Reviewerly/exHarmony huggingface dataset:
Ets-Net: Edge Enhanced Two-Stream Network for Medical Image Segmentation

Over the past several years, deep learning methods were widely applied to some medical image processing task such as medical image segmentation and achieved adequate performance. However, universal and effective methods are still needed to segment the details such as the edge of object in some medical image segmentation task. And the medical images nowadays tend to contain richer detail information with the development of the medical imaging technology, which makes the above challenge more obvious. To this end, we propose an edge enhanced two-streams network (ETS-Net) to improve the segmentation near the edge of the object. The proposed network contains a regular stream aiming at getting the contextual information and an edge detail stream aiming at capturing more detail features near the edge. Moreover a multi-scale supplement module (MSSM) is applied to the regular stream to obtain more contextual information from different scales and a context enhance fusion module (CEFM) is used to fuse the features from the two streams. The proposed method is thoroughly validated in three different datasets, and the experimental results demonstrate that the proposed method generally outperforms other state-of-the-art algorithms for various metrics.
Design of Underground Transportation Manipulator Based on Ant Colony Optimization Algorithm

Petroleum, coal mining and chemical industry are the pillar industries of our country and one of the economic sources of national development and social progress. Coal mining requires a lot of manpower and material resources. In order to improve the efficiency of mining and protect people's safety, big technology can be used to design the underground coal mine transportation robotic arm. The purpose of this study is to facilitate the exploration and transportation of coal mines. This paper mainly uses the experimental method and the comparative method to test the ability of the underground coal mine transportation manipulator. The experimental data shows that the actual rotation angle is 178.53, so the robotic arm can work flexibly.
Pelatihan Pembuatan Website Berbasis CMS Di SMK Ciracas

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Implementation of Word Recommendation System Using Hybrid Method for Speed Typing Website

Typing is one of the most frequently done activities in society therefore a medium is necessary to help train typing words that are often mistyped. Methods used in this research are the Content-Based Filtering Algorithm to gather the words that have a similar pattern to the words that are often mistyped based on the user's previous typing records and the Collaborative Filtering Algorithm that uses other users typing pattern to recommend the words. The result of this study shows the Collaborative Filtering Algorithm was able to gather words that are hard to type by the user with an accuracy of 49.2%, dan the Collaborative Filtering able to predict the score on how difficult for the user to type a word with the result of Root Mean Square Error (RMSE) value of 0.82 and with the Root Mean Square Percentage Error (RMSPE) value of 30% from the actual value, and a website which is the combination of the two algorithms with the result of 28% of the total word that is recommended was indeed difficult to type by the user with the typing speed of 103 WPM, and 72.3% for the user that has a typing speed of 39 WPM.
Bit Manipulation

This chapter explores bit manipulation including bit-wise logic, shifting, and rotation.
Implementation of a New E-voting System based on Blockchain using ECDSA with Blind Signatures

The latest research shows the benefits, the impact, and the usage of Blockchain and decentralized systems with a high confidence. Its popularity becomes even higher with the electronic voting systems based on the technology itself. In this paper we propose a new implementation of an electronic voting system based on Blockchain using ECDSA with blind signatures. Additionally, the system is compared with other electronic voting systems based on Blockchain technology. Mainly these types of systems hardly ever fulfill the scalability. Nevertheless, our system has an advantage in comparison with the other systems. Since the idea of the Blockchain technology is to show the flexibility and equal privileges to all nodes, this implementation with Angular and Spring Boot shows that, so everyone can track the chain. To sum up, this implementation can have a good usage in smaller departments, because of the performances and all mathematical operations.
Risk homeostasis and security fatigue: a case study of data specialists

Purpose Organisations use a variety of technical, formal and informal security controls but also rely on employees to safeguard information assets. This relies heavily on compliance and constantly challenges employees to manage security-related risks. The purpose of this research is to explore the homeostatic mechanism proposed by risk homeostasis theory (RHT), as well as security fatigue, in an organisational context. Design/methodology/approach A case study approach was used to investigate the topic, focusing on data specialists who regularly work with sensitive information assets. Primary data was collected through semi-structured interviews with 12 data specialists in a large financial services company. Findings A thematic analysis of the data revealed risk perceptions, behavioural adjustments and indicators of security fatigue. The findings provide examples of how these concepts manifest in practice and confirm the relevance of RHT in the security domain. Originality/value This research illuminates homeostatic mechanisms in an organisational security context. It also illustrates links with security fatigue and how this could further impact risk. Examples and indicators of security fatigue can assist organisations with risk management, creating “employee-friendly” policies and procedures, choosing appropriate technical security solutions and tailoring security education, training and awareness activities.
Prototype to Identify the Capacity in Cybersecurity Management for a Public Organization

Public organizations are subjected to a complex security situation, which can be addressed by permanently strengthening and evaluating their cybersecurity capabilities.The objective of this research is to develop a model to identify the cybersecurity management capacity of public organizations.The deductive method was applied for the review and analysis of criteria, factors and variables related to cybersecurity capacity in public organizations.It resulted in a model to identify the Cybersecurity Management Capacity of public organizations, with its process to assess and categorize organizations according to their level of cybersecurity capacity.It was concluded that public organizations from developed countries in cybersecurity such as Spain have better capacities (greater than 60% CMC) than less developed countries such as Ecuador (less than 60% CMC), due to the cybersecurity context where these organizations operate.To obtain a high level of cybersecurity, public organizations must have the support of the governments of the different political divisions of a country, as well as permanent international collaboration in the field of cybersecurity.
Transfer Learning based Automated Essay Summarization

The human evaluation of essays has become a very time-consuming process as the number of schools and universities has grown. The available software entities are unable to assess the sentiment associated with essays. Thus, we propose a model using Natural Language Processing to assess the essay based on both grammar and sentiment associated with the essay by using linear regression and ULMFiT (Universal Language Model Fine-tuning for Text Classification) models. Evaluation of essay is done in two parts. Part one is on essay grading with respect to grammar with maximum 12 and minimum 0 grade points and in part two score of 0/1 for sentiment analysis with 0 being negative and 1 being positive. The model can be used to score the essay and discard any essay with a score less than a specified value or specified sentiment score.
Freedom of Expression and Misinformation Laws During the COVID-19 Pandemic and the European Court of Human Rights

This article assesses the European Court of Human Rights’ possible responses to post-COVID-19 misinformation laws. These laws are intended to protect society but may become dangerous weapons if used by governments wishing to silence opponents. We identify four categories of speech restrictions that appeared during the COVID-19 pandemic. We then present the recent misinformation laws from Council of Europe member states as well as various potential arguments when cases appear before the Court, and assess their potential weight. We also analyze the expected post-pandemic development of the European Convention on Human Rights’ Article 10 jurisprudence.
Big Data and the Global South

This contribution identifies and critiques the role that dialogue could play as an important tool in bridging the gap between Northern and Southern imaginaries, perceptions and realities on the presence of data mining corporates in Africa, Latin America, the Arab World, Asia and other regions of the world that do not associate themselves with the highly industrialized West. Coming at a time when cynicism over the presence of Silicon Valley and other major data players in Africa and other non-Western regions of the world has been growing, a stakeholders’ dialogue to openly discuss and conscientize all parties on how and why the presence of these major tech players may be deemed unwelcome by locals and other observers.
The Information System Construction of Learning Story Resource Database Based on Big Data and Cloud Computing

Learning stories is a formative evaluation system for children's learning guided by clear educational values. The outstanding feature of “learning stories” is “practicality”, which rooted in the life of kindergarten and in the work of kindergarten teachers. The construction of learning story resource database is essential for preschool education practice. In this article, the author tries to do research in the construction of learning story resource database, aiming to figure out the variety of conceptions and relationships in database construction on learning story.
Visualisation and prediction of user ratings on video games using web analytics

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhammed Haziq Muhammed Nor, Choong-Yeun Liong, Hariharan Gunasekaran; Visualisation and prediction of user ratings on video games using web analytics. AIP Conference Proceedings 8 February 2023; 2500 (1): 020010. https://doi.org/10.1063/5.0128976 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Public maturity assessment on official statistics (PMAOS): Using web analytics to understand insight of perception developed by media and DOSM user needs

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Jamaliah Jaafar, Tengku Noradilah Tengku Jalal; Public maturity assessment on official statistics (PMAOS): Using web analytics to understand insight of perception developed by media and DOSM user needs. AIP Conference Proceedings 8 February 2023; 2500 (1): 020048. https://doi.org/10.1063/5.0110016 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Visual and semi-automated interpretation methods for urban flood detection using SAR Sentinel-1A image: study case in North Aceh Regency, Indonesia

Flood is a natural disaster when causes damage and loss, especially in urban areas that are inhabited by many people and have many properties. The goal of this study is to map rapidly the flooded areas in North Aceh district on January, 5 2022 using multi-temporal SAR Sentinel-1A images through visual interpretation and semi-automated interpretation methods including thresholding and support vector machine (SVM). Visual interpretation relied on multi-polarization image of VH-VV with median filters of 3x3 and 5x5. This study produced the flooded area maps from three methods used with an area of each method: visual interpretation (422.86 ha), thresholding (791.22 ha) and SVM (1084.76 ha). According to the comparison with the result generated from visual interpretation using Planetscope-3A, SVM method has the similar result in the flooded areas with an area of 1001.52 ha. This study also analysed impact of flood event against land cover referring to Sentinel-2 10-Meter Land Use/Land Cover of ESRI and showed that class of cultivated crops is most affected. Results show that this study can be very helpful in damage and loss assessment of flood event and can be reference for urban planning management in order to face the climate change.
Blockchain Security and Calculation Improvements

Every blockchain block header contains an attribute called Merkle-root. It is the calculated hash from all included transactions into a particular block. For example, in Bitcoin, the function used for its calculation is double SHA-256. Few questions are rising: What if another hash algorithm is used? What if another hash algorithm with a different digest size is used? What if a combination of a few different hash algorithms is used? How about reflecting all those changes to the performance compared to the classical usage of the double SHA-256 function. This paper highlights, summarizes, and extends a part of the author's Ph.D. thesis research for blockchain security improvement and, at the same time, speed-up the calculation of block headers. The proposed method divides the calculation of the Merkle-root block header attribute into four groups. The measures in each group are done with just one hash function. Two sequential groups should use the same hash function. The paper's focus is around three of the possible options for arranging the hash functions in the groups. All calculated results from the three types of group arrangements are compared to the usage of the classical double SHA-256 function. The results show that for two group arrangements, the calculation speed is improved, regardless of the used compute infrastructure.
How Predictive Software Engineering Creates Effective Business Solutions Through Custom Software Development

The paper explores the Predictive Software Engineering (PSE) framework and covers the seven principles listed therein, which work together to ensure transparency, controllability, and predictability. The framework provides a reliable, systematic approach for delivering development services. Thus, the article aims to help readers transform their custom software products into successful, high-quality business solutions. Throughout the article, the authors investigate how PSE principles allow be-spoke software development companies to offer transparent products and services that fit budget constraints. The paper examines each principle, one by one, providing strategies, best practices, methodologies, and relevant KPIs. The PSE principles were created taking into account 27+years of software development experience and global engineering best practices.
Research on unsupervised lithology classification based on dimension reduction data: a case of Jimusar area of Xinjiang in China

The classification and extraction of lithology is an important research direction of remote sensing geological survey, but lithology is different from other objects, often because of terrain differences, natural weathering and other factors, spectral differences are not obvious, simple unsupervised classification may not have good results even if the number of iterations increases. For Landsat8 data, we adopt two common data dimension reduction methods, classify the results by K-means unsupervised classification to divide lithology, compare the results with the dimension reduction, and obtain good experimental results, which can be used as a good method to quickly understand the geological situation of the study area.
Research on the Model of Equipment Theory Learning based on Tabu Search-Parallel Genetic Algorithm

In view of the characteristics of teaching and training tasks in military academies, a Tabu Search-parallel Genetic Algorithms is proposed in this paper, and the model of equipment theory learning is studied based on this algorithm. The algorithm can greatly improve the operation efficiency and convergence, and can realize fast retrieval and matching. It plays a key role in the establishment of learning model, and provides technical support for the development of equipment theory learning system in the next step.
Extracting relations from NER-tagged sentences for ontology learning

This paper is a continuation of the research on training and using a neural-network language model for ontology learning. The paper deals with the problem of analyzing domain natural language texts and extracting possible relations between concepts from them. A brief overview of existing approaches to solving this problem is presented. A procedure for extracting relations between concepts based on the analysis of the sentence syntax dependency tree is proposed. The results of evaluating the efficiency of extracting relations using this procedure are considered.
Internet of things with artificial intelligence detection and blockchains of crop availability for supply chain management

Supply chain management is a competitive advantage for logistic companies. Before giving a product to a consumer, confirm availability. In this study, we use internet of things (IoT) input-gathering methods to check crop availability. This technology-enabled service inputs weather, moisture, temperature, and soil fertility data and outputs crop availability. Machine learning monitors and predicts crop availability by predicting weed, water, insect, and animal infiltration and crop growth. The paper suggests blockchain-based AI. Energy savings are shown. Decentralized data storage enhances supply chain visibility and quality, boosting farmer participation. Blockchain enhances supply chain security and provenance. Blockchain protects supply chain data transfers. The simulation compares existing approaches and the proposed machine learning-based blockchain. Crop feature extraction and classification are simulated. The proposed ML model is more accurate, exact, and recalled. By 2050, 70% of food will come from agriculture. On a 9-billion-person planet, manufacturers must be more efficient. This newspaper offers IoT and blockchain to tackle these issues.
SCBERT: Single Channel BERT for Chinese Spelling Correction

Chinese spelling correction (CSC) and BERT pre-training task can both be regarded as text denoising. In this work, to further narrow the gap between the pre-training and CSC tasks, we present a Single Channel BERT (SCBERT) which incorporates semantics, pinyin and glyph of typos to provide effective spelling correction. In model pre-training, we introduce fuzzy pinyin and glyph of Chinese characters and adjust mask strategies to restore the pinyin or glyph information of the “[MASK]” token under certain probabilities. Therefore, we can mask out the char channel of the typo and only provide its pinyin or glyph information in order to reduce the input noise when using our models, as the char information of typos in CSC is a kind of noise. Moreover, we apply synonym replacement and sentence reordering for paraphrasing to improve the accuracy of the correction step. We conduct experiments using widely accepted benchmarks. Our method outperforms state-of-the-art approaches under zero-shot learning condition and achieves competitive results when fine-tuning.
Dictionary-Induced Manifold Learning for Incomplete Multi-modal Fusion

Data missing is a common problem in multi-modal fusion, and existing incomplete multi-modal methods usually only consider the case of two modalities and ignore the semantic information of samples during data recovery. In this paper, we propose dictionary-induced manifold incomplete multi-modal latent space representation, which reconstructs missing views with dictionary to assist consensus representation and captures the local manifold structure with reverse graph regularization. Specifically, we adopt dictionary learning to recover missing data with linear combinations of available samples for latent space alignment, and Laplacian matrix is utilized to embed the structural information of the high-dimensional space into the low-dimensional manifold latent space for optimizing the common representation. The proposed method can not only deal with multi-modal data fusion task, but also recovering missing data by effectively mining the structural information among different modalities. Experimental results demonstrate that our method performs better than other incomplete multi-modal fusion methods.
Co-segmentation Using Graph Convolutional Network

This chapter describes a supervised method for solving the co-segmentation problem for an image pair by training a graph convolutional neural network (GCNN)-based classifier. First each image pair is represented as a weighted graph through superpixel segmentation, and intra-image and inter-image superpixel feature similarities. The network takes the graph as input, and learns to classify each node of the graph into either the common foreground or the background. During training, an additional classifier is used that learns to classify the entire graph into the semantic class the common object belongs to. The entire network is trained end-to-end using a combination of the binary cross-entropy loss and the categorical cross-entropy loss.
Readiness of the University Economic Programs’ Graduates for the First Employment

The article shows an analysis of the readiness of regional universities’ graduates of economic programs for the first employment. An overview of scientific publications on the problems of the first employment has shown the relevance of the issue not only for Russian regions, but also for the economies of other countries. Theoretical approaches to understanding the provision of conditions for the success of the first employment and increasing the readiness of economic programs’ graduates for the transition from educational to work were tested at the empirical level. The survey was conducted among graduates of the Faculty of Economics of Stavropol State Agrarian University. The respondents answered the questions of the questionnaire in electronic format (Google Form). A total of 846 people took part in the survey; they have successfully completed their studies in the last 3 years in the direction of training “Economics”. The survey database was processed in the SPSS Statistics Software (version 23). With a general population of 1487 people, a confidence interval of 95% and a random sample of 846 people, the error in the results of the sampling study is about 2.0%. Conclusions are made showing the possibilities for increasing the readiness of graduates of economic programs of regional universities for the first employment. We proposed the directions of further research of the subject area related to professional socialization and the readiness of university graduates for the first employment.