is also very friendly to parallel implementation, enabling efficient deployment of edge nodes authentication at high throughput. When implemented on a GPU device, the proposed scheme can generate 113 signatures/s and verify 120 signatures/s, which is 12.56× and 10.00× faster than a serial implementation in CPU.
Computational augmentation of neoplastic endometrial glands in digital pathology displays

Abstract The pathologic diagnosis of neoplasia requires localization and classification of lesional tissue, a process that depends on the recognition of an abnormal spatial distribution of neoplastic elements relative to admixed normal background tissue. In endometrial intraepithelial neoplasia (EIN), a pre‐cancer usually managed by hysterectomy, a clonally mutated proliferation of cytologically altered glands (‘neoplastic‐EIN’) aggregates in clusters that also contain background non‐neoplastic glands (‘background‐NL’). Here, we used image analysis to classify individual glands within endometrial tissue fragments as neoplastic‐EIN or background‐NL, and we used the distribution of predictions to localize foci diagnostic of EIN. Nuclear coordinates were automatically assigned and were used as vertices to generate Delaunay triangulations for each gland. Graph statistical variables were used to develop random forest algorithms to classify glands as neoplastic‐EIN or background‐NL. Individual glands in an independent validation set were scored by a ‘ground truth’ biomarker (PAX2 immunohistochemistry). We found that exclusion of small glands led to improvement in classification accuracy. Using an inclusion threshold of 200 nuclei per gland, our final model classification accuracy was 77.5% in the validation set, with a positive predictive value of 0.81. We leveraged this high positive predictive value in a point cloud overlay display to assist end‐user identification of EIN foci. This study demonstrates that graph theory approaches applied to small‐scale anatomic elements in the endometrium allow biologic classification by machine learning, and that spatial superimposition over large‐scale tissue expanses can have practical diagnostic utility. We expect this augmented diagnostic approach to be generalizable to commonly encountered problems in other organ systems. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley &amp; Sons, Ltd.
Machine Learning Based Bitcoin Address Classification

A bitcoin address is required for trading and maintaining pseudonymity for the owner. By exploiting this pseudonymity, various illegal activities are conducted around the world. To detect and deter illegal transactions, this paper proposes a method of identifying the characteristics of bitcoin addresses related to illegal transactions. We extracted 80 features from bitcoin transactions. Using machine-learning techniques, we successfully categorized addresses involved with illegal activities with a $$\sim $$ 84% accuracy. We also examined the address features most affecting classification performance and compared two machine-learning models. By applying the majority voting to the classification results of bitcoin addresses associated with a particular transaction, it will be possible to determine which category the transaction belongs to.
Preparing tunable Bell-diagonal states on a quantum computer

The class of two-qubit Bell-diagonal states has been the workhorse for developing understanding about the geometry, dynamics, and applications of quantum resources. In this article, we present a quantum circuit for preparing Bell-diagonal states on a quantum computer in a tunable way. We implement this quantum circuit using the IBM Q 5 Yorktown quantum computer, and as an application example, we measure the non-local, steering, entanglement, and discord quantum correlations and non-local quantum coherence of Werner states.