Reviewerly/exHarmony huggingface dataset:
Information Retrieval: representation of the subjective

The world of presences is simulated, presented and represented on digital environments. In the context of digital-virtual communication, and from the recent transformations on their supports, information multimodal collections move to a state in which informational subjects’ access to information can be facilitated and expanded. In such a universe, represented information and data need conceptual and technical treatments that respect the nature and particularities of the media and languages that transit through them. In addition, as they are processed on a fragile medium such as the digital medium, information encoded on electronic media also needs procedures and curation aimed at its integral preservation. In Information Science, the set of continuous and iterative procedures aimed at meeting the demands of curation to optimize access and preservation is named Digital Curation: a complex of processes that range from the initial design and conceptualization, to metadata designation, appraisal for preservation or disposal decisions, transformation, access, sharing and re-appraisal of digital objects. When it comes to gender issues, conditions are more acute as the need for Digital Curation becomes more pressing when we are faced with the dramatic events concerning its multiple territories. In them, information must be processed with the socio-cultural purpose of creating strategies and instruments to overcome numerous challenges and current injustices. The book Digital Curation and Gender in Information Science: access and preservation addresses, through the combined vision of authors from the national and international panorama, issues concerning some gender issues in the perspective of improvements provided by Digital Curation actions and the particulatiries of preservtion and acces that apply. ---- This book is the result os discussions and collaborations intiated in 2020, the period one of the organizers was, as a Visiting Professor, at Universidade Carlos III de Madrid, grated by Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (Capes), under the Capes-Print Program, No. 88887.310463/2018-00, International Cooperation Project No. 88887.468796/2019-00. We thank Capes for providing the opportunity for meetings with the involved scholars and for funding this collection. Selo Editorial: Apoio:
On Hybrid Network Covert Channel Capacity

Covert channel is a communication channel that was not designed for information transmission. Covert channels in IP networks can be implemented in a way that is difficult to detect. That is why the method of capacity limitation of a potential covert channel seems to be an effective counteraction that can be used when allowable level of covert channel capacity is set up. To effectively protect information from leakage, it is needed to estimate covert channel capacity accurately. In this paper authors investigated the way to estimate hybrid covert channel capacity taking into account cipher type for traffic encryption.
IMPORTANCE OF CYBER SECURITY ANALYSIS IN THE OPERATIONAL TECHNOLOGY SYSTEM LIFECYCLE

This research focuses on the importance of cyber security analysis in the operational technology system lifecycle. Specifically, cyber security issues are analyzed when using information technology workstations to manage modern safety instruments that are critical components of safety instrumented systems. Attack paths and security controls in real-world industrial control safety system architectures typically used in the oil and gas sector are examined to determine whether a safety-instrumented-system-mediated architecture could provide better protection against unauthorized and malicious safety instrument configuration changes than a multiplexer-mediated architecture. The determination leveraged crafted assessment questions that were answered using standard cyber security assessment methods. The research reveals that recurring vulnerabilities exist in all safety systems due to design issues in safety instruments, the Highway Addressable Remote Transducer protocol, third-party device management software and safety instrument management solutions. Additionally, device-native hardware write protection provides the best defense followed by safety instrumented system write protection. When using safety instrumented system security controls, a safety-instrumented-system-mediated architecture can protect against unauthorized device reconfigurations better than a multiplexer-mediated architecture. The key insight is that cyber security analyses commonly used in information technology systems must be adapted and used in the lifecycles of operational technology systems such as industrial control systems and safety instrumented systems to manage the safety risks induced by cyber attacks.
Fractal analysis of structural characteristics and prospecting of the Luanchuan polymetallic mining district, China

Abstract The Luanchuan polymetallic mining district (LPMD) covers many major deposits of the East Qinling metallogenic belt in Henan Province, central China. It has attracted much attention because of its various types of minerals, abundant mineral reserves and huge metallogenic potential. Systematic research on the geology of LPMD has been carried out previously, and it is concluded that the formation of the ore deposits is dominantly controlled by fault structure. However, there are few reports on the quantitative characterization of fault structure and spatial distribution of deposits and the coupling relationship between them. This study applies fractal theory to the illustration of the characteristics of inter-relationship between fault structure and spatial distribution of the ore deposits in LPMD, which then may serve as a basis for further ore exploration. Our results indicate that the capacity dimension (CPDs) of integrated fault structure, NW-trend fault and NE-trend fault for the area investigated are 1.7206, 1.6539 and 1.2145, respectively; while the information dimension (IND) of which is 1.7143, 1.6559 and 1.2222, respectively. The studied area has superior geological environment for potential ore deposits, with major contribution from the NW-trend fault. Spatial distribution dimension value of metallic ore is 0.8873. Quantity fractal dimension and Density fractal dimension values of the metallic deposits are 1.1154 and 1.115, respectively. Quantitatively, the main ore deposits discovered in this area mainly fall on either CPD ≥ 1.49, IND ≥ 1.38 or 1.49 &gt; CPD ≥ 1.00, 1.38 &gt; IND ≥ 1.00. Qualitatively, conditions that facilitate ore deposits require higher fractal dimension values. Among the areas studied, we found that Rank I area is the most ideal region that fulfilled the quantitative and qualitative conditions, which is consistent with the actual location of the ore deposits discovered.
A Comparative Performance Analysis of Fast K-Means Clustering Algorithms

Data clustering is a fundamental and widespread problem in computer science, which has become very attractive in both scientific communities and application domains. Among the different algorithmic methods, the k-means algorithm, and its prominent implementation, the Lloyd algorithm, has developed into a de facto standard for partitioning-based clustering. This algorithm, however, turns out to be inefficient on very large databases. In order to mitigate this efficiency issue, several fast k-means algorithms for ad-hoc and exact data clustering have been proposed in the literature. Since their inner workings and applied pruning criteria differ, it is difficult to predict the efficiency of individual algorithms in certain application scenarios. We thus present a performance analysis of existing fast k-means algorithms. We focus on simple interpretability and comparability and abstract from many implementation details so as to provide a guide for data scientists and practitioners alike.
Considerations for SLAM Technology in Dynamic Underground Spaces

The ultimate goal of this research is to estimate the self-position of an autonomous vehicle in an underground environment where GNSS cannot be used and environmental texture changes are poor.
AI Based Approach for Shop Classification and a Comparative Study with Human

The rapid advancements in artificial intelligence algorithms have sharpened the focus on street signs due to their prevalence. Some street signs have consistent shapes and pre-defined colors and fonts, such as traffic signs while others are characterized by their visual variability like shop signboards. This variations create a complicated challenge for AI-based systems to classify them. In this paper, the annotation of the ShoS dataset were extended to include more attributes for shop classification. Then, two classifiers were trained and tested utilizing the extended ShoS dataset. SVM showed great performance as its F1-score reached 89.33\%. The classification performance was compared with human performance, and the results showed that our classifier excelled over human performance by about 15\%. The results were discussed, so the factors that affect classification were provided for further enhancement.
Research on Intelligent Learning Graphics Diagnosis Technology Based on Computer Deep Learning Algorithm

The image feature extraction method and the choice of the classifier are the key factors affecting the accuracy of image classification. The traditional algorithm uses a single image feature and shallow structure to classify images. The algorithm is simple to implement but the accuracy of the result is not high. This paper proposes an adaptive convolutional neural network (CNN) image classification algorithm. By fusing the main color features of the image, CNN is used to extract spatial location features, and for the setting of multi-feature fusion weights, the improved differential evolution algorithm is used to optimize the weights of each feature and improve the classification accuracy of fixed weights. The experimental results show that the classification accuracy of the algorithm is improved by 9.2 percentage points compared with the CNN algorithm, and it has a better classification effect in image classification.
Research on learning vocabulary test system under computer big data technology

The application of computer big data technology in education is an inevitable technology trend leading to educational reform. The wide application of big data technology has brought new development opportunities to English learning, and big data technology has enriched the ways and means of English professional vocabulary learning. Therefore, the English vocabulary test system driven by big data has become one of the essential topics in the current education reform and the cultivation of high-quality professionals. English learning and vocabulary testing have always been the bottleneck in English learning. We must use the big data platform to provide timely and accurate data and an accurate teaching system to break this bottleneck. This paper has some enlightenment effects on English Vocabulary learning from the background of the times, vocabulary learning mode and practice, application conditions of a test system, theory, and test items.
Academic Administration Information System for Junior High School (SMP) PGRI Bengkulu City

SMP PGRI Bengkulu City is one of the schools in Bengkulu Province. In the process of managing academic data, most of them are still done manually and have also used office application packages in managing data, namely Microsoft Excel. The problem that often occurs is the absence of an integrated system that is able to manage academic data as a whole, so that data management takes a long time. The Academic Administration Information System for Junior High School (SMP) PGRI Bengkulu City was created using the PHP programming language and MySQL database. The features contained in the Academic Information System are operator data processing, student data, teacher data, class data, subject data, student grade data, teacher data recap, student data recap, subject data recap per class, grade recap per subject. In this academic information system, there are 3 access rights, namely Operator, Teacher, and Student, each of which has different features. Based on the results of the tests that have been carried out, the Academic Information System at the PGRI Bengkulu City Junior High School (SMP) has been running as it should.
Classification and Identification of Objects in Images Using CNN

Convolution neural network also called as CNN is one of the deep learning technique. CNN in recent time has evolved as most popular tool to solve vision related use cases. In the field of computer vision, the challenge of classifying a given image and detecting an object in an image is extremely difficult and it has numerous real-world applications. In recent years, the use of CNN has increased dramatically in a variety of fields, including image classification, segmentation, and object recognition. Alex Nets, GoogLeNet, and ResNet50 are the most popular CNNs for object detection and from the different images. The performance of CNN depends directly on its hyperparameters. More you tune those parameters better you get the results. As a result, it’s an important study on how to use CNN to improve object detection performance. Many strategies have been explored to optimise the hyperparameters of the CNN architecture. Gradient Descent, Back Propagation, Genetic Algorithm, Adam Optimization, and so on are some of them. The CNN architecture was trained using a variety of population-based search and evolutionary computing (EC) methodologies. Genetic algorithms, differential evolution, ant colony optimization, and particle swarm optimization, among other population-based techniques, have recently been utilised to train hyperparameters. In this literature, we will review the various aspects of CNN and its architecture followed by a detailed explanation of optimization strategies that aid in boosting accuracy.
Application of Procedural Generation Algorithms in Real-Time Game Strategy Environment Based on the MVC Concept

This paper presents an example of using procedural generation methods in a real-time strategy environment created on the basis of the MCC concept. The most important feature of the presented project is its modularity. All game level objects are independent of each other. An algorithm for visualizing objects based on procedural generation is described. The problems and their solutions that arose during the creation of the game are considered.
Effective Natural Language Processing and Interpretable Machine Learning for Structuring CT Liver-Tumor Reports

In the past, the liver tumors were reported manually in an unstructured format. There actually exists much valuable knowledge in these reports for further disease risk assessment, disease recognition and treatment recommendation. Yet, it is not easy to read and mine knowledge from the unstructured reports. Hence, how to extract the knowledge from these biomedical reports effectively and efficiently has been a challenging issue in the past decades. Although a set of Natural Language Processing techniques were proposed for Bio-medical information retrieval, few related works were made on transforming the unstructured CT liver-tumor reports into structured ones. To aim at this issue, in this paper, we propose a two-stage report structuring method by integrating effective Natural Language Processing (NLP) and interpretable machine learning. For the first stage, the candidate keywords in unstructured reports are extracted. Next, the feature keywords are determined by the feature-selection technique. For the second stage, the well-known multi-classifiers are performed, and finally the reports are labeled in a refined structure format. Further, the factor keywords in the classification model are filtered to interpret the performance. In overall, the proposed report structuring method generates a hierarchical data structure, including the common features and refined features in the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> levels/stages, respectively. To reveal the performance of proposed method, a set of evaluations were conducted and the results show that, the proposed method is more promising than the fashion neural networks such as Bert (Bidirectional Encoder Representations from Transformers) in terms of effectiveness and efficiency.
Temperament detection based on Twitter data: classical machine learning versus deep learning

Deep learning has shown promising results in various text-based classification tasks. However, deep learning performance is affected by the number of data, i.e., when the number of data is small, deep learning algorithms do not perform well, and vice versa. Classical machine learning algorithms commonly work well for a few data, and their performance reaches an optimal value and does not increase with the increase in sample data. Therefore, this study aimed to compare the performance of classical machine learning and deep learning methods to detect temperament based on Indonesian Twitter. In this study, the proposed Indonesian Linguistic Inquiry and Word Count were employed to analyze the context of Twitter. The classical machine learning methods implemented were support vector machine and K-nearest neighbor, whereas the deep learning method employed was a convolutional neural network (CNN) with three different architectures. Both learning methods were implemented using multiclass classification and one versus all (OVA) multiclass classification. The highest average f-measure was 58.73%, obtained by CNN OVA with a pool size of 3, a dropout value of 0.7, and a learning rate value of 0.0007.
Analysis and design SCADA alarm management system strategy based on jitter characteristics

The alarm management system often has the problem of "flood" alarm for a period of time. A large number of flooding alarm will cause a crisis of trust between human and machine and reduce the availability of the system. The article first investigates the problem of unbalanced alarm distribution in the actual application of the alarm system. In order to adjust the probability density of different alarms, it is necessary to introduce the concept of alarm chatter rate that characterizes the arrival density of alarms. Then, the description principles and methods of the characteristics of alarm redundancy chatter are given. An alarm chatter rate characterization algorithm combining attenuation factor and natural alarm arrival rate is proposed. Finally, the concrete realization of the compression strategy based on this feature processing is given. Comparing and analyzing in actual test, this method can effectively reduce the number of redundant alarms and improve the efficiency of human-machine information interaction expression.
HUBUNGAN KERAGAAN BIOFISIK DAN MORFOMETRI DAERAH ALIRAN SUNGAI (DAS) KENALI BESAR, PROVINSI JAMBI DENGAN KONDISI HIDROLOGINYA (The Relationship between The Biophysical and Morphometry of The Kenali Besar Watershed, Jambi Province with Its Hydrological Conditions

Watershed degradation is one of the environmental problems in many parts of Indonesia. The main environmental problem in the watershed is land-use that does not consider soil and water conservation principles. Irresponsible land use will affect the hydrological function of an area. An impaired hydrological function is one indicator of watershed damage. The aim of the study was to identify and evaluate the biophysical conditions, watershed morphometry, and its relationship with the hydrological condition of the Kenali Besar watershed. The Kenali Besar watershed covers the area of Jambi City and Muaro Jambi Regency. The research was carried out in 2020 using a survey method and the research data were analyzed descriptively. The results showed that the Kenali Besar watershed has an elongated shape with medium drainage density. The watershed morphometry shows that the Kenali Besar has a relatively fast time to peak and decline in flow as well. The analysis shows that the Kenali Besar watershed has a flood risk because the land use is dominated by residential areas (40.69%), which has a relatively low capacity and infiltration rate. The ratio of instantaneous flow rates in the middle and downstream reaches 54.10 m3/s and 57.67 m3/s, respectively
Photobombing Removal Benchmarking

Photobombing occurs very often in photography. This causes inconvenience to the main person(s) in the photos. Therefore, there is a legitimate need to remove the photobombing from taken images to produce a pleasing image. In this paper, the aim is to conduct a benchmark on this aforementioned problem. To this end, we first collect a dataset of images with undesired and distracting elements which requires the removal of photobombing. Then, we annotate the photobombed regions which should be removed. Next, different image inpainting methods are leveraged to remove the photobombed regions and reconstruct the image. We further invited professional photoshoppers to remove the unwanted regions. These photoshopped images are considered as the groundtruth. In our benchmark, several performance metrics are leveraged to compare the results of different methods with the groundtruth. The experiments provide insightful results which demonstrate the effectiveness of inpainting methods in this particular problem.
YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

Wearing of surgical face masks has become the new norm of our daily life in the context of the COVID-19 pandemic. Under many conditions at various public places, it is necessary to check or monitor whether the face mask is worn properly. Manual judgement of mask wearing not only wastes manpower but also fails to monitor it in a way of all-time and real-time, posing the urge of an automatic mask wearing detection technology. Earlier automatic mask wearing methods uses a successive means in which the face is detected first and then the mask is determined and judged followingly. More recent methods take the end-to-end paradigm by utilizing successful and well-known CNN models from the field of object detection. However, these methods fail to consider the diversity of face mask wearing, such as different kinds of irregularity and spoofing. Thus, we in this study introduce a comprehensive mask wearing detection dataset (named as Diverse Masked Faces) by distinguishing a total of five different classes of mask wearing. We then adapt the YOLOX model for our specific task and further improve it using a new composite loss which merges the CIoU and the alpha-IoU losses and inherits both their advantages. The improved model is referred as YoloMask. Our proposed method was tested on the new dataset and has been proved to significantly outperform other SOTA methods in the literature that are either successive or end-to-end.
Rancangan dan Analisis Model Algoritma Genetika Untuk Menyelesaikan Permasalahan Knapsack 2 Dimensi

The knapsack problem is problem that is still often found in everyday life, one of which is the problem of selecting goods to be transported into containers for delivery of goods. This knapsack problem can be solved by using various optimization algorithms, one of which is the genetic algorithm. This study aims to design a genetic algorithm model to solve the 2-dimensional knapsack problem. 2-dimensional knapsack problem is a knapsack problem that has 2 constraints and in this study, the constraints used were weight and volume.. The evaluation results of the genetic algorithm will be compared with dynamic programming. From the evaluation results that have been carried out, it can be concluded that genetic algorithms can produce near-optimal results with faster computational times than dynamic programming.