Reviewerly/exHarmony huggingface dataset:
Medical Image Watermarking using Spread Spectrum and Compressive Sensing Techniques

In the current era, almost everyone has used digital media to exchange information. One example of the use of digital media in the medical field is the Electronic Patient Record (EPR). However, the use of digital media in EPR raises concerns about the authenticity and security aspects of patient data. The authenticity of medical images is one of the important data and must be considered. One way that can be done to minimize the possibility of various threats is to use a watermark. Another problem that arises is the size of the medical image. This study proposes a watermarking system for medical images using the Compressive Sensing method based on Spare Singular Value Decomposition (SVD) which can be used to reduce the size of medical images. Compressive Sensing includes lossless compression which is a way to compress the size of a data without losing the original information. The medical image watermarking process is divided into two processes, namely the insertion process and the extraction process. The insertion process is carried out using the Spread Spectrum method with a Gaussian distributed PN code key, resulting in a watermarked image. While the extraction process is carried out by reconstructing the image using the Orthogonal Matching Pursuit (OMP) method, SVD reconstruction. The experimental results obtained BER values with an average of 0 when the Gaussian noise attack was carried out. PSNR value range is 30–38 dB and compression ratio is 0.2656.
SiamRPN++D

This paper presents a novel and robust long-term tracking algorithm to address continuous target tracking problems. The continuous target tracking demands handling of correct re-initialization of the lost target when it reappears. The main limitation of the currently popular Siamese class of deep trackers is their inability to re-initialize a target when it is lost for sufficiently long duration or when it re-appears at a location away from the lost location. Most of the Siamese class of deep trackers search for the lost targets in a limited region, close to where it disappears. Hence, they fail in automated re-initialization, tracking resumption and maintaining track after long-term occlusion or tracker-loss. This puts a serious impediment on the current state-of-the-art deep tracker frameworks for many real applications. Here, we propose integration of a lightweight and efficient Cascaded Classifier based detection mechanism with the Siamese trackers for re-initialization of the target. While the proposed approach is generic and applicable to all Siamese class of deep trackers, we have taken SiamRPN++ as a base tracker to illustrate the effectiveness of our tracking framework. The proposition enables Cascaded Classifier based detector to adaptively direct the search region for the base tracker. Extensive experimental results on the well known tracking benchmark datasets such as UAV123, VOT2019, VOT2016, VOT2018 and VOT2018-LT show that the proposed integration significantly improves the performance of the base tracker under occlusion and tracker-loss scenarios. Further, the proposed tracker improves the precision by 1.71% and the recall by 13.98% over the base tracker for the long-term tracking on the VOT2018-LT dataset.
Procedural people in Pixar's Presto

Pixar's Presto Crowd Framework (Pcf) has taken interactive crowd scalability to new heights by harnessing the power of Presto's scalable execution framework for procedurally rigged crowds. Pcf is a vectorized and highly parallelized crowd system that represents an entire crowd as an aggregate model. Prior to Pcf, Presto crowds used a model per agent, via the Mf (model framework), and only scaled to around thousand agents, even with fast GPU based linear blend skinning. Pcf CrowdPrims by contrast can scale to tens and even hundreds of thousands of agents using the same underlying data while maintaining interactive frame rates. Pcf was also designed from the ground up to import from and export to USD to make pipeline deployment seamless. Pcf Crowd Prims use procedural implementations for crowd techniques that are traditionally simulated, like Finite State Machines, making the results deterministic and pipeline friendly. Taken together, our Real-Time Live Demo will prove to the audience that they do not need to settle for the long execution times and history dependent nature of simulated crowds, and that with sufficiently a vectorized/parallelized framework, even stadium sized crowds can be generated/choreographed interactively.
Estimating Imagined Images from Brain Activities via Visual Question Answering

Investigating human mental contents has been a topic for many years, but its ambiguous property has made the analysis difficult. We propose a neural decoding method via a machine learning model that predicts the imagined content based on measuring brain activity in this paper. This technique uses brain activity and computer vision models to discover the association between human functional magnetic resonance imaging (fMRI) activity and imagined contents. Decoding models based on neural networks learned using stimulus-induced brain activity in the visual cortex region showed an accurate estimation of the content. We provide a means of revealing subjective mental content by analysis with visual question answering. This result shows that a mental experience relates to brain activity patterns.
Decision Tree for Uncertain Numerical Data Using Bagging and Boosting

Data uncertainty is ubiquitous in real-world applications, and the number of variables such as imprecise estimation, network latency, obsolete sources, and sampling errors leads to data uncertainty. It is important to carefully deal with this kind of ambiguity, or else the mining results might be inaccurate or even incorrect. We have therefore used a new method called bagging and boosting algorithms for decision tree for the classification and prediction algorithm. As a model of sequential decision problems under uncertainty, a decision tree may be used. To manage data with uncertain information, classifiers are used. To deal with data confusion, we primarily use the PDF distribution method. It is the method in which the complete data are carried out by the distributions of probability to construct a decision tree. Supply these values to the decision tree by calculating the PDF and accordingly generate the decision tree, and on the other hand, supply the bagging and boosting algorithms with PDF and then compare both outcomes. Bagging is one of the well-known techniques of the ensemble, which constructs data bags with the same class mark and the same probability of the original data collection. More complex strategies, such as boosting the distribution of the sample, rely on how difficult it is to identify each sample. Instead of abstracting unknown data by statistical derivatives, if we use the probability density function (PDF) distribution, the accuracy of a decision tree can be much better. The dataset is initially provided to the PDF generation in our project, we provide it to the bagging and boosting algorithm in the next stage instead of giving it directly to the decision tree, then, the generated dataset is given to the decision tree, and then, the classification is carried out.
Analyzing the Cost-Effectiveness of Running HPC Applications on Microsoft Azure Virtual Machines

Nowadays, high-performance computing (HPC) is delivered as a cloud service. Microsoft Azure, one of the prominent cloud service providers, has a designated class of virtual machines (VM) that is recommended for HPC programs. This study presents a cost-effectiveness analysis of using the designated VM type, compared to other cheaper VM types on Microsoft Azure. The study analyzes the communication demand of five message-passing (MPI) programs available in the NAS benchmarks suite on Microsoft Azure VMs, and explains the correlation between cost saving and communication behavior in these programs. The findings of this paper imply the need for future research to consider using machine learning approaches for guiding users towards a more cost-effective use of available VM types on Microsoft Azure for running HPC applications.
Comparison of Feature Selection in The Classification Of Patients With Parkinson's Disease

Parkinson's disease is a weakened disease brain function so that early treatment so far has not been able to be done surgically. Parkinson's disease is a disease that involves a degenerative process in dopaminergic neurons in the substantia nigra. Some parameters for the detection of Parkinson's disease are not continuous, so the role of expert doctors cannot be ignored. Therefore, to help doctors, research in the field of machine learning is done on the detection of patients with Parkinson's disease. This research discusses comparisons of the classification techniques of patients with Parkinson's disease using three feature selection methods, namely information Gain (filter method), Forward Selection (wrapper method) dan Maximum Relevance Minimum Redundancy (mRMR). Feature selection with mRMR approach can improve accuracy compared to filter method and Wrapper method ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$IG &lt; FS &lt; mRMR$</tex> ). Filter method is no better than a wrapper method in experiments conducted. The results show that attribute weighting with mRMR is better than the other two methods.
Project-Innovation Activity in The Vasyl Stefanyk National Scientific Library Of Ukraine in Lviv

The project-innovation activity experience in the Vasyl Stefanyk National Scientific Library of Ukraine in Lviv, in particular development of strategy of development of a monument of cultural heritage &#x00AB;Complex restoration of architectural monument of the early XIX century the building of the V. Stefanyk National Scientific Library of Ukraine in Lviv, Stefanyka Street, 2&#x00BB; within the framework of the national project &#x00AB;Great Construction&#x00BB; within the program &#x00AB;Great Building in Culture&#x00BB;. A set of measures to be implemented within three years for the modernization, systemic transformation and further intensive innovative development of the book collection is considered. Emphasis is placed on ways to ensure the sustainability of project results. The project management procedure is described in detail. It is emphasized that the implementation of the project will help solve a number of problems in the future. In particular, such as: 1) introduction of a customer-oriented service model; 2) improvement of physical and virtual library spaces; 3) improvement of digital content, tools and services; 4) creation of information and communicative creative space; 5) scientific support of library development; 6) ensuring the professional development of staff, updating the system of library and information education and training; 7) creating a system of stable financing.
A Music Playback Control System Based on Facial Expression Recognition

An intelligent music playback control system, which can select the appropriate music to play according to the facial expression recognition result, is designed. The hardware system consists of PYNQ_Z2 development board, camera, HDMI display and audio. The software system performs face detection on the core chip of ARM + FPGA through Haar feature and Adaboost algorithm, and realizes expression recognition through LDA and K nearest neighbor algorithm. The expression recognition results are sub-classified by confidence, and the PID control algorithm is used to adjust the confidence to improve the robustness of the system during uneven illumination. The system has low delay, strong robustness and rich human-computer interaction, and has a wide range of application scenarios.
Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures

Deep learning has made vast advances over the recent decade due to hardware, software, and architecture improvements. Applying deep learning still has some challenges to overcome, especially when processing on GPU or in parallel. This work seeks to apply RNN and LSTM over a variety of architectures to demonstrate the challenges and techniques encountered when applying deep learning. We apply RNN and LSTM over GPU and CPU across PC, high performance computing cluster, and cloud and present our challenges and results.
Optimization of Node-clustering-based DAG partition targeting NVDLA Architecture

The design of deep learning accelerators is in full swing, and reducing the number of on-chip and off-chip data communications is worthy of great attention. Due to limited hard-ware resources, memory allocation significantly affect hardware performance.Tradition directed acyclic graph (DAG) partition algorithm based on dynamic programming and node clustering, a quantity of invalid partitions exist in searching process, which lead to 1) the algorithm search time enlongates and 2) the amount of data is too large. In this article, we proposed an automatic evaluation tool that can evaluate the number of transmissions of any input convolutional neural network (CNN) model on and off the chip. At the same time, the evaluator internally optimizes the network model. Our experimental results on different neural networks show that the evaluation tool can effectively calculate the number of data communications and maximize the efficiency of memory allocation.
Possibilities of using the electronic educational resources to ensure the interactivity of the learning process

This article provides information on characteristics and classification of electronic educational resources according to the type and purpose of use. The main parameters of electronic educational resources such as electronic type of publication (resource); subject area of study; the proposed level of education; the proposed type of educational process; peculiarity of the audience are described in the article. The possibilities of using electronic educational resources to ensure the interactivity of the learning process are also considered, their advantages such as the ability to simulate various processes that replace the use of special equipment and reagents, interactivity, the possibility of network distribution, ease of information retrieval, openness for entering new data, compact storage data are also describes, as well as some disadvantages such as the possibility of information oversaturation of the educational process, the emergence of additional cognitive load, etc are noted in the article. The article also explains such types of electronic educational resources as a computer textbook (textbook, lecture notes, etc.), an electronic reference book, a computer book of tasks, a computer laboratory practice (models, simulators, etc.), a computer testing system that are widely used by teachers to increase the interactivity of the lessons. New types of e-learning resources that have become the most popular links with the transition to online learning like Zoom, Google meet, Google classroom, Microsoft teams and their role in improving the efficiency of the educational process are also described in this article. The role of the teacher and their tasks and goals and requirements, which have increased with the advent of digital educational resources, are also discussed in the article.
A Neural Networks Approach to SPARQL Query Performance Prediction

The SPARQL query language is the standard for querying RDF data and has been implemented in a wide variety of engines. These engines support hundreds of public endpoints on the Web which receive thousands of queries daily. In many cases these endpoints struggle when evaluating complex queries or when they receive too many of them concurrently. They struggle mostly since some of these queries need large amounts of resources to be processed. All these engines have an internal query optimizer that proposes a supposedly optimal query execution plan, however this is a hard task since there may be thousands of possible query plans to consider and the optimizer may not chose the best one. Herein we propose the use of machine learning techniques to help in finding the best query plan for a given query fast, and thus improve the SPARQL servers' performance. We base such optimization in modeling SPARQL queries based on their complexity, operators used within the queries and data accessed, among others. In this work we propose the use of Dense Neural Networks to improve such SPARQL query processing times. Herein we present the general architecture of a neural network for optimizing SPARQL queries and the results over a synthetic benchmark and real world queries. We show that the use of Dense Neural Networks improve the performance of the Nu-SVR approach in about 50% in performance. We also contribute to the community with a dataset of 19,000 queries.
Web VI Layout Issue Detection in Internationalization Automation Testing

With the increasing number of user languages supported by Web applications, internationalization support has become an important function in Web applications. In the Web application test, during the Web UI layout issues, this paper proposes an automatic detection method. This method can perform the real-time UI layout detection for the rendered Web application page, aiming to find the UI layout issues that existed in the different user languages' UI pages, such as string truncation, element nodes overlap. This method realizes automatic detection and analysis, filtering detection results. Experimental tests show that this method can accurately detect Web UI layout issues and improve test efficiency. It provides reference and help for enterprises in automated testing and international testing.
An image classification using convolutional sparse representation and cone-restricted subspace method

This paper presents a classification method based on Convolutional Sparse Representation (CSR) and Cone Restricted Subspace Method (CRSM). CSR extracts image features as convolutional filters and shows the feature maps as coefficients of the filters. This scheme is similar to a convolutional layer of CNNs (Convolutional Neural Networks). To increase the robustness against image shift, this work uses power spectrum as a non-linear operator like activate functions on CNNs. CRSM approximates each set of class as a cone because of non-negativeness of the power spectrum; furthermore, Nonnegative Matrix Factorization (NMF) generates the basis vectors of CRSM. Experiments on handwritten image classification shows that our method using a 3-layer network shows higher recognition ratio than the same structure of CNN when the number of learning images is less than 1,000.
Multitask Variational Autoencoding of Human-to-Human Object Handover

Assistive robots that operate alongside humans require the ability to understand and replicate human behaviours during a handover. A handover is defined as a joint action between two participants in which a giver hands an object over to the receiver. In this paper, we present a method for learning human-to-human handovers observed from motion capture data. Given the giver and receiver pose from a single timestep, and the object label in the form of a word embedding, our Multitask Variational Autoencoder jointly forecasts their pose as well as the orientation of the object held by the giver at handover. Our method is in large contrast to existing works for human pose forecasting that employ deep autoregressive models requiring a sequence of inputs. Furthermore, our method is novel in that it learns both the human pose and object orientation in a joint manner. Experimental results on the publicly available Handover Orientation and Motion Capture Dataset show that our proposed method outperforms the autoregressive baselines for handover pose forecasting by approximately 20% while being on-par for object orientation prediction with a runtime that is 5x faster. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup>
Optimization for Node Cooperation in Hierarchical Federated Learning

With the popularity of the Internet of things (IoT), a large number of data is generated in the network edge. The combination of edge computing and federated learning has become a key technology to reduce latency and energy consumption in mobile networks. The long delay is caused by the nodes transmitting parameters in Hierarchical Federated Learning (HFL). In this work, we propose a Dynamic Cooperative Cluster Algorithm (DCCA) for the delay minimization problem that is proved NP-hard. Moreover, communication between nodes adopts Device-to-Device (D2D) and opportunistic communication. In DCCA, clusters are generated according to the capabilities of different nodes for collaborative training models to reduce delay. We design the DCCA algorithm in two steps. For the first step, we propose an initial dynamic cooperative cluster algorithm based on similarity. For the second step, based on the computing capacity and transmission capacity of the nodes, another algorithm is proposed to adjust the dynamic cooperative cluster according to the core nodes. Extensive simulations are conducted to verify the performance of DCCA. We also observe a reduction in maximum delay by 12.44% and 18.70% respectively, compared with DOA and FedAvg.
Label GM-PHD Filter Based on Threshold Separation Clustering

Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT). However, the traditional GM-PHD filter cannot form a continuous track in the tracking process, and it is easy to produce a large number of redundant invalid likelihood functions in a dense clutter environment, which reduces the computational efficiency and affects the update result of target probability hypothesis density, resulting in excessive tracking error. Therefore, based on the GM-PHD filter framework, the target state space is extended to a higher dimension. By adding a label set, each Gaussian component is assigned a label, and the label is merged in the pruning and merging step to increase the merging threshold to reduce the Gaussian component generated by dense clutter update, which reduces the computation in the next prediction and update. After pruning and merging, the Gaussian components are further clustered and optimized by threshold separation clustering, thus as to improve the tracking performance of the filter and finally realizing the accurate formation of multi-target tracks in a dense clutter environment. Simulation results show that the proposed algorithm can form a continuous and reliable track in dense clutter environment and has good tracking performance and computational efficiency.
Woolery: Toward Semantic Annotation of Structured Documents with FrameNet

In this paper, we introduce Woolery, a preliminary design and implementation of an intelligent annotator interface for structured documents (i.e. JSON). This annotator infers a mapping between the semantics of the keys in the document and an appropriate FrameNet structure, which can then be exported for downstream natural language processing tasks.
Multi-Language Support in TouchCORE

TouchCORE is a multi-touch enabled modelling tool aimed at developing scalable and reusable models. Up to recently, TouchCORE was hard-coded to support only design modelling with class, sequence, and state diagrams. This demonstration paper presents the newest release of TouchCORE, TouchCORE 8, that features multi-language support through language plug-ins and perspectives. The paper reviews what a language designer needs to do to integrate a modelling language into the TouchCORE architecture, and how perspectives can be configured to ensure consistency between multiple models. The generic navigation facilities of TouchCORE are reviewed, and the generic split-view GUI support for displaying multiple models side-by-side together with inter-model consistency mappings is presented.