Reviewerly/exHarmony huggingface dataset:
ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN ALGORITMA GRID SEARCH TIME SERIES CROSS VALIDATION UNTUK PREDIKSI JUMLAH KASUS TERKONFIRMASI COVID-19 DI INDONESIA

Coronavirus Disease 2019 or Covid-19 is a group of types of viruses that interfere with the respiratory tract associated with the seafood market that emerged in Wuhan City, Hubei Province, China at the end of 2019. The first confirmed cases of Covid-19 in Indonesia on March 2, 2020, were 2 cases and until the end of 2021, it continues to grow every day. The purpose of this study was to predict the number of confirmed cases of Covid-19 in Indonesia using the Support Vector Regression (SVR) method with linear kernel functions, radial basis functions (RBF), and polynomials. Support Vector Regression (SVR) is the application of a support vector machine (SVM) in regression cases that aims to find the dividing line in the form of the best regression function. The advantage of the SVR model is can be used on time series data, data that are not normally distributed and data that is not linear. Parameter selection for each kernel used a grid search algorithm combined with time series cross validation. The criteria used to measure the goodness of the model are MSE (Mean Square Error), MAPE (Mean Absolute Percentage Error) and R2 (Coefficient of Determination). The results of this study indicate that the best model is Support Vector Regression (SVR) with a polynomial kernel and the parameters used include Cost = 1, degree = 1, and coefficient = 0.1. The polynomial kernel SVR model produces a MAPE value of 0.4946215%, which means the model has very good predictive ability. The prediction accuracy obtained with an R2 value of 85.65011% and an MSE value of 161606.1.
First person – Sara Rolfe

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping researchers promote themselves alongside their papers. Sara Rolfe is first author on ‘ Deep learning enabled multi-organ segmentation of mouse embryos’, published in BiO. Sara is a Research Scientist in the lab of Murat Maga at Seattle Children's Research Institute, investigating quantifying morphology from 3D images, automated phenotyping, and open image science.
LMS System Requirements from the Libraries’ Point of View

Library services are essential for teaching. The LMS redesign concept should guarantee better integration of library services like catalogs, databases, media, as well as literature and information management. The comparison to other industries can be helpful: often, customer relationship is maintained and intensified by using customer accounts. You should consider especially customer accounts of banking systems and insurance companies. Banking online accounts offer many relevant customer relationship functions that could be helpful in library management systems, too. Also, involving librarian professionals in literature research can be helpful.
Dynamic Adaptation of User Preferences and Results in a Destination Recommender System

Studying human factors has gained a lot of interest in recommender systems research recently. User experience plays a vital role in tourism recommender systems since user satisfaction is the main factor that guarantees the success of such recommender systems. In this work, we have designed and implemented a destination recommender system in which the recommendations adapt instantly based on the user preferences. The recommendations can be explored on a world map with additional information. This interface addresses common visualization challenges in recommender systems, such as transparency, justification, controllability, explorability, the cold-start problem, and context awareness. We have conducted a user study to evaluate different aspects of this recommender system from the users' perspective.
Video-Sharing Platforms’ Appropriate Technological Measures to Counter Copyright Infringements

YouTube II
Stochastic weather generator and deep learning approach for predicting and sampling extreme European heatwaves

Sampling rare events such as extreme heatwaves whose return period is larger than the length of available observations requires developing and benchmarking new&amp;#160; simulation methods.&amp;#160;There is growing interest in applying deep learning alongside already existing statistical approaches to better generate and predict rare events. Our goal is to benchmark Stochastic Weather Generator (SWG) [1] based on analogs of circulation, soil moisture and temperature as a tool for sampling tails of distribution as well as forecasting heatwaves in France and Scandinavia using data from General Circulation Model (GCM). Analog method has been successfully implemented in rare event algorithms for low dimensional climate models [2]. SWG is implemented using a Markov chain with hidden states (.e.g. geopotential height at 500 hPa) with Euclidean metric. When applying such methods to climate data two challenges emerge: a large number of degrees of freedom and the difficulty of including slow drivers such as soil moisture alongside circulation patterns. Consequently, we are going to discuss ways of adjusting the distance metric of the analog Markov chain and dimensionality reduction techniques such as EOFs and variational auto encoder. By choosing the correct combination of weighted variables in the Euclidean metric and using analogs of only 100 years and generating long synthetic sequences we are able to correctly estimate return times of order 7000 years, which is validated based on a 7200 year long control run. The teleconnection patterns generated thus also look reliable compared to the control run. Next we compare SWG forecasts of heatwaves with a direct supervised approach based on a Convolutional Neural Network (CNN). Both CNN and SWG are trained and validated on exactly the same GCM runs which allows us to conclude that CNN performs better in both regions. One could consider SWG as a baseline approach for CNN for this task.[1] Yiou, P. and J&amp;#233;z&amp;#233;quel, A., https://doi.org/10.5194/gmd-13-763-2020, 2020[2] D. Lucente at al. https://10.1088/1742-5468/ac7aa7, 2022[3] DP Kingma, M Welling - https://doi.org/10.48550/arXiv.1312.6114, 2013[4] G. Miloshevich, at al - https://doi.org/10.48550/arXiv.2208.00971, 2022
Slate-Aware Ranking for Recommendation

We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the user can select one or several items from the list for further interaction. In this setting, the significant impact on user behaviors from the mutual influence among the items is well understood. The existing methods add another step of slate re-ranking after the ranking stage of recommender systems, which considers the mutual influence among recommended items to re-rank and generate the recommendation results so as to maximize the expected overall utility. However, to model the complex interaction of multiple recommended items, the re-ranking stage usually can just handle dozens of candidates because of the constraint of limited hardware resource and system latency. Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage. In this paper, we propose a solution named Slate-Aware ranking (SAR ) for the ranking stage. By implicitly considering the relations among the slate items, it significantly enhances the quality of the re-ranking stage's candidate set and boosts the relevance and diversity of the overall recommender systems. Both experiments with the public datasets and internal online A/B testing are conducted to verify its effectiveness.
RNN-based Text Summarization for Communication Cost Reduction: Toward a Semantic Communication

Text summarization has become a subject of great importance because of the continuous global growth of internet technology. The frequent occurrence of the same textual information creates an overhead in the communication network. Extracting the summary from a large document is quite challenging for any human being. Text summarization can play a vital role in this regard. To be precise, text summarization is the process of automatically producing and condensing the content of a given document into a more manageable form with a coherent message. Even though it only contains a few sentences, this concise explanation would nonetheless effectively convey the key idea. This illustrates how text summarization retains the essential information while substantially lowering the quantity of information that must be communicated. In this research, we propose a system model architecture where a central base station sends the summarized text to the users on their edge devices. We also demonstrate how the communication cost is reduced with each transmission of the condensed text. To ensure the best possible summary, we choose the long short-term memory recurrent neural network (LSTM-RNN) since RNN-based deep learning models have achieved great success in text analysis over the past few years. Additionally, the experimental results also show that our proposed model, in combination with LSTM-RNN reduces communication costs by an average of 85%.
Software Metrics for Assessing Reusability of Component Based Software System

For developing Component Based Software System (CBSS), reusability of components plays essential role. Highly reusable components should be selected for developing CBSS to improve its performance and to reduce chances of negligence in society. Reusability is a composite characteristic of any component that depends on several other sub-factors. Researchers have prioritized these sub-factors differently in their research work and assessed various sub-factors. Such three major sub-factors (understandability, customizability, and reliability) are selected in this work for assessing reusability od CBSS. Reusability can be assessed qualitatively or quantitatively. Soft computing techniques can be used for qualitative assessment and software metrics is a good option for quantitative assessment. In this work, software metrics are used to assess the values of these sub-factors quantitatively which are further used to assess the reusability. For showing working of proposed work, experiment is performed on a real world CBSS which was having five different interconnected components, and its reusability is assessed for different weight values.
Modelling seismicity based on fault geometry: maximum magnitudes and magnitude-frequency distributions.

Investigating clusters of events and geophysical screening often provides limited constraints on fault geometries. This imaging issue prevents the integration of realistic fault zone geometry into earthquake studies, which can affect our capacity to evaluate the role of pre-existing faults on seismicity. This study describes a modelling strategy accounting for realistic fault zone geometries. Our approach uses stochastic methods underpinned by quantitative fault zone parameterization, followed by an assessment of seismicity from simulations of rupture dynamics controlled by fault geometry. This method is used to investigate the role of fault maturity on seismicity for two case studies, including seismicity associated with the reactivation of a pre-existing fault network due to hydraulic fracturing in Harrison County, Ohio, from 2013 to 2015, as well as the natural seismicity associated with the Yushu-Ganzi left-lateral strike-slip fault system in central-eastern Tibet. In the Harrison County case, we analyze the effect of vertical variability in fault maturity and show how more mature faults in the deeper crystalline basement generate higher magnitude seismicity than shallow, immature faults in younger sedimentary rocks. In the Yushu-Ganzi case study, we show how lateral variability in structural maturity, arising from long-term fault propagation and strain rates, leads to different seismicity on individual fault segments. Our findings indicate that fault geometry determines seismic patterns, with rupture length controlled by fault zone geometry rather than fault lengths, and favour the adoption of a structural geological perspective for the integration of realistic fault geometry into seismicity prediction strategies.
Comparison of deep learning approaches to monitor trash screen blockage from CCTV cameras

We investigate the use of CCTV cameras and deep learning to automatically monitor trash screen blockage.&amp;#160;Trash screens are installed to prevent debris from entering critical parts of river networks (pipes, tunnels, locks,...). When the debris piles up at the trash screens,&amp;#160; it&amp;#160; may block the waterway and can cause flooding. It is thus crucial to clean blocked trash screens and avoid flooding and consequent damage. Currently, the maintenance crews must manually check a camera or river level data or go on site to check the state of the screen to know if it needs cleaning. This wastes valuable time in emergency situations where blocked screens must be urgently cleaned (e.g., in case of forecast&amp;#160; heavy rainfall). Some initial attempts at trying to predict trash screen blockage exist. However, these have not been widely adopted in practice.&amp;#160; CCTV cameras can be easily installed at any location and can thus be used to monitor the state of trash screens, but the images need to be processed by an automated algorithm to inform whether the screen is blocked.With the help of UK-based practitioners (Environment Agency and local councils), we have created a dataset of 40000 CCTV trash screen images coming from 36 cameras, each labelled with blockage information. Using this database, we have compared 3 deep learning approaches to automate the detection of trash screen blockage:&amp;#160;A binary image classifier, which takes as input a single image, and outputs a binary label that estimates whether the trash screen is blocked. An approach based on anomaly detection which tries to reconstruct the input image with an auto-encoder trained on clean trash screen images.&amp;#160; In consequence, blocked trash screens are detected as anomalies by the auto-encoder. An image similarity estimation approach based on the use of a siamese network, which takes as input two images and outputs a similarity index related, in our case, to whether both images contain trash.&amp;#160; Using performance criteria chosen in discussion&amp;#160; with practitioners (overall accuracy, false alarm rate, resilience to luminosity / moving fields of view, computing capabilities), we show that deep learning can be used in practice to automate the identification of blocked trash screens. We also analyse the strengths and weaknesses of each of these approaches and provide guidelines for their application.
A continual encrypted traffic classification algorithm based on WGAN

With the constant updating of applications and the emergence of various encryption technologies, a large amount of new encrypted network traffic is generated every day. Therefore, it is a challenging task to achieve continual learning of encrypted traffic. Existing encrypted traffic classification techniques can only handle a fixed number of traffic classes, which is not applicable to real network environments. In this paper, we proposed a continual encrypted traffic classification method based on WGAN, called CETC. The method takes advantage of the powerful data generation capabilities of WGAN to model the data distribution of encrypted traffic. When learning from a new traffic class, the samples from the old class is generated by WGAN to train the new classifier. We use the ISCX VPN-nonVPN dataset to test the performance of CETC. Experimental results show that WGAN can generate high-quality samples of encrypted traffic and the accuracy of CETC is higher than 93%. With its efficient and continual learning capability, CETC can be applied to various encrypted traffic detection and management systems.
Earthquake relocation at intermediate depths using automatically detected teleseismic depth phases

Intermediate-depth earthquakes, accommodating intra-slab deformation, typically occur within subduction zone settings at depths between 40-350 km. High magnitude events can pose a significant hazard to populations, and increase the risk of damaged infrastructure, injury and fatality. Despite improvements in recorded seismic data density and quality, the distribution and controls of these events remain poorly understood. Here, we demonstrate an automatic method for the detection of depth phases from these intermediate-depth earthquakes using seismic array data, with the aim of determining their hypocentral depths and locations to a greater level of accuracy. These will allow new comparisons and insights into the governing controls on the distribution of earthquakes in subducted slabs.Depth phases (near-source surface reflections, e.g. pP and sP) are crucial for the accurate determination of earthquake source depth using global seismic data, however, they suffer from poor signal-to-noise ratios in the P-wave coda. This reduces the ability to systematically measure differential travel times to the corresponding direct arrival, particularly for the frequent lower-magnitude seismicity which highlights considerable seismogenic regions of the subducted slabs. To address this limitation, we have developed an automated approach to group globally distributed stations at teleseismic distances into sub-arrays, before optimising and applying phase-weighted beamforming techniques to each sub-array. Resultant vespagrams allow automated picking algorithms to determine differential arrival times between the depth phases (pP, sP) and their corresponding direct P arrival. These are subsequently used to invert for a new hypocentre location. We demonstrate this method by relocating intermediate-depth events associated with the Peruvian flat slab region of the subducting Nazca plate.
Sufficient Networks for Computing Support of Graph Patterns

Graph mining is the process of extracting and analyzing patterns from graph data. Graphs are a data structure that consists of a set of nodes and a set of edges that connect these nodes. Graphs are often used to represent real-world entities and the relationships between them. In a graph database, the importance of a pattern (also known as support) must be quantified using a counting function called a support measure. This function must adhere to several constraints, such as antimonotonicity that forbids a pattern to have support bigger than its sub-patterns. These constraints make the tasks of defining and computing support measures highly non-trivial and computationally expensive. In this paper, I use the previously discovered relationship between support measures in graph databases and flows in networks of subgraph appearances to simplify the process of computing support measures. I show that the network of pattern instances may be successfully pruned to contain just particular kinds of patterns and prove that any legitimate computing support measures in graph databases can adopt this strategy. When the suggested method is utilized, experimental evaluation demonstrates that network size reduction is significant.
SUSTAINABILITY HUB: a virtual world island to teach Agenda 2030 and Sustainability

A virtual world island called Sustainability Hub is described, residing in a virtual world focused on STEM (Techland) created by one of the author as a teaching tool for 11-14 years old students. &amp;#160;Techland is based on the 3D platform Opensimulator, widely used to build virtual scenarios and learning environments accessed by users in forms of avatars.Sustainability Hub focuses on Sustainability and Agenda 2030. The learning path is composed of six thematic areas, each one devoted to a specific topic:-Welcome area for students&amp;#8217; and teachers&amp;#8217; training and orienteering-Global issues area, where are explained &amp;#160;the main issues humans have to face, like climate change, loss of biodiversity, over exploitation of georesources, world population growth and so on.-Georesources and circular economy, where the importance of recycling waste and favoring circular economy in industrial processes is highlighted.&amp;#160;-Environmental footprint indicators area, focused on the tools used to measure the impact of human activities on planet Earth, like carbon-, water-, ecological footprint and the Material Flow Indicator called also Ecological Rucksack-Agenda 2030 and Sustainable Development Goals area, which shows the timeline path to the approval of Agenda 2030 in 2015 and the description of all Sustainable Development Goals and Targets.-Sustainable City Game area represents the final part of the experience, where students/avatars can play a virtual version of a sort of the Goose game trying to solve quizzes and challenges on topics related to the themes treated in the Hub.In each zone multimedia presentations, links to external websites, videos and online games are present. Various interactive 3D objects, when clicked, perform animations that explain abstract concepts, data and facts or allow practical activities such as the calculation of the carbon footprint.&amp;#160;Students explore the learning environment consulting documents and interacting with the 3D objects (gamification approach). At the end of each area students have to solve a quiz to acquire a badge and continue to the next section.The island has been experimented during the pandemic lockdown with 136 students (11-14 years old) that accessed the world from home. Pre- and post-activity questionnaires were administered, showing good results in the cognitive test and appreciation of the activity in the satisfaction survey. In particular, they were happy to make practical activities in the forced online teaching period, improving socialization in the difficult pandemic time, feeling close to their classmates.
Blockchain-Based Remote Construction Monitoring Using UAV in SITL Simulation

Intelligent UAV-based buildings that monitor construction development from afar are quickly becoming an indispensable tool for better resource utilization and citizen service quality. Cameras and sensors can determine if subcontractors are infringing health and safety laws, if they are weary, or if a machine is overheating, among other use cases, with the help of a remote monitoring system. This technique is used to keep track of construction progress from afar. However, the epidemic has propelled remote construction monitoring, as well as remote working in general, into the mainstream. Remote technologies that are utilized to administer their sites boost productivity, time management, and waste reduction. When technology is used to replace the human element in order to increase scalability and reduce risk. A model has been chosen that employs an unmanned quadcopter, the Pixhawk hardware platform, and ArduPilot software and is intended for non-commercial movies and photographs of building sites. This risk in data security during transmission may jeopardize the technology's advantages. As a result, an effective technique for achieving a secure architecture is required, taking into account the limited processing capabilities aboard the UAV agents as well as the distributed aspect of the system.