Reviewerly/exHarmony huggingface dataset:
Methodology for the Virtualisation of Engineering Drawing Exercises for Use Through Extended Reality

The present study is framed within the experiences of educational innovation related to the techniques of graphic representation. This article provides a new approach on the teaching-learning process that implements techniques of extended reality (XR), also known as mixed reality (MR), constituted by the set of solutions of extended reality (XR), Augmented Reality (AR), Virtual Reality (VR) and Mixed and Diminished Realities in teaching. A selection of exercises of Descriptive Geometry in 2D can be transformed into 3D formats, which can be visualised in the three-dimensional space through new and different devices through virtualisation. To this end, a specific methodology is followed, which adapts them to a new way of visualising them. This document explains the methodology carried out to attain this visualisation. The exercises are subjected to different checks to allow the students to delve into the contents of the subject in a completely immersive and friendly manner. The implementation of these improvements in the teaching-learning process shows that one of the most innovative ways of observation is through virtual-reality headphones and goggles (Oculus or equivalent), which implies a step forward in non-immersive 3D visualisation through digital screens in the field of graphic engineering.
An Industrial Robot for Firewater Piping Inspection and Mapping

The design of an industrial pipe inspection robot for 4" firewater piping inspection and mapping with some new and unreported features is presented. The robot consists of a compact 3D MEMs Lidar with non-linear scanning pattern in the form of Lissajous trajectories that produces a dense scan of inner pipe surfaces. Evaluating the surface integrals of these 3D point clouds acquired at pipe turns allow for turn direction discrimination on the horizontal plane which is not realizable using IMUs. A non-SLAM method is used for robust localization: the actual distance traveled by the robot inside a pipe network is measured explicitly by measuring tether displacement; this measurand is then used as the arc length parameter of a parametric curve. This parameterization represents the robot trajectory such that the 3D robot positions along this trajectory are defined in terms of distances traveled (arc length of parametric curve). Pose transitions measured using an on-board IMU combined with the arc-length parametric trajectory let us determine the full 6D robot pose inside featureless pipes. Instantaneous Lidar scans and the 6D poses are used to create a high density 3D map of the pipe network to help identify pipe joint deformation at cm level.
Cost-Performance Evaluation of Heterogeneous Tierless Storage Management in a Public Cloud

Data analytics, which extracts valuable information from a large amount of data, plays an important role in a company's business decision-making. Data analytical processing is generally I/O-intensive because of the need to retrieve data from storage devices. Public cloud services have recently become a popular choice for data analytical processing because a wide variety of storage volumes is immediately available without preparing real hardware. In this type of public cloud, it is necessary to combine multiple types of storage volumes appropriately to obtain a high I/O throughput at a low cost. In this paper, using Amazon Web Services (AWS), we quantitatively evaluate the advantages of a state-of-the-art heterogeneous tierless storage management (HTSM) technique, that is designed for relational databases, over a traditional storage caching mechanism. Our evaluation with all types of Elastic Block Store (EBS) volumes and TPC-H and TPC-DS benchmarks shows that the HTSM technique outperforms Linux bcache by up to 3.35 times within specified cost constraints. Moreover, we demonstrate that it also mitigates the AWS-specific throughput degradations of storage volumes.
Smart-DNN: Efficiently Reducing the Memory Requirements of Running Deep Neural Networks on Resource-constrained Platforms

Deep neural networks (DNNs) have gained considerable attention in various real-world applications due to their strong performance in representation learning. However, running a DNN needs tremendous memory resources, which significantly restricts DNN from being applicable on resource-constrained platforms (e.g., IoT, mobile devices, etc.). Lightweight DNNs can accommodate the characteristics of mobile devices, but the hardware resources of mobile or IoT devices are extremely limited, and the resource consumption of lightweight models needs to be further reduced. However, the current neural network compression approaches (i.e., pruning, quantization, knowledge distillation, etc.) works poorly on the lightweight DNNs, which are already simplified. In this paper, we present a novel framework called Smart-DNN, which can efficiently reduce the memory requirements of running DNNs on resource-constrained platforms. Specifically, we slice a neural network into several segments and use SZ error-bounded lossy compression to compress each segment separately while keeping the network structure unchanged. When running a network, we first store the compressed network into memory and then partially decompress the corresponding part layer by layer. According to experimental results on four popular lightweight DNNs (usually used in resource-constrained platforms), Smart-DNN achieves memory saving of 1/10∼1/5, while slightly sacrificing inference accuracy and unchanging the neural network structure with accepted extra runtime overhead.
EAD: An Efficient Anomaly Detection Algorithm for Multivariate Time Series

Anomaly detection based on deep learning has been widely used in IT infrastructure management. Driven by large amount of data, deep learning (DL) based algorithms can achieve higher accuracy compared to rule-based algorithms. However, the computational complexity of these algorithms is much higher than traditional rule-based ones, which will cost lots of time and computing resources. This limits the application of such algorithms in some actual systems. Therefore, it is necessary to improve the detection efficiency (e.g., execution time, resource occupation, etc.) of existing DL-based algorithms to make them more practical. In this work, we propose an efficient detection approach to solve this problem by combining DL-based algorithms and rule-based algorithms. Specificly, we apply rule-based algorithms to filter the original data roughly and then exploit the DL-based algorithms to make the final decision. We evaluate our approach using two state-of-the-art deep learning anomaly detection approaches with three real-world datasets. The results show that the proposed approach can significantly improve detection efficiency, saving about 80%~95% of execution time under the premise that the accuracy is nearly unchanged.
Characterization and Classification Algorithm for Mammography Images by means of the BIRADS Assessment Categories

According to the World Health Organization, breast cancer is the most common cancer in the world. This is a disease in which cells in the breast grow and multiply out of control. Fortunately, it can be treated and cured if it is early detected. The most widely used screening method for this disease is mammography, which has a reporting standard, called “Breast Imaging Reporting and Data System” (BIRADS), which classifies the lesions in categories numbered from 0 to 6. The aim of this research seeks to design and implement a computer-assisted diagnosis algorithm, in order to identify and classify breast lesions using image processing techniques, as a diagnostic aid for radiologists. For this purpose, five stages were done: Image pre-processing, image segmentation (including pectoral muscle and lesions in the area) by using region-growing technique, texture and morphological features extraction and classification of the lesions. To classify the lesions, a multilayer perceptron (MLP) was used, obtaining an 74.6% of accuracy, fulfilling the objective of exceeding the accuracy of a specialized observer.
Productive High-Performance k-Truss Decomposition on GPU Using Linear Algebra

The k-truss decomposition algorithm aims to find the cohesive subgraphs in a graph. It includes a two-step procedure of triangle counting and weak edge deletion. Due to the significant differences of graph typologies, the best performer appearing in the last year’s MIT/IEEE/Amazon GraphChallenge presents to manually explore a well-optimized combination from many k-truss techniques for each graph dataset, but this requires expertise from multiple disciplines. Linear algebra provides a unified framework to write parallel and scalable k-truss decomposition algorithm easily. However, the work imbalance and low memory efficiency remain. In this work, we propose a productive yet efficient k-truss decomposition implementation on GPU based on the fine-grained linear algebra. We propose a preprocessing method that renumbers the vertices to eliminate the longest adjacency list to balance the workloads and putting the adjacency list of the same source vertex into shared memory that exploits the shared memory effectively to improve memory access efficiency. Results show that our approach outperforms a linear algebra based implementation– eager (a champion in 2019) by up to 24.81× and a manual optimization KTRUSSEXPLORER by up to 2.69×.
A new approach for cross-silo federated learning and its privacy risks

Federated Learning has witnessed an increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the approaches in the literature are focused on mobile environments, where mobile devices contain the data of single users, and typically deal with images or text data. In this paper, we define HOLDA, a novel federated learning approach tailored for training machine learning models on data distributed over federated organizations hierarchically organized. Our method focuses on the generalization capabilities of the neural network models, providing a new mechanism for selecting their best weights. In addition, it is tailored for tabular data. We empirically test the performance of our approach on two different tabular datasets, showing excellent results in terms of performance and generalization capabilities. Then, we also tackle the problem of assessing the privacy risk of users represented in the training data. In particular, we empirically show, by attacking the HOLDA models with the Membership Inference Attack, that the privacy of the users in the training data may have high risk.
Visual Saliency Detection Based on Frequency Band Compensation

The computational model of visual attention simulates the selective attention characteristic of visual system, which has important value and significance in the fields of computer vision, artificial intelligence, image processing and other fields. In this paper, a visual attention detection method based on frequency band compensation was proposed from the perspective of frequency domain analysis, combining the low frequency, medium frequency and high frequency information of images, and using the characteristics of color and brightness. This method was fast, easy to implement, and generated a high-quality saliency map with the same size and resolution as the input image.
Mitigating Web Scrapers using Markup Randomization

Web Scraping is the technique of extracting desired data in an automated way by scanning the internal links and content of a website, this activity usually performed by systematically programmed bots. This paper explains our proposed solution to protect the blog content from theft and from being copied to other destinations by mitigating the scraping bots. To achieve our purpose we applied two steps in two levels, the first one, on the main blog page level, mitigated the work of crawler bots by adding extra empty articles anchors among real articles, and the next step, on the article page level, we add a random number of empty and hidden spans with randomly generated text among the article's body. To assess this solution we apply it to a local project developed using PHP language in Laravel framework, and put four criteria that measure the effectiveness. The results show that the changes in the file size before and after the application do not affect it, also, the processing time increased by few milliseconds which still in the acceptable range. And by using the HTML-similarity tool we get very good results that show the symmetric over style, with a few bit changes over the structure. Finally, to assess the effects on the bots, scraper bot reused and get the expected results from the programmed middleware. These results show that the solution is feasible to be adopted and use to protect blogs content.
Improved Algorithm of Multiple Minimum Support Association Rules Based on Can Tree

To improve the mining efficiency of Can Tree in association rules, this study found that the header table in the traditional Can Tree mining algorithm repeatedly traverses the path, causing excessive overhead. And the single support will lead to invalid rules. The improved Can Tree mining algorithm performs reduction processing and removes the header table, making it more suitable for the idea of Can tree construction. Traversing the path only once avoids multiple traversals, and the addition of multiple minimum support does not introduce too much overhead. Experiments have proved that the improved Can tree mining is better than the traditional mining algorithm.
Coord-FCN for same-class objects segmentation

The segmentation of same-class objects is a challenge in instance segmentation. In this paper, we propose a simple and universal method for same-class objects segmentation. Our method effectively segments the same-class objects in the image. This method called Coord-FCN extends FCN by adding the coordinate convolution(CoordConv) layer. Coord-FCN training is simple and only adds a small overhead compared to FCN. In addition, CoordConv is easy to generalize to other tasks. The common point of these tasks is that the objects have similar characteristics but are in different positions.
Data Privacy Protection for Maritime Mobile Terminals

Mobile edge computing (MEC) can effectively support various high reliability and low delay maritime applications, but security risks in computing tasks offloading exist. We describe a maritime network (MN) model based on MEC, and analyze the risk of data privacy leakage of maritime mobile terminal (MMT) during the offloading tasks. For abnormal maritime monitoring data (MMD), a data cleaning algorithm of sample density-based local outlier factor (SD-LOF) is proposed. It can effectively save the cost of data privacy protection (DPP) and improve the quality of MEC service. For data privacy leakage of MMT, an attribute sensitivity-based differential privacy (AS-DP) algorithm is proposed to balance security and availability of data and improve the differential privacy protection utility. Experimental results have verified the validity of the proposed two algorithms.
INSTITUTIONAL DIRECTION ON THE USE OF MOBILE DEVICES IN HYPERCONNECTED EDUCATIONAL CONTEXTS.

The aim of this paper is to present the methodological approach of a research focused on the study of the direction that the educational centres of Secondary Education in Spain, are taking around the dimension defined by the mobile learning. How is the use of mobile devices being regulated in educational environments that include generation Z training? What value is given to digital technology in the regional institutional guidelines? What discursive practices underlie its regulation? Methodologically, the work is articulated from a hermeneutic-interpretative study of normative texts and through a qualitative research that aims to know the position of those responsible for innovation in schools in the Community of Castilla and León of the use of mobile devices. As first results we highlight the multiscale character of the established regulation, which allows the centres to establish their own actions in this regard and the lack of evidence that exists regarding the pedagogical significance of these mobile devices.
Cyber-Security Culture: Psychological and Legal Aspects

Digitalization has become part and parcel of the modern-day human activities. Nowadays it is going into every field of business and personal life. To develop and prosper, most organizations need IT systems, and hence to take the safeguarding of their informational assets seriously. Many of the processes which are essential for securing their IT assets, largely depend on human interaction. This study has attempted to address the culture of cyber-security in the light of psychology and law. The results of the research showed that from the psychological standpoint, the culture of cyber-security involves the willingness on the part of a modern human to overcome the digital expansion by mastering the tools for countering the negative IT factors. In its turn, from the legal standpoint, the culture of cyber-security is based on the legislative framework which regulates the legal relations in the field of cyber-security.
Classification of Questions Based on Difficulty Levels using Support Vector Machine and Naïve Bayes Algorithms for Imbalanced Class

Quiz questions are crucial evaluations in measuring student learning development because they are one of the lecturers' benchmarks for providing learning materials. The accuracy of the results of measuring student competency achievement is important because it will be used as a benchmark for assessment by lecturers, therefore a question instrument that functions well is needed in distinguishing between students who have high abilities and students who have low abilities based on defined criteria. A good question, that is, when a question has a balanced level of difficulty (proportional), it is said that the question is good. However, a question should be neither too difficult nor too easy. On that basis, grouping the level of difficulty of the questions should be done to make a package of questions that fit the portion. The case study taken by the researcher is a Data Warehouse S1 Information System at Telkom University. The case study was taken because the Data Warehouse course is a compulsory subject in the Information Studies Program at Telkom University. In doing the classification, the writer compares the Naive Bayes algorithm and the Support Vector Machine. The comparison results obtained the highest accuracy with the algorithm method SVM Classification. The accuracy results were obtained from the comparison of the average scores on the algorithm Na&#x00EF;ve Bayes (Before SMOTE) of 85.73% and the SVM algorithm (Before SMOTE) of 85.11%. then for the comparison of the average score on the algorithm Na&#x00EF;ve Bayes (After SMOTE) of 88.9% and on the SVM algorithm (After SMOTE) of 97.82%.
Global Attention Augmentation Ghost Module: More Features from Lightweight Global Attention Extraction

Recently, in order to deploy neural networks on mobile devices, many studies have focused on reducing the number of parameters and computational complexity of neural networks. However, most existing methods do reduce the computational complexity of deep neural networks, but also greatly sacrifice their performance. To maintain the relative balance of computational complexity and performance of deep neural networks, this paper proposes a Global Attention Augmentation Ghost(GAAG) module, which decreases the number of parameters while bring performance improvements. Analyzed the network architecture of the Ghost module, we empirically show it is waste of computational resources that cheap operation in Ghost module produces more feature maps by linear transformations, only increasing the width of convolution neural network instead of extracting more useful feature information, and in a convolution layer composed of multiple feature blocks, the circulation of channel information is essential to better integrate the information of each feature blocks. Therefore, we propose a lightweight long-range dependency extraction block instead of cheap operation, which increases the ability to extract the non-local information of the model while keeping the computational cost almost invariable. Furthermore, we combine channel shuffle and channel attention to promote the fusion of local and non-local information. The proposed GAAG module is efficient yet effective and can be flexibly plugged into existing convolutional neural networks. Experiments conducted on the benchmark demonstrate that the GAAG module can perfectly replace the traditional convolutional layer in the baseline model. We extensively evaluate our GAAG module on image classification and object detection with backbones of ResNets. The experimental results show that our GAAG module can keep a good balance between lightweight and high performance compared with the similar model.
InterPlanetary File System-Based Decentralized and Secured Electronic Health Record System Using Lightweight Algorithm

The electronic health record (EHR) system is a cloud-based patient health record in digital format that often includes contact information about the patient, test reports, medical history, and current and previous prescriptions. However, data breaches in cloud-based EHRs pose significant privacy and security concerns for a variety of health care organizations. Cryptographic techniques currently in use are inadequate to secure EHR data in the cloud from data breaches. Blockchain technology is a new technology that can be used to address security and privacy problems with EHR data on the blockchain in a decentralized manner. We have created a stable decentralized medical blockchain in this paper to address privacy and security concerns when sharing patient data on health care between medical organizations. The health care data is encrypted using Advanced Encryption Standard-based lightweight authenticated encryption algorithm before being uploaded to a cloud-based blockchain and Solidity smart code built on Ethereum to restrict access to EHR data in the cloud. We have used an InterPlanetary file system to store data because it is distributed and ensures record immutability. The medical blockchain also ensures that patient EHR data is interoperable, traceable, and anonymous across organizations. The stable cloud-based blockchain of medical records visualizes patient care data in a distributed and immutable environment with enhanced protection.
Digital Dutch Archaeology: Future perspectives

Digital archaeology in the Netherlands is connected with investments in a new large-scale and innovative Dutch research infrastructure for the next 10 years. Priorities are set to combine forces at a national level in order to contribute to the international position of the Netherlands as a 'knowledge country'. Researchers need to coordinate and collaborate even more to set up a FAIR enabling data infrastructure with limited resources. Regarding the archaeological discipline, the use of formal quality standards and legislation that certifies archaeological organisations to carry out archaeological work improves national collaboration and stimulates the digital workflow. DANS is the dedicated national repository for archaeology in the Netherlands and is launching the Data Station Archaeology, a repository meeting the latest technological standards. Finding and sharing data of high quality facilitates knowledge of archaeological discoveries; a flourishing open access trend in Dutch archaeology stimulates a strong growth in the use of data. To keep up with innovative developments, a growing community of archaeologists and other specialists are working together in international projects to secure the future of European Archaeology.