Reviewerly/exHarmony huggingface dataset:
Digital Literacy and Its Impact on the Inclination towards English Literature: An Analytical Study

Digital literacy is the capacity to find, assess, produce, and communicate information using digital technology.The extensive use of digital technology and the internet in recent years has significantly changed how people read, write, and create literature.This has significantly affected the propensity for English literature.People can now easily access a huge amount of English-language literature from all around the world thanks to digital literacy.Additionally, the availability of internet resources, like e-books and audiobooks, has increased the accessibility of literature to a larger audience.Additionally, digital platforms where readers may exchange and discuss their passion for English literature have also been made possible thanks to digital literacy.Connecting with people who have similar literary interests has become simpler thanks to social media networks like Goodreads, for instance.Overall, by making English literature more accessible and enabling the development of reader communities, digital literacy has had a significant impact on the propensity toward it.This impact will probably only get stronger in the future as digital literacy develops even further.
Virtual Reality versus Augmented Reality

Technology is developing quickly, making a lot of things that were before impossible feasible.Virtual reality (VR) and augmented reality (AR) are two examples of the cutting-edge technology that, only a few years ago, were considered science fiction.However, they are now an essential component of everyday life.The distinction between augmented reality & virtual reality is the one that causes the most misunderstanding in the world.While Virtual Reality is entirely immersive, augmented realities is a synthetic, computer-simulated reality or reproduction of a real-time setting that allows a user to interact with the reproduced actual settings.Optical head-mounted displays (OHMDs), which combine virtual reality (VR) or augmented reality (AR), are close to becoming common gear that consumers may purchase and use to do 3D tasks.Front-facing cameras are a feature of certain OHMDs that enable augmented reality (AR) or virtual reality (VR) capability.Seeing the actual world may alter interaction with virtual items in addition to preventing collisions with the surroundings.It is unknown if virtual reality (VR) or augmented reality (AR) provides any advantages over one another for almost all jobs.One of the most crucial areas of research in the present-day IT field is the development of augmented reality as well as virtual reality technologies.These two technologies have been used in a variety of fields, including education, healthcare, construction, military operations, and entertainment, and research on their technical aspects is progressing concurrently with research on their evaluation and the development of user-centered optimisation programmes.The starting place for this article was the real estate show.The authors selected the appropriate assessment index as well as quantification technique through comparisons with virtual reality (VR) and augmented reality (AR) in the physical display field, compared the evaluation results of each technology, and then created a user experience evaluation model for each.
Uji Kadar Zat Besi (Fe) Dalam Sediaan Obat Tambah Darah (Ferro Fumarat) Dengan Metode Kompleksometri

Iron is a very important element to form hemoglobin (Hb). Blood supplement tablets which are often consumed in the process of adding iron are ferro fumarate, so this study aims to determine the levels of iron (Fe) contained in blood supplement drugs (ferro fumarate). This type of research was a laboratory experiment using the complexometric method and a complex forming substance, namely disodium ethylene diamine tetra acetate (Na2EDTA). A sample of blood supplement tablets (ferro fumarate) which was prepared in 10 tablets was made into powder and then 150 mg was taken and titrated with Na2EDTA as the titrant and using the Erichrome Black T (EBT) indicator resulting in a purple titration end point turning blue. The results showed that the level of iron (Fe) in the blood supplement sample (ferro fumarate) was 200%.
A Robust Netrosophic Clustering Based Raven Routing Optimization to Alleviate Void Hole and Energy Depletion in IoT WSN

Wireless Sensor Networks (WSNs) find extensive utilisation in diverse domains, including but not limited to environmental monitoring, industrial control, and healthcare. Wireless Sensor Networks (WSNs) have a crucial function in gathering and relaying information from sensor nodes to a central node or sink in various applications. Wireless Sensor Networks (WSNs) encounter various obstacles that can have a significant impact on their performance and lifespan. These challenges include but are not limited to limited battery life, void holes, and energy depletion.&#x0D; Several routing algorithms have been suggested in scholarly works to tackle these concerns, such as clustering-based routing, data-centric routing, and location-based routing. Nonetheless, a majority of these algorithms possess constraints and may not be appropriate for every situation.&#x0D; The present study introduces a methodology called Rncrro (Robust Netrosophic Clustering Based Raven Routing Optimisation) that aims to mitigate the issues of void holes and energy depletion in Wireless Sensor Networks (WSNs). The proposed approach utilises Netrosophic Clustering and Raven Optimisation algorithms in tandem to enhance the efficacy and resilience of the routing mechanism.&#x0D; The significance of this study is rooted in its capacity to surmount the constraints of current routing algorithms and furnish a superior and dependable resolution for Wireless Sensor Networks (WSNs). The proposed methodology has the potential to enhance the performance and longevity of Wireless Sensor Networks (WSNs) by mitigating void holes and energy depletion. This, in turn, can result in more efficient and dependable data collection and transmission across diverse applications.
Secure E- Voting System Using Encryption Technique

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; The potential of electronic voting to improve the efficiency and effectiveness of the voting process is immense. However, its security and privacy are still a major concern. Traditional methods of securing such systems, such as digital signatures and public key cryptography, are not always ideal when it comes to safeguarding the confidentiality of ballots. In this paper, we introduce a novel method for securing electronic voting ballots that utilizes homomorphic encryption. This method allows for the computation of ballots without needing decryption. The proposed e-voting system is composed of three servers. The first one is an e-voting server that stores the voter's information before it is sent to the tallying or decryption server. The latter two are used to calculate the total vote count and decrypt it. To evaluate the performance of this proposed system, we performed a series of tests on its various components. These included its execution time, memory usage, CPU usage, and communication overhead. The results of the tests revealed that the proposed system is secure and can maintain acceptable performance levels. The proposed system's accuracy and robustness are comparable to that of conventional e-voting systems. Its ability to withstand attacks is greatly enhanced by implementing homomorphic encryption. The proposed e-voting system we presented exhibited the security and privacy benefits of homomorphic encryption. By enabling computations on encrypted data without needing decryption, it helps protect the ballot's confidentiality and provides reliable and accurate results.&#x0D; &#x0D; &#x0D; &#x0D;
Design Of Energy Efficient and Secure Routing Protocol for Wsn in Iot

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; This paper proposes an energy-efficient and secure routing system for wireless sensor networks (WSNs) in the Internet of Things (IoT). The proposed routing protocol improves packet delivery ratio, end-to-end latency, and network lifetime over existing protocols. The protocol under consideration reduces node energy consumption, increasing the network's lifetime, while ensuring data security. Simulation studies using several metrics evaluate the proposed procedure. The recommended protocol outperforms existing protocols in energy efficiency and security. The protocol under discussion has several IoT applications, including smart agriculture, healthcare monitoring, and environmental monitoring. It ensures reliable and secure data transfer. This paper introduces a novel routing system that addresses energy saving and security in wireless sensor networks and the Internet of Things (IoT).&#x0D; &#x0D; &#x0D; &#x0D;
D-SWE: Data-driven discovery of a seismic wave equation

Summary We propose a data-driven method for the wave equation D-SWE, which includes machine learning and genetic algorithm (GA), capable of discovering a wave equation from the observed spatial-temporal wavefields. D-SWE first trains a neural network (NN) in a supervised fashion to establish the mapping between the spatial-temporal locations (x, y, z, t) and observation displacement wavefield function u(x, y, z, t). The trained NN serves to generate meta-data and provide the time and spatial derivatives of the wavefield (e.g., utt and uxx) by automatic differentiation. Then, the overcomplete library, including candidate function terms, is produced by using GA. Finally, to choose the proper candidate terms and determine the accurate form of a seismic wave equation, we use a physics-informed information criterion to evaluate precision and parsimony of potential equations and determine the best structure of a wave equation, and we further train a physics-informed neural network to identify the corresponding coefficients of each functional term. Examples in discovering the 2D acoustic wave equation are used to validate the feasibility and effectiveness of the proposed method. Results demonstrate that D-SWE enables the identification of the general form of the acoustic wave equation, also, is robust to noisy and sparse wavefield data.
Attribute Constraint-based 3D Res-UNet Network, Probability Grading and Fusion Display for Fault Reliability Evaluation

Summary There are many difficulties in interpreting and describing complex fault block reservoirs, such as complex tectonic anomalies, low signal-to-noise ratio, unclear fault points, and the development of a large number of small micro-faults. Conventional seismic interpretation relies too much on the experience of geologists, which is subjective and time-consuming. Intelligent geophysical techniques based on big data analysis are becoming popular. In this paper, we introduced a multi-layer depth-supervised mechanism based on 3D Res-UNet + attribute constraints to construct a 3D depth-supervised network for intelligent prediction of faults. Finally, a comprehensive evaluation of fault reliability is done by probability grading and fusion display. The actual data are applied well, which verifies the effectiveness and feasibility of the method in this paper.
Image search at work? An AI pipeline to classify and geo-locate figures from corporate subsurface documents.

Summary The presentation will focus on the application of world class artificial intelligence technologies to reveal and extract knowledge from ConocoPhillips datasets. For this project, conducted by Kadme and ConocoPhillips, on top of the Lumin platform, Kadme has entered a collaboration with Inmeta, a consulting company part of the Crayon group. One of the objectives of the project was to gain a better understanding of images contained within the unstructured data realm and being able to utilise the enriched data for a quicker understanding of their context. In fact, not all knowledge can be located using search terms and metadata. A domain expert can learn a lot about a document just by looking at the figures contained within it. The solution must be capable of scaling to 10s of millions of figures, from dozens of different sources. The model must also remain relevant based on feedback from the end users, so retraining workflows are required. Further, the solution and the pipeline must be repeatable in many different environments. These subsequent deployments may wish to use different figure classes, so a workflow for automated bootstrapping of a new model also must be created.
Mineral System Modelling of Base Metal Systems - Key Challenges and Opportunities

Summary Success in minerals exploration has declined in recent years, with fewer quality discoveries and increasing discovery costs on a per unit basis. This reflects to a large extent the increasing exploration maturity of outcropping and near-surface environments in established metallogenic provinces and districts. Minerals exploration is becoming increasingly technically challenging and more expensive. More effective exploration targeting across a range of scales is therefore essential to increase success rates and potentially reverse or slow the trend of increased discovery costs. Understanding the mineral system more holistically enables us to de-risk exploration investment decisions and to better predict the economic potential of an area under exploration. Only when we consider and quantify all system-critical processes over geological time, that a robust validation of a mineral system is possible and allows us to test various system hypotheses. The resulting mineral system models then allow us to define the key risk factors for an effective resource assessment. Mineralization events require a working mineral system over a specific time period. Here we combine “basin modeling”, through geological time, with “reactive transport modeling”. This allows us to simulate mineral systems to quantitatively assess the impact of uncertainties and the effectiveness of the metal concentration processes.
Intelligent seismic horizon picking based on Gabor wavelet transform of seismic attribute

Summary Seismic horizon picking is the foundation of structural interpretation and seismic inversion. Current intelligent seismic horizon picking methods have not utilized the inherent characteristics of seismic data and their attibutes to supervise the crucial step. Therefore, we propose a new intelligent seismic horizon picking method based on seismic signal feature extraction. This method does not use the peaks or troughs of the seismic signal and instead focuses on the energy change in the seismic signal at the stratum interface, extracting the characteristics of the energy change to horizon picking. We apply sliding window preprocessing to the seismic signal to convert the seismic signal to a spectrum analysis energy view by performing operations such as short-time Fourier transforms (STFTs) on the preprocessed results. On this basis, the local texture information of the spectrum analysis view can be processed by the Gabor wavelet transform to obtain the Gabor attribute features of the seismic signal and seismic Gabor attribute solves the problem of single feature of amplitude data. Finally, the support vector machine, random forest, extreme gradient boosting models and deep residual shrinkage network are used to horizon picking. The results show that accuracy and root mean square error of horizon picking are satisfactory.
Context-Aware Mobile Apps: Identifying Tools for Creation Approaches

When developing context-sensitive mobile applications there are several considerations that must be considered such as: being able to express the relevant concepts and subsequently generate the required applications. In this area it is worth mentioning that the technology used must allow expressiveness related to factors such as category, relevance, combination, type of configuration and type of execution and generate a context-sensitive mobile application, framed within the characteristics that are currently in demand and using the most appropriate tools for it. This paper analyzes the particularities of developing a context-sensitive mobile application through a Metamodel creation process, then the generation of a Visual D.S.L with Obeo Sirius, its treatment with Xtext, the generation of a mobile application with Acceleo and finally the definition of the graphical user interface GUI with the help of Ionic and Capacitor, which will allow to exploit the functionalities of the mobile. That is to say, the process to establish the appropriate tools for the creation of context-sensitive mobile applications is shown
Multi-Scale Interaction Network for Low-Light Stereo Image Enhancement

Low-light stereo image enhancement (LLSIE) is a task that improves the visual quality of stereo images captured in low-light environment. The core idea of LLSIE is to exploit the cross-view cues for enhancing the stereo image pairs. However, LLSIE is still a challenging task due to the perspective difference and insufficient information interaction between left and right views. In this paper, we propose a novel Multi-Scale Interaction Network (MSINet) for LLSIE. To be specific, we present a new multi-scale stereo Interaction module (MIM), which can promote the multi-scale cross-view information flow. Firstly, MIM extracts multi-scale features from both the left and right views. Then, the features from two views are fused in different scales, so that the information from stereo image pairs can be fully exchanged. Compared to the existing parallax attention module (PAM), our MIM explores the cross-view interaction in multiple scales with less computational cost more sufficiently. Besides, we also develop a new Spatial Gating Block (SGB) for refining intra-view features. By incorporating the long residual and spatial gating mechanism, SGB is capable of completing dynamic spatial modeling and obtaining strong feature representations. Extensive experiments on the Flickr1024, KITTI 2015 and Middlebury datasets show that the proposed MSINet reaches SOTA performance and obtains better visual results than other compared methods.
Impact of Emerging Hardware on Security and Privacy

The articles in this special issue focus on ongoing research efforts in the development, use, and evaluation of emerging hardware features and techniques to improve system security. We have seen the emergence of new hardware features to improve software security by limiting memory access within an address space, such as Intel’s memory protection keys (MPKs) and extended page table (EPT) switching, among others. Researchers have also proposed hardware-assisted roots of trust using trusted execution environments (TEEs), such as Intel’s software guard extensions (SGXs), so some questions are what the utility is of such techniques and what limitations they still face. The papers in this special issue provide guidance in these areas, which can be helpful in examining future research directions and future applications of such techniques.
Content-based filtering algorithm in social media

Content-based filtering is a recommendation algorithm that analyzes user activity and profile data to provide personalized recommendations for content that matches a user's interests and preferences. This algorithm is widely used by social media platforms, such as Facebook and Twitter, to increase user engagement and satisfaction. The methodology of content-based filtering involves creating a user profile based on user activity and recommending content that matches the user's interests. The algorithm continually updates and personalizes the recommendations based on user feedback, and incorporates strategies to promote diversity and serendipity in the recommendations. While content-based filtering has some limitations, it remains a powerful tool in the arsenal of social media platforms, offering efficient content discovery and personalized user experiences at scale.
Face detection by using Haar Cascade Classifier

the Haar Cascade Classifier is a popular technique for object detection that uses a machine-learning approach to identify objects in images and videos. In the context of face detection, the algorithm uses a series of classifiers that are trained on thousands of positive and negative images to identify regions of the image that may contain a face. The algorithm is a multi-stage process that involves collecting training data, extracting features, training the classifiers, building the cascade classifier, detecting faces in the test image, and post-processing the results to remove false positives and false negatives. The algorithm has been shown to be highly accurate and efficient for detecting faces in images and videos, but it has some limitations, including difficulty in detecting faces under challenging lighting conditions or when the faces are partially occluded. Overall, the Haar Cascade Classifier algorithm remains a powerful and widely-used tool for face detection, but it is important to carefully evaluate its performance in the specific context of each application and consider using more advanced techniques when necessary.
A Consumer-Good-Type Aware Itemset Placement Framework for Retail Businesses

It is a well-established fact that strategic placement of items on the shelves of a retail store significantly impacts the revenue of the retailer. Consumer goods sold in retail stores can be classified into essential and typically low-priced convenience items, and non-essential and high-priced shopping items. Notably, the lower-priced convenience items are critical to ensuring consumer foot-traffic, thereby also driving the sales of shopping items. Moreover, users typically buy multiple items together (i.e., itemsets) to facilitate one-stop shopping. Hence, it becomes a necessity to strategically index and place itemsets that contain both convenience items and shopping items. In this regard, we propose a consumer-good-type aware and revenue-conscious itemset indexing scheme for efficiently retrieving high-revenue itemsets containing both convenience and shopping items. Moreover, we propose an itemset placement scheme, which exploits our indexing scheme, for improving retailer revenue. Our performance study with two real datasets shows that our framework is indeed effective in improving retailer revenue w.r.t. a reference scheme.
Metode K-Means Clustering Untuk Pemetaan Gedung Olah Raga Badminton Di Soloraya

There are many Badminton Sports Buildings in the Soloraya area, including Sukoharjo Regency, Boyolali Regency, Klaten Regency, Wonogiri Regency, Karanganyar Regency, Sragen and Solo. Each Badminton Gymnasium has different conditions, starting from varying rental prices, as well as the various facilities offered by each Badminton Sports Hall. The purpose of this research is to help people in Soloraya find suitable badminton sports halls, an information system is needed that can explain the mapping of badminton sports halls in Soloraya. This study uses the K-Means and GIS methods to solve the problem of grouping badminton sports halls based on their categories. The result of this research is a geographic mapping information system to make it easier for people to find badminton courts that match the criteria of people in Soloraya.
ONLINE DIGITAL CHEQUE CLEARANCE AND VERIFICATION SYSTEM USING ETHERUM BLOCKCHAIN

India uses the Cheque Truncation System (CTS), an image-based check clearing system. The semi-manual approach has some drawbacks and can take up to 3 working days in India to clear an inter-bank national check. Due to the shortcomings of this system, commercial banks and cheque users must have access to an effective and secure system that can clear a check in less than 24 hours while maintaining the systems integrity and anonymity. This study presents an automated solution to the aforementioned problems that might be implemented by any commercial bank in India. All banks interested in participating in this framework must connect to the proposed blockchain-based system, which is the foundation of the proposed system.