Reviewerly/exHarmony huggingface dataset:
A secure image protection algorithm by steganography and encryption using the 2D-TSCC*

Based on the two-dimensional (2D) tan-sin-cos-coupling (2D-TSCC), a new image protection method is designed, this method includes steganography and encryption. First, a 2D-TSCC system is designed. The 2D-TSCC has a large parameter space in a hyperchaotic state. The chaotic trajectory fills the entire window. The chaotic sequence generated by the 2D-TSCC has a good pseudorandomness, so it can be used in steganography and encryption. Then, the amount of information contained in each bit of the cover image is analyzed, and the three bits which carry the least amount of information are selected. The secret image is hidden in these three bits base on the 2D-TSCC. Finally, the carrier image is scrambled and diffused by the 2D-TSCC. The ciphertext is generated in this way. Send the ciphertext to the recipient through channel transmission, and the recipient obtains the secret image by decrypting twice.
Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

Constructing a knowledge graph with mapping languages, such as RML or SPARQL-Generate, allows seamlessly integrating heterogeneous data by defining access-specific definitions for e.g., databases or files. However, such mapping languages have limited support for describing Web APIs and no support for describing data with varying velocities, as needed for e.g., streams, neither for the input data nor for the output RDF. This hampers the smooth and reproducible generation of knowledge graphs from heterogeneous data and their continuous integration for consumption since each implementation provides its own extensions. Recently, the Web of Things (WoT) Working Group released a set of recommendations to provide a machine-readable description of metadata and network-facing interfaces for Web APIs and streams. In this paper, we investigated (i) how mapping languages can be aligned with the newly specified recommendations to describe and handle heterogeneous data with varying velocities and Web APIs, and (ii) how such descriptions can be used to indicate how the generated knowledge graph should be exported. We extended RML’s Logical Source to support WoT descriptions of Web APIs and streams, and introduced RML’s Logical Target to describe the generated knowledge graph reusing the same descriptions. We implemented these extensions in the RMLMapper and RMLStreamer, and validated our approach in two use cases. Mapping languages are now able to use the same descriptions to define the input data but also the output RDF. This way, our work paves the way towards more reproducible workflows for knowledge graph generation.
Sailing Data Visualization in Augmented Reality: Systematic Review, Issues, and Perspectives

Abstract Today's sailing visualization instruments struggle to cope with the increasing number of onboard sensors, automation, artificial intelligence, and the high dynamism of the crew. Current solutions scatter multiple displays all over the boat, both inside and outside, potentially reducing usability and increasing costs. This work presents a systematic review of augmented reality (AR) as an integral solution for sailing data visualization, which revealed four scientific papers and eight commercial products. We analyzed the publication type, the AR hardware, what and how information is presented using AR, the validation method (if present), and the technological readiness. We defined the technical requirements needed for the AR device for sailing and distinguished a first generation of commercial solutions based on head-up displays from a second one based on proper augmentation with stereo head-mounted displays. The displayed information elements are limited in number and are commonly 2-D graphics (e.g., text and symbols) with a screen-relative frame of reference (as opposed to body- or world-relative). The most visualized elements are heading (10) followed by wind direction (8), boat speed (7) compass (7), and wind speed (7). We also found that most of the solutions lack critical evaluation. We conclude that AR has the potential to integrate sailing data from different systems and to improve accessibility, situation awareness, and safety for a large group of users. However, the main limitations are the lack of AR head-mounted displays suitable or adaptable for sailing conditions, an extensive exploration of 3-D interface elements, and an adequate number of usability studies in the scientific literature.
Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM

In order to ensure flight safety and eliminate hidden dangers, it is very important to detect aircraft track anomalies, which include track deviations and track outliers. Many existing track anomaly detection methods cannot make full use of multidimensional information of the relevant track. Based on this problem, an aircraft track anomaly detection method based on the combination of the Multidimensional Outlier Descriptor (MOD) and the Bi-directional Long-Short Time Memory network (Bi-LSTM) is proposed in this paper. Firstly, track deviation detection is transformed into the track density classification problem, and then a multidimensional outlier descriptor is designed to detect track deviation. Secondly, track outliers detection is transformed into a prediction problem, and then a Bi-LSTM model is designed to detect track outliers. Experimental results based on real aircraft track data indicate that the accuracy of the proposed method is 96% and the recall rate is 97.36%. It can detect both track deviation and track outliers effectively.
Short video recommendations based on analytic hierarchy process and collaborative filtering algorithm

In video-sharing mobile apps, personalized recommendation systems can help users quickly finding the mobile videos of interest through analyzing videos characteristics and user behaviors, and thus have been widely applied in many applications. Collaborative filtering is one of the most popular technologies in the personalized recommendation systems. To improve the performance of personalized recommendation services, this paper proposes a new personalized collaborative filtering algorithm recommendation by combining the analytic hierarchy process, which captures various video information and user behaviors, and comprehensively evaluates users' interest in videos. Experiments on NetEase Cloud Music app cloud village dataset shows that the proposed algorithm outperforms traditional method in recall rate, accuracy rate and coverage rate, offering more accurate personalized recommendations.
Effect of Project-Based Learning Through Blended Learning on Website Design Skills

This study aims to determine the website design skills of Information Technology and Computer Education Program (P.TIK) at IKIP PGRI Pontianak students and to determine the differences in website design skills of P.TIK students using Blended Learning (BL) and face-to-face learning (F2F) in website design project. The research method used in this study is an experimental method with a quasi-experimental form. The sample in this study amounted to 63 students who were divided into two groups namely classes that apply BL and classes that apply F2F in implementing website design projects. Data collection techniques in the form of measurement techniques using tools in the form of a portfolio. Data analysis techniques using descriptive statistics and inferential statistics. The results showed that the website design skills of students after working on a website design project were in the good category, and there were significant differences in website design skills between the groups applying BL and the groups applying F2F
Single image super resolution based on multi-scale structure and non-local smoothing

Abstract In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K -means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.
Author placement in Computer Science: a study based on the careers of ACM Fellows

The order in which authors of a scientific paper place their names on the byline follows in many research fields some implicit rules. In most fields, the first author is considered to be the one who contributed most to the intellectual effort described in the paper. Additionally, the last author is normally the most senior researcher and in many situations the contribution to the paper is more indirect. In this manuscript, we intend to analyse the evolution of the positions of computer science (CS) researchers on the bylines of scientific papers throughout their careers. In particular, this bibliometic study considers the Association for Computing Machinery (ACM) Fellows (the most prestigious members) that present a long and rich publication record. Our hypothesis is that young CS authors tend to have their names placed in the first positions of the bylines, while senior CS researchers are often considered as last authors. Several statistical analyses were conducted by using biblometric data collected from ACM Fellows and other CS researchers. Overall, the obtained results do confirm our initial hypothesis.
Learning media for the transliteration of Latin letters into Bima script based on android applications

Preservation of Indonesian culture is an important thing that must be considered. One of the efforts that have been made in preserving culture is technological development implementation. In cultural preservation, based on data obtained from 45 respondents from the Bima community in Indonesia, 34.9% of the community does not understand the Bima script. It means that only 65.1% of the community can understand Bima script. This condition will continue to grow if there is no effort made to preserve the Bima script. Because people should have a comprehensive knowledge of the Bima script to understand its usage in writing and reading activities, people tend to have less desire to learn the Bima script. Mataram University is trying to develop creative and innovative learning products to learn the Bima script learning model in an interactive application using a smartphone to translate Latin letters into the script. This development aims to facilitate the process of learning Bima characters for the community. The method that has been used is to apply a string replacement algorithm based on the Bima script rules in the reference book containing the ancient Bima script. According to the experiment result, the alpha test value from 31 respondents is 99.36%, and the Beta test value from 45 respondents Bima society is 91.50%. It can be concluded that this application is feasible as a learning medium.
A Comparative Study Between Containerization and Full-Virtualization of Virtualized Everything Functions in Edge Computing

The astronomical amount of data is generated today. The currents computer network architectures have trouble managing high bandwidth, dispersion, low geographical latency, and privacy. Edge Computing (EC) aims to solve these problems by allowing data processing to be done close to end users. Network Function Virtualization promises to simplify the network service creation workflow by turning traditional network functions. This work presents a detailed comparative study between containers and virtual machines. To this end, we present an architecture and implementation of a management and orchestration platform (MANO) for Virtualized Everything Functions (VxFs), unified across bare-metal servers as well as low-cost devices. A detailed comparison of the performance of VxFs based on containers and virtual machines is made, represented respectively by the LXD and KVM platforms on a Raspberry Pi 4. The results show that containerization is not an adequate solution for all cases. Instead, it can be thought of as a complementary solution to be used with full-virtualization making the most of these approaches.
The Classification of Short Scientific Texts Using Pretrained BERT Model

Automated text classification is a natural language processing (NLP) technology that could significantly facilitate scientific literature selection. A specific topical dataset of 630 article abstracts was obtained from the PubMed database. We proposed 27 parametrized options of PubMedBERT model and 4 ensemble models to solve a binary classification task on that dataset. Three hundred tests with resamples were performed in each classification approach. The best PubMedBERT model demonstrated F1-score = 0.857 while the best ensemble model reached F1-score = 0.853. We concluded that the short scientific texts classification quality might be improved using the latest state-of-art approaches.
Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study

The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require a large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data, such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset. Unfortunately, the inherent differences in the combined data, known as batch effects, often hamper learning a model. To mitigate this issue, techniques such as multi-source domain adaptation [Multi-source Domain Adversarial Networks (MSDA)] aim at learning an effective classification function that uses (learned) domain-invariant latent features. This article analyzes and compares the performance of various popular MSDA methods [MDAN, Domain AggRegation Networks (DARN), Multi-Domain Matching Networks (MDMN), and Moment Matching for MSDA (M 3 SDA)] at predicting different labels (illness, age, and sex) of images from two public rs-fMRI datasets: ABIDE 1and ADHD-200. It also evaluates the impact of various conditions such as class imbalance, the number of sites along with a comparison of the degree of adaptation of each of the methods, thereby presenting the effectiveness of MSDA models in neuroimaging-based applications.
PREDICTION OF HOTEL BOOKING CANCELLATION USING DEEP NEURAL NETWORK AND LOGISTIC REGRESSION ALGORITHM

Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes
Heterogeneity for the Win: One-Shot Federated Clustering

In this work, we explore the unique challenges -- and opportunities -- of unsupervised federated learning (FL). We develop and analyze a one-shot federated clustering scheme, $k$-FED, based on the widely-used Lloyd's method for $k$-means clustering. In contrast to many supervised problems, we show that the issue of statistical heterogeneity in federated networks can in fact benefit our analysis. We analyse $k$-FED under a center separation assumption and compare it to the best known requirements of its centralized counterpart. Our analysis shows that in heterogeneous regimes where the number of clusters per device $(k')$ is smaller than the total number of clusters over the network $k$, $(k'\le \sqrt{k})$, we can use heterogeneity to our advantage -- significantly weakening the cluster separation requirements for $k$-FED. From a practical viewpoint, $k$-FED also has many desirable properties: it requires only round of communication, can run asynchronously, and can handle partial participation or node/network failures. We motivate our analysis with experiments on common FL benchmarks, and highlight the practical utility of one-shot clustering through use-cases in personalized FL and device sampling.
Extractive Research Slide Generation Using Windowed Labeling Ranking

Presentation slides describing the content of scientific and technical papers are an efficient and effective way to present that work.However, manually generating presentation slides is labor intensive.We propose a method to automatically generate slides for scientific papers based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites.The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization.Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures importance and novelty of sentences by combining semantic and lexical features within a sentence window.Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score.
Detection of Effective Porosity and Permeability Zoning in an Iranian Oil Field Using Fractal Modeling

Identification and delineation of different zones in oil fields are among the fundamental tasks in petroleum explorations. Fractal methods are useful tools for such purposes. The aim of this paper is to conduct a comparative study of Concentration-Area (C-A) and Number-Size (N-S) fractal models to separate effective porous and permeable zones based on core logging samples collected from one of the oilfields in southern Iran. However, permeability and porosity threshold values were calculated based on the C-A and N-S log-log plots. A comparison between the C-A and N-S fractal results showed that the C-A method is more compatible with reality, and it is capable of separating permeable and porous zones in this oilfield.
A Data Privacy Approach Using Shamir’s Secret Scheme in Permissioned Blockchain

Information sharing is the basic necessity for advancement in research works. Information sharing in the existing technology is relied on trusted third parties (TTP). There is a lack of trust for transparency, security, and immutability in these systems because of the participation of TTP. For these issues, the blockchain technology can be used to secure the information sharing in the decentralized network by maximizing the advantages of the decentralized file system (DFS). Blockchain faces serious issues with data privacy, which is an obstacle for the application of the blockchain. This paper introduces the usage of the Shamir’s secret sharing (SSS) system to the blockchain. The proposal resists passive and disguise attacks. The proposed systems with SSS have results with lesser computational time compared to the advanced encryption standard (AES) 128. In non-transactional scenarios, the proposed system is functional and effective.
RETRACTED: A Brief Analysis of the Application of English Language in British and American Literature Based on Artificial Intelligence

Abstract artificial intelligence (AI), abbreviated in English, is the research and development of human body to produce new technologies to simulate and expand the application of human intelligence in machines. The ultimate goal of artificial intelligence is to be able to produce robots that are not different from human beings and only listen to human beings. To achieve this technology, the most important thing is to be able to analyze and study the substantive operation function of human intelligence. The most ideal way of language learning is to be able to personally visit the language environment, feel the atmosphere of the language culture, and have in-depth communication with the language owner. However, in reality, such an idealized state is impossible to exist, so literary works have become more ideal language and cultural materials. Both British and American languages use English to communicate, although both belong to English, the cultural differences lead to their different ways of expression. This paper first makes a simple analysis of the artificial intelligence system, then analyzes the differences between British and American literary works, and then interprets and analyzes the application of artificial intelligence system in British and American literary works in detail.
Sports Video Augmented Reality Real-Time Image Analysis of Mobile Devices

Sports video is loved by the audience because of its unique charm, so it has high research value and application value to analyze and study the video data of competition. Based on the background of football match, this paper studies the football detection and tracking algorithm in football game video and analyzes the real-time image of real-time mobile devices in sports video augmented reality. Firstly, the image is preprocessed by image graying, image denoising, image binarization, and so on. Secondly, Hough transform is used to locate and detect football, and according to the characteristics of football, Hough transform is improved. Based on the good performance of SIFT algorithm in feature matching, a football tracking algorithm based on SIFT feature matching is proposed, which matches the detected football with the sample football. The simulation results show that the improved Hough transform can effectively detect football and has good antijamming performance. And the designed football tracking algorithm based on SIFT feature matching can accurately track the football trajectory; therefore, the football detection and tracking algorithm designed in this paper is suitable for real-time football monitoring and tracking.