Reviewerly/exHarmony huggingface dataset:
GRE: A GAT-Based Relation Embedding Model of Knowledge Graph for Recommendation

Compared with collaborative filtering, knowledge graph embedding based recommender systems greatly boost the information retrieval accuracy and solve the limitations of data sparsity and cold start of traditional collaborative filtering. In order to fully explore the relationship and structure information hidden in knowledge graphs, we propose the GAT-based Relation Embedding (GRE) model. In our model, we propose a Triple Set to denote a set of knowledge graph triples whose head entities are linked by items in interaction records, and a Triple Group to denote a group of knowledge graph triples extracted from Triple Set according to different relations. The proposed GRE is a neural model that aims at enriching user preference representation in recommender systems by utilizing Graph Attention Network (GAT) to aggregate the embeddings of adjacent tail entities to head entity over Triple Group and embedding the representation of relation in the process of polymerization of Triple Groups in Triple Set. By embedding relation information into each Triple Group representation and concatenating Triple Group representations in Triple Set, this proposed novel relation embedding method addresses the problem that GAT-based models only consider aggregating the neighboring entities and ignore the effect of relations in triples. Through extensive experimental comparisons with the baselines, we show that GRE has gained state-of-the-art performance in the majority of the cases on two open-source datasets.
Research on the Integration of Innovation Elements into the Digital Transformation of Higher Vocational Specialty under the Background of Digital Economy

Innovation’is a key word in the digital economy era. In recent years, innovation elements have become an important content of innovation theory research. Professional digital transformation is an innovative development activity of higher vocational colleges, which will inevitably integrate innovative elements and play its role. The digital transformation of specialty also reflects the requirement that specialty ADAPTS to the integrated development of industry. On the macro level, this paper explores the necessity and feasibility of digital transformation from the top-level design and group logic of professional groups. On the micro level, it studies the effective ways of professional digital transformation from the aspects of the revision of professional talent training scheme, curriculum system reform, teaching mode innovation, teacher team construction and so on.
CryoDRGN2: Ab Initio Neural Reconstruction of Dynamic Protein Complexes

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
PEMBINAAN PENINGKATAN KETERAMPILAN MENGGUNAKAN MICROSOFT OFFICE 2016 PADA MASYARAKAT SEKUPANG

Microsoft Office 2016 is one of the applications in data processing. Regarding community groups, it must be admitted that the community's ability to use Microsoft Office 2016 is minimal, because many people, especially students at the SMP Kampung Baru Indosat Sekupang Batam, do not understand and understand the use of Microsoft Office 2016 applications. The difficulties they experience are: school assignments related to questions in the form of essays related to the tools in Microsoft Office 2016 because the hours of computer subjects are less when compared to hours of other subjects. In addition, understanding of how the Microsoft Office 2016 application works and functions is still minimal. This is where the service team provides guidance in improving using Microsoft Office 2016 with the aim of improving knowledge and understanding skills for students using Microsoft Office 2016 applications.
Deep Learning-based Blind Denoising for Enhancing Energy-dispersive X-ray Spectroscopy (EDS) Images

Journal Article Deep Learning-based Blind Denoising for Enhancing Energy-dispersive X-ray Spectroscopy (EDS) Images Get access Jack Taylor, Jack Taylor Distributed Algorithms CDT, University of Liverpool, Liverpool, UK Search for other works by this author on: Oxford Academic Google Scholar Ke Chen, Ke Chen Department of Mathematical Sciences, University of Liverpool, Liverpool, UK Search for other works by this author on: Oxford Academic Google Scholar Yalin Zheng, Yalin Zheng Department of Eye and Vision Science, University of Liverpool, Liverpool, UK Search for other works by this author on: Oxford Academic Google Scholar Nigel D Browning Nigel D Browning Department of Mechanical, Materials, and Aerospace Engineering, University of Liverpool, Liverpool, UKPhysical and Computational Sciences, Pacific Northwest National Lab, Richland, WA, USASivananthan Laboratories, Inc., Bolingbrook, Illinois, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 508–509, https://doi.org/10.1017/S1431927622002665 Published: 01 August 2022
A Comparison of Quartz Source Determination Using Supervised Machine Learning verses a Traditional Ratio Approach

Journal Article A Comparison of Quartz Source Determination Using Supervised Machine Learning verses a Traditional Ratio Approach Get access Angela Halfpenny, Angela Halfpenny Department of Geological Sciences, Central Washington University (CWU), Ellensburg, WA, USA Corresponding author: angela.halfpenny@cwu.edu Search for other works by this author on: Oxford Academic Google Scholar Meaghan Wetherell, Meaghan Wetherell School of Information, University of Arizona, Tucson, Arizona, USA Search for other works by this author on: Oxford Academic Google Scholar Craig Scrivner Craig Scrivner Department of Geological Sciences, Central Washington University (CWU), Ellensburg, WA, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 552–553, https://doi.org/10.1017/S1431927622002793 Published: 01 August 2022
Research and Application of Anti-Counterfeiting Technology of Inspection and Test Report Based on Blockchain Technology

The product quality inspection report is the “final product” provided by the inspection and testing organization to customers. Due to the lack of effective anti-counterfeiting methods, forged reports appear from time to time in the market and show an increasing trend, resulting in inferior products being replaced by good ones, disturbing the market order, seriously endangering the safety of people's lives and property, hindering the normal development of inspection and testing institutions, and becoming a pain point in the industry. According to the management requirements of inspection and detection reports, combined with the unique characteristics of Blockchain technology, such as non-tampering, traceability and non-repudiation, this paper puts forward the anti-counterfeiting management technology of inspection and detection reports based on Blockchain technology, and constructs a transparent, trusted and traceable report query platform.
Research on Transformation Methods of Scientific and Technological Achievements from the Perspective of Innovation Ecology

The transformation of scientific and technological achievements has ushered in new opportunities and shown new characteristics. On the basis of analyzing the characteristics of innovation ecosystem, this paper puts forward the main mechanism of innovation ecosystem enabling the transformation of scientific and technological achievements, constructs the innovation ecological model of the transformation of scientific and technological achievements, defines the innovation subject and innovation environment of the transformation of scientific and technological achievements, and puts forward three core mechanisms of innovation ecosystem supporting the transformation of scientific and technological achievements. By constructing the colleges and universities, enterprises, science and technology intermediary, new research and development institutions, and other areas of innovation alliance, combined with the industry needs in the field of strategy will contend for layout construction of a batch of new research and development institutions, creating innovative synthesis as the core of innovation and improve cluster innovation environment, make the transformation of scientific and technological achievements from accidental sporadic events qualitative to batch convert "rainforest effect".
Filtering of X-Ray Images using Nonlinear Isotropic Diffusion

In this paper an optimization procedure for the number of iterations and two scaling parameters of the Perona-Malik filter is proposed. It is of general type having as aim variables the Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) of the filtered images. The method is applicable for various types of image content but is extensively tested on X-Ray chest images. The highest PSNR achieved is 35.77 dB and SSIM − 0.9622 when processing images with Additive White Gaussian Noise (AWGN) with a variance of 0.001. One of the aspects for further development of the algorithm is optimizing the time for filtering which turns out to be two orders of magnitude less than that of the Gaussian and the Average filters.
Analysis of barriers in implementation of blockchain technology in Indian industries

Blockchain technology could change the way that confidentiality, collaboration and ambiguity are perceived in this digitised world. These features have increased its potential for application in various sectors. However, this technology is still emerging and integrating it with the current system poses several challenges. There are numerous barriers that hinder the successful implementation of blockchain technology in Indian industries. Therefore, in this study a framework based on IF-TOPSIS methodology is developed for analysing various barriers in the implementation of blockchain technology. This work considers 14 such barriers that have been identified through extensive literature review and experts' opinions. Application of the proposed framework showed that 'poor economic behaviour', 'market-based risks' and 'cyber-attack risk' are the most relevant barriers for implementation of the said technology. The study provides an insight to the industries and government to enable proper and successful implementation of the blockchain technology in future industries of India.
An Image Hiding Method of Block Matching Way With Improved Texture Fusion Feature and High Embedding Capacity Product Code Over Z4

This paper presents a novel image hiding method with improved block matching way and high embedding capacity that allows the important image (<i>IM</i>) to be several times larger than the cover image. Multi-layered least-significant-bit (LSB) planes of the cover image (<i>CI</i>) are obtained for constructing the candidate block-matching set (CBMS) by using the features fusion which consists of the texture features of wavelet transform and gray level difference; By combining features way to search the best-matching blocks from the CBMS, and the Huffman coding is used to compress the data including the indices of the best-matching blocks, the not-well-matched blocks part of <i>IM</i> and the parameters into a secret information flow which is embedded into the double-layered bit planes of <i>CI</i> by using a high embedding rate encoder. Because the high embedding payload we need, the SPAM features will be chosen to test the performance of resisting steganalysis attack which can obviously improve the security of the stego images.
Quantum computation capability verification protocol for noisy intermediate-scale quantum devices with the dihedral coset problem

In this article, we propose an interactive protocol for one party (the verifier) holding a quantum computer to verify the quantum computation power of another party's (the prover) device via a one-way quantum channel. This protocol is referred to as the dihedral coset problem (DCP) challenge. The verifier needs to prepare quantum states encoding secrets (DCP samples) and send them to the prover. The prover is then tasked with recovering those secrets with a certain accuracy. Numerical simulation demonstrates that this accuracy is sensitive to errors in quantum hardware. Additionally, the DCP challenge serves as benchmarking protocol for locally fully connected (LFC) quantum architecture and aims to be performed on current and near-future quantum resources. We conduct a 4-qubit experiment on one of IBM Q devices.
Image Analysis for Detecting Malaria Cell Using Otsu Thresholding and Machine Learning Models

Motivation : Malaria is a dangerous disease that affects thousands of individuals each year all around the world. It can be fatal if not treated promptly. According to the most recent World Malaria Report from the World Health Organization, there would be 241 million malaria cases and 627 000 malaria deaths globally in 2020. Despite recent advances in malaria diagnosis, the microscopy approach remains the most widely used. Moreover, the efficiency of microscopic diagnosis is dependent on the expertise of the microscopist, which restricts malaria throughput. Distinguishing parasite development phases remains a very challenging task. Goal: The main aim is to develop a system to identify malaria stages in blood smears using machine learning models. This paper proposes a study of seven machine learning models and one ensemble model to foresee which model will better predict the malaria stage. Results: To avoid a large number of individuals from being infected with malaria, an early and precise diagnosis is essential. A web-based application is developed for the end-user using a flask, where the user can upload the sample images of the multi-stage malaria parasite and recognize the cell image. This will help the doctors to take the necessary steps to prevent the disease and choose the appropriate solution.
Analysing and Mitigating the Problem of Internet and Credit Card Fraud In Nigeria

Internet fraud connotes fraudulent activities committed via the internet. Technological advancement has increased reliance on the internet for communication and business transactions. This reliance has made internet users vulnerable to internet frauds and other forms of cybercrimes. This paper analyses several forms of internet fraud in Nigeria including credit card fraud. It pulled data related to internet fraud from governmental agencies to articulate the menace of internet fraud and the need for drastic mitigating measures. The study further reviews several approaches to detecting and preventing credit card fraud by financial institutions. It also analysed frameworks that have been put into place by the government to mitigate internet fraud generally. It discovered several challenges to the mitigation and prevention of internet fraud in Nigeria and made several contextual recommendations.
Cryptographic protocol for finding the meeting place of participants with confidentiality property

In this paper a new cryptographic protocol for finding the meeting place of participants is proposed. This protocol allows several participants to choose the meeting place closest to their locations not revealing the coordinates of each of the participants. The protocol also allows to detect an error occurring during the transfer of information between participants. The correctness and security of the used approach are proved, and the main advantages and disadvantages are determined.
Lossless Image Compression for 4D-STEM Datasets

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Reliability evaluation for deployment of multi service multi functional service oriented computing based on different techniques

Reliability evaluation of service-oriented computing against massive stresses of end users imputes greater emphasis on adaptability of different hosting techniques. As such, we propose to study the failure and reliability aspects of multi service-based web services by using Tomcat and Internet Information Service web server. The service roles such as broker, parent and service provider are segregated among different services. We present here in detail the architecture, a reliability evaluation framework, experimentally and statistically analysed results to establish the applicability of the proposed work. The overall assessment of such deployment in respective web servers highlights the dynamic nature of service reliability. The results shows that the reliability of Internet Information Service is better than Tomcat web server but the efficiency of processing massive request is comparatively poor than its counterpart. Also, the reliability of segregation of web service role is observed to be better than the merged roles.
Hiding Data in Plain Sight: Undetectable Wireless Communications Through Pseudo-Noise Asymmetric Shift Keying

Undetectable wireless transmissions are fundamental to avoid eavesdroppers. To address this issue, wireless steganography hides covert information inside primary information by slightly modifying the transmitted waveform such that primary information will still be decodable, while covert information will be seen as noise by agnostic receivers. Since the addition of covert information inevitably decreases the SNR of the primary transmission, key challenges in wireless steganography are: i) to assess the impact of the covert channel on the primary channel as a function of different channel conditions; and ii) to make sure that the covert channel is undetectable. Existing approaches are protocol-specific, also we notice that existing wireless technologies rely on phase-keying modulations that in most cases do not use the channel up to its Shannon capacity. Therefore, the residual capacity can be leveraged to implement a wireless system based on a pseudo-noise asymmetric shift keying (PN-ASK) modulation, where covert symbols are mapped by shifting the amplitude of primary symbols. This way, covert information will be undetectable, since a receiver expecting phase-modulated symbols will see their shift in amplitude as an effect of channel/path loss degradation. We first investigate the SER of PN-ASK as a function of the channel; then, we find the optimal PN-ASK parameters that optimize primary and covert throughput under different channel condition. We evaluate the throughput performance and undetectability of PN-ASK through extensive simulations and on an experimental testbed based on USRP N210 software-defined radios. We show that PN-ASK improves the throughput by more than 8x with respect to prior art. Finally, we demonstrate through experiments that PN-ASK is able to transmit covert data on top of IEEE 802.11g frames, which are correctly decoded by an off-the-shelf laptop WiFi.
Face Mask Recognition Using Modular, Wavelet and Modular-Wavelet PCA

The world is currently under the grip of the COVID-19 pandemic. The only possible escape from this pandemic is wearing a mask. Mask checks are being done in most public places. This project brings out a good technique for recognizing a mask on a face. Principal component analysis (PCA) is a dimensionality reduction method which is used in image and signal processing. It uses only the principal components of a dataset and ignores rest of the components. In this paper, three different pre-processing methods (modular, wavelet and a combination of both) have been performed on an image dataset. The resultant data has been processed through PCA. Through comparison of the processed data vectors, the similarity between images has been established. Also, a comparison between different PCA techniques is developed through recognition of distorted faces. Initially, a comparison between different PCA techniques is developed through recognition of distorted faces. Then, a comparison is done between the Modular PCA, Wavelet PCA and Modular-Wavelet PCA techniques by observing the performance for mask recognition. Finally, a conclusion is drawn as to which method is the best in terms of mask recognition.
Human-Centric Active Perception for Autonomous Observation

As robot autonomy improves, robots are increasingly being considered in the role of autonomous observation systems -- free-flying cameras capable of actively tracking human activity within some predefined area of interest. In this work, we formulate the autonomous observation problem through multi-objective optimization, presenting a novel Semi-MDP formulation of the autonomous human observation problem that maximizes observation rewards while accounting for both human- and robot-centric costs. We demonstrate that the problem can be solved with both scalarization-based Multi-Objective MDP methods and Constrained MDP methods, and discuss the relative benefits of each approach. We validate our work on activity tracking using a NASA Astrobee robot operating within a simulated International Space Station environment.
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video

Key challenges for the deployment of reinforcement learning (RL) agents in the real world are the discovery, representation and reuse of skills in the absence of a reward function. To this end, we propose a novel approach to learn a task-agnostic skill embedding space from unlabeled multi-view videos. Our method learns a general skill embedding independently from the task context by using an adversarial loss. We combine a metric learning loss, which utilizes temporal video coherence to learn a state representation, with an entropy regularized adversarial skill-transfer loss. The metric learning loss learns a disentangled representation by attracting simultaneous viewpoints of the same observations and repelling visually similar frames from temporal neighbors. The adversarial skill-transfer loss enhances re-usability of learned skill embeddings over multiple task domains. We show that the learned embedding enables training of continuous control policies to solve novel tasks that require the interpolation of previously seen skills. Our extensive evaluation with both simulation and real world data demonstrates the effectiveness of our method in learning transferable skills from unlabeled interaction videos and composing them for new tasks. Code, pretrained models and dataset are available at http://robotskills.cs.uni-freiburg.de
Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellation

New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities. However, the large volumes of images that they produce have uneven qualities, creating the need for automatic quality assessment methods. In this work, we quantify the sharpness of images from the PlanetScope constellation by estimating the blur kernel from each image. Once the kernel has been estimated, it is possible to compute an absolute measure of sharpness which allows to discard low quality images and deconvolve blurry images before any further processing. The method is fully blind and automatic, and since it does not require the knowledge of any satellite specifications it can be ported to other constellations.