Reviewerly/exHarmony huggingface dataset:
Semantic Tagging via Entity-Level Analytics: Assessment of Concise Content Tagging

Digital curation requires substantial human expertise in order to achieve and maintain document collections of high quality. This necessitates usually expert knowledge of a librarian or curator in order to interpret the content and categorize it accordingly. This process is at the same time expensive and time-consuming. With the advent of knowledge bases and the plenitude of information contained within them new opportunities emerge at the horizon. In particular, entity-level analytics allows to semantically enrich contents via linked open data (LOD). To this end, we assess in this paper the approach of concise content annotation as a means of supporting the process of digital curation. In particular, we compare various entity-level annotation methods and highlight the importance of concise semantic tagging based on qualitative as well as quantitative evaluations.
Incorporating Security Features in System Design Documents Utilized for Cloud-Based Databases

Cloud-based systems are being increasingly deployed due to their numerous benefits. Yet, there is uneasiness amongst organizations opting for such systems mainly due to security concerns. Security and protection of cloud-database systems from unauthorized access present countless challenges but are indispensable to address. Although security features should be included in the initial stages of system design, sometimes they are overlooked and left to the later stages in the development lifecycle. The framework proposed in this paper tackles this lacuna by including security directives at the initial design stage. It allows database designers to adorn the application’s conceptual models namely entity structures, entity life history and data flow diagrams with security features. Discretionary and role-based access control mechanisms are utilized as the main form of security since they can counteract a high portion of security threats. The proposed framework consists of the creation of a unique security profile for each tenant and his users and an analysis algorithm which assists in the detection of possible security pitfalls. Based on the system’s design data provided and security features encoded, this framework is then responsible for testing the overall design; for example, to ensure reachability and isolation of all database objects, functions and roles. Once the design, now supplemented with security features, is evaluated and deemed to be acceptable then SQL language constructs corresponding to the secure database design are generated. The framework is also useable when the cloud database goes live as any of the underlying security specifications can change during run-time, thus ensuring that security is always accounted for and manageable by developers and later on by the tenants themselves.
Comparison of interpretation uncertainty in spatial domains using portable x-ray fluorescence and ICP data

Spatial domains defining either geological models or mineral estimation envelopes are among the few components of the mining life cycle that are not quantitatively assessed to communicate uncertainty or error in mineral resource projects. Recent work has investigated the use of Bayesian approximation methods to assess interpretation uncertainty of the classification of drill hole intercepts to spatial domain categories. A fundamental assumption is that the spatial domains being tested are used for geostatistics and that the spatial domains contain data that is homogenous to satisfy an assumption of stationarity. A binomial or multinomial model of data used to generate the spatial domains can be trained and simulated to assess the model's ability to predict categories. Categorical subjective geological logging and multielement data have shown suitable data inputs to assess interpretation uncertainty at two case studies. In early-stage projects laboratory multielement data may not be available or economically feasible. Portable X-Ray Fluorescence (pXRF) is a cost and time-effective method of providing multielement data at lower precision and accuracy. A case study had both Inductively coupled plasma mass spectrometry (ICP) and pXRF data. Bayesian approximation models were created from both data sets to assess interpretation uncertainty. Visual assessment of uncertainty band graphs and statistical testing using the Bayesian correlated t-test shows that the two data sets' interpretation uncertainty models are practically equivalent and that pXRF data can be used in early stage projects to assess interpretation uncertainty.
Research Competencies Mediated by Technologies: A Systematic Mapping of the Literature

This study starts from the need to identify which are the research competencies mediated by technologies, required to support the acquisition of knowledge in higher education institutions. The work developed here aims to analyze the scientific production related to the subject with the intention of identifying current research trends. To achieve this, the method of systematic review of literature adjusted to social sciences was used, taking as a source of information the Scopus database. The results indicate that most of the scientific texts have been produced in English, and that the treatment of the topic has been constantly developed since 2016, mostly from the approach of the relationship between research strategies with digital literacy, information literacy, access to digital library databases and the development of critical thinking.
FPGA Implementation of a Stream-Based Real-Time Hardware Line Segment Detector

Line detection is the fundamental image processing technique which has various applications in the field of computer vision. For example, lane keeping required to realize autonomous vehicles can be implemented based on line detection technique. For such purposes, however, low detection latency and power consumption are essential. Using hardware-based stream processing is considered as an effective way to achieve such properties since it eliminates the need of storing the whole frame into energy-consuming external memory. In addition, adopting FPGAs enables us to keep flexibility of software processing. The line segment detector (LSD) is the algorithm based on intensity gradient, and performs better than the well-known Hough transform in terms of processing speed and accuracy. However, implementing the original LSD on FPGAs as a pipeline structure is difficult mainly because of its iterative region growing approach. Therefore, we propose a simple and stream-friendly line segment detection algorithm based on the concept of LSD. The whole system is implemented on a Xilinx Zynq-7000 XC7Z020-1CLG400C FPGA without any external memory. Evaluation results reveal that the implemented system is able to detect line segments successfully and is compact with 7.5% of Block RAM and less than 7.0% of the other resources used, while maintaining 60 fps throughput for VGA videos. It is also shown that the system is power-efficient compared to software processing on CPUs.
The Path of Promoting the Innovation of Teachers' Teaching Idea in School-based Teaching Research

In the basic teaching reform, school-based teaching research belongs to a very important topic, so how can we integrate the new curriculum reform idea with the teachers' teaching idea reform and set up new teaching requirements and objectives? This is the most typical problem confronted by the majority of teachers in the actual teaching at this stage. In order to solve the problem effectively, it is necessary to carry out diversified teaching activities with "school-based teaching research" as the starting point, so as to set up a new teaching mechanism with rich connotation and various forms and realize the overall improvement of teaching quality. In this paper, the author conduct initial discussion on the characteristics and significance of school-based teaching research, and then think about the ways to promote teachers' teaching idea innovation in school-based teaching research, hoping to comprehensively improve the teaching level of China.
Troika Generative Adversarial Network (T-GAN): A Synthetic Image Generator That Improves Neural Network Training for Handwriting Classification

Training an artificial neural network for handwriting classification requires a sufficiently sized annotated dataset in order to avoid overfitting. In the absence of sufficient instances, data augmentation techniques are normally considered. In this paper, we propose the troika generative adversarial network (T-GAN) for data augmentation to address the scarcity of publicly labeled handwriting datasets. T-GAN has three generator subnetworks architectured to have some weight-sharing in order to learn the joint distribution from three specific domains. We used T-GAN to augment the data from a subset of the IAM Handwriting Database. We then compared this with other data augmentation techniques by measuring the improvements brought by each technique to the handwriting classification accuracies in three types of artificial neural networks (ANNs): deep ANN, convolutional neural network (CNN), and deep CNN. The data augmentation technique involving the T-GAN yielded the highest accuracy improvements in each of the three ANN classifier types – outperforming the standard techniques of image rotation, affine transformation, and combination of these two – as well as the technique that uses another GAN-based model, the coupled GAN (CoGAN). Furthermore, a paired t-test between the 10-fold cross-validation results of the T-GAN and CoGAN, the second-best augmentation technique in this study, on a deep CNN-made classifier confirmed the superiority of the data augmentation technique that uses the T-GAN. Finally, when the generated synthetic data instances from the T-GAN were further enhanced using the pepper noise removal and median filter, the classification accuracy of the trained CNN and deep CNN classifiers were further improved to 93.54% and 95.45%, respectively. Each of these is a big improvement from the original accuracies of 67.43% and 68.32%, respectively of the 2 classifiers trained on the original unaugmented dataset. Thus, data augmentation using T-GAN – coupled with the mentioned two image noise removal techniques – can be a preferred pre-training technique for augmenting handwriting datasets with insufficient data samples.
Adaptive uncertainty-weighted ADMM for distributed optimization

We present AUQ-ADMM, an adaptive uncertainty-weighted consensus ADMM method for solving large-scale convex optimization problems in a distributed manner.Our key contribution is a novel adaptive weighting scheme that empirically increases the progress made by consensus ADMM scheme and is attractive when using a large number of subproblems.The weights are related to the uncertainty associated with the solutions of each subproblem, and are efficiently computed using lowrank approximations.We show AUQ-ADMM provably converges and demonstrate its effectiveness on a series of machine learning applications, including elastic net regression, multinomial logistic regression, and support vector machines.We provide an implementation based on the PyTorch package 1 .
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other.Highquality CPC models can significantly benefit applications such as comparative question answering and review-based recommendation.Among the existing approaches, nondeep learning methods suffer from inferior performances.The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities.We propose Sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC accuracy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer.Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.
A Blockchain-Based Access Control System for Cloud Storage

Multi-client framework for access control to datasets put away in an untrusted cloud condition. Distributed storage like some other untrusted condition needs the capacity to verify share data. Our methodology gives an entrance authority over the information put away in the cloud without the supplier investment. The primary apparatus of the access control component is ciphertext-strategy characteristic based encryption plot with dynamic properties. Utilizing a blockchain-based decentralized record, our framework gives the permanent log of all important security occasions, for example, key age, get to strategy task, change or renouncement, get to ask for. We propose a lot of cryptographic conventions guaranteeing the security of cryptographic tasks requiring mystery or private keys. Just ciphertext of hash codes are exchanged through the blockchain record. The model of our framework is executed utilizing shrewd contracts and tried on the Ethereum blockchain stage.
ANALYZING PASSWORD DECRYPTION TECHNIQUES USING DICTIONARY ATTACK

To guard ourselves against a word attack or a breach, it is always important to have an awareness of the unremarkably used sorts of attacks. The most common type of attack is password guessing. Hackers can guess the passwords locally or remotely using either manually or through an automated approach. One such attack is Dictionary Attack. A dictionary attack tries to make an authentication mechanism fail by sequentially entering each word in a dictionary as a password or trying to find the decryption key of an encrypted message or document. In this paper, an empirical research on how dictionary attack works are performed. In addition to that, different techniques and approaches to the existing dictionary attacks are implemented to make the system more robust. Furthermore, a comparison of methods is performed to find which approach is better to protect the system.
Markov Chain and Its Applications

This paper will explore concepts of the Markov Chain and demonstrate its applications in probability prediction area and financial trend analysis. The historical background and the properties of the Markov’s chain are analyzed.
Editorial to Special Issue “Remote Sensing Data Compression”

A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.
Algorithms Patrolling Content: Where’s the Harm?

This paper reveals ways in which algorithms on the Facebook platform have the effect of suppressing content distribution without specifically targeting it for removal, and examines the consequential stifling of users’ speech. At the heart of it is an examination of the colloquial concept of a ‘shadow ban’. This is a term that refers to specific scenario where users’ content is hidden or deprioritised without informing them. The paper reveals how the Facebook shadow ban works by blocking dissemination in News Feed. This is Facebook’s recommender system that curates content for users, and is also the name of the algorithm that encodes the process. The decision-making criteria are based on ‘behaviour’, a term that relates to activity of the page that is identifiable through patterns in the data. It’s a technique that is rooted in computer security, and raises questions about the balance between security and freedom of expression. The paper is situated in the field of research that addresses the responsibility and accountability of the large online platforms with regard to content moderation. It works through the lens of the user to examine the impact of the Facebook shadow ban. Users, whether they are acting as speakers or as recipients of information, have positive rights that must be protected and they should not be treated as passive victims. The user experience was studied over the period of a year from November 2019 to November 2020 across 20 Facebook Pages from the UK. Data provided to the Pages via Facebook Insights was analysed in order to produce a comparative metric, and it was considered how the shadow ban could be assessed under human rights standards.The paper concludes with a recommendation for quality controls on Facebook’s internal processes, potentially with a form of triage to identify genuine, lawful content that has been caught up in the security net. Overall, an improved understanding should be developed around the automated processes and algorithms that are used in content moderation. This is a vital step to safeguarding the online platforms as a forum for public discourse.
Improving Library Book Retrieval by using Topic Modeling

Given the increasing number of books that libraries have, it becomes increasingly difficult for students to find books. We note that the current methodologies like the Dewey Decimal system, are becoming inefficient with book retrieval as the number of books increases. In this paper, we attempt to provide a content-based classifier to organize books to significantly improve retrieval over the current retrieval method. Support Vector Machine (SVM) was the best performing model achieving an accuracy of 79.8%, while latent Dirichlet allocation achieved an accuracy of 28.1%. We also note that the SVM model predicts each news headline in constant time. On average it takes 0.0029s to predict the category of a news headline.
Thermal Performance of Natural Insulation Materials for Energy Efficient Buildings

Natural materials are renewable, sustainable, have less environmental impact and have lower embodied energy. Materials like rock wool, and expanded and extruded polystyrene are of non-renewable nature, have a greater environmental impact and high embodied energy. Natural insulation materials have less effect on the environment as compared to materials like rock wool, and expanded and extruded polystyrene. The mean thermal conductivity of natural materials like wood fiber is almost same and that of insulation materials like wood waste, kenaf insulation board and coconut husk insulation board is comparable with that of materials like rock wool, and expanded and extruded polystyrene. The other natural derived materials like oriented strand board, particleboard, plywood, hardboard and solid wood have a mean thermal conductivity slightly higher than materials like rock wool, and expanded and extruded polystyrene.
Optimal Bayesian Estimation Framework for Reduction of Speckle Noise from Breast Ultrasound Images

A new methodology is proposed in this paper, for the elimination of Speckle noise from the breast Ultrasound Images. Ultrasound is used in radiology to scan the anatomy and other physiological processes of the body and it is an important technique for clinical diagnosis as it gives the live view of internal structures of tissues in the targeted regions and safe also. The existence of Speckle noise in the breast Ultrasound images creates hindrance in getting a clear breast image and making the diagnosis strenuous. In this paper, Optimal Bayesian Estimation Framework intended for the Reduction of Speckle Noise from Breast Ultrasound Images is proposed. The suggested method is compared with that of existing a guided filter and speckle reducing anisotropic diffusion (SRAD) which is the state of art method for reducing the speckle noise with respect to four performance metrics, i.e., Root-mean-square error (RMSE), Structural similarity index metric (SSIM), Peak signal-to-noise ratio (PSNR) and Correlation parameter (CP). Simulation results depict that our present suggested technique shows better outcome with regard to performance metrics.
Modernized Didactic Techniques and Their Impact on the Teaching of Histology by Means of Virtual Microscopy

Background Virtual microscopy is heavily reliant on the quality of computer equipment. The purpose of this study was to determine how students perceived computer upgrades in a histology classroom, and whether these upgrades had any effect on their performance in the administered tests.  Methods This was a pretest-posttest experimental study. Census sampling was applied, and a structured researcher-made questionnaire with 5 items was distributed to 83 students (23 males, 60 females, 20-22 years old). The students took a histology course at the Faculty of Medicine and Dentistry of Palacky University in Olomouc, Czech Republic. The outdated technical equipment used in the first semester was replaced with modern systems in the second semester in 2019. Moreover, students’ performances were compared in computer-aided tests. We compared the average test scores (percentage of correct answers) of the same group using the Wilcoxon test before and after the upgrades. To exclude other possible influences (e.g. better understanding of histology in 2nd semester and approaching histology exam), we also compared the test scores of students who had attended histology classes in the preceding year. Results In total, 75 completed questionnaires (90.4%) were returned, and the results showed that the majority of students (82.7%) noticed a difference when using the new computer equipment. They pointed to the overall comfort of viewing virtual slides using widescreen full HD monitors (n=64/75, 85.3%), better perception of details (n=59/75, 78.7%) and a lower incidence of technical difficulties (n=46/75, 61.3%). Moreover, an improvement was observed in students´ performance in tests (P=0.0002) following the upgrades. Conclusion Investment in the modernization of didactic techniques should be considered as an integral part of any e-learning strategy in morphological fields such as histology.
Big data processing frameworks and architectures: a survey

In recent times, there has been rapid growth in data generated from autonomous sources. The existing data processing techniques are not suitable to deal with these large volumes of complex data that can be structured, semi-structured or unstructured. This large data is referred to as Big data because of its main characteristics: volume, variety velocity, value and veracity. Extensive research on Big data is ongoing, and the primary focus of this research is on processing massive amounts of data effectively and efficiently. However, researchers are paying little attention on how to store and analyze the large volumes of data to get useful insights from it. In this chapter, the authors examine existing Big data processing frameworks like MapReduce, Apache Spark, Storm and Flink. In this chapter, the architectures of MapReduce, iterative MapReduce frameworks and components of Apache Spark are discussed in detail. Most of the widely used classical machine learning techniques are implemented using these Big data frameworks in the form of Apache Mahout and Spark MLlib libraries and these need to be enhanced to support all existing machine learning techniques like formal concept analysis (FCA) and neural embedding. In this chapter, authors have taken FCA as an application and provided scalable FCA algorithms using the Big data processing frameworks like MapReduce and Spark. Streaming data processing frameworks like Apache Flink and Apache Storm is also examined. Authors also discuss about the storage architectures like Hadoop Distributed File System (HDFS), Dynamo and Amazon S3 in detail while processing large Big data applications. The survey concludes with a proposal for best practices related to the studied architectures and frameworks.