Reviewerly/exHarmony huggingface dataset:
Context-Driven Feature-Focusing Network for Semantic Segmentation of High-Resolution Remote Sensing Images

High-resolution remote sensing images (HRRSIs) cover a broad range of geographic regions and contain a wide variety of artificial objects and natural elements at various scales that comprise different image contexts. In semantic segmentation tasks based on deep convolutional neural networks (DCNNs), different resolution features are not equally effective for extracting ground objects with different scales. In this article, we propose a novel context-driven feature-focusing network (CFFNet) aimed at focusing on the multi-scale ground object in fused features of different resolutions. The CFFNet consists of three components: a depth-residual encoder, a context-driven feature-focusing (CFF) decoder, and a classifier. First, features with different resolutions are extracted using the depth-residual encoder to construct a feature pyramid. The multi-scale information in the fused features is then extracted using the feature-focusing (FF) module in the CFF decoder, followed by computing the focus weights of different scale features adaptively using the context-focusing (CF) module to obtain the weighted multi-scale fused feature representation. Finally, the final results are obtained using the classifier. The experiments are conducted on the public LoveDA and GID datasets. Quantitative and qualitative analyses of state-of-the-art (SOTA) segmentation benchmarks demonstrate the rationality and effectiveness of the proposed approach.
A Contrastive Study on Employee Performance and Benchmarking in the Selected IT Industry

Benchmarking is agitating the employee’s target and performance of the IT industry. The study analyses the employee performance and benchmarking in a disparate sectors in a specified roles. Employee performance is mainly based on the skills, talents, and leadership are the major part to achieve the benchmark in the IT industry. Especially in IT sectors is figuring out the statistical range as 80% globally. Currently Industrial Technology having a lot of workforce and commitments towards their owned employees in various aspects. So the employee performance becomes a part to achieve the benchmark in the IT industry. Hence, the employees are ability to meet their targets. Therefore, the paper looks into the need for benchmark to motivate their performance in an effective and efficient way. The researcher attempts to find the major tactics of the employee performance to beat their targets in a several aspects such as measuring, understanding, verifying, equating and achieving. The study focus on the research objectives as to analyze the perception of employee performance and benchmarking with the help of statistical tool such as Mean Score Analysis, factor analysis for the period of 2018-2022. The study adopts stratified random sampling with the sampling population of 500 covered by selected IT employees. The sample size is restricted on the basis of pilot study as 0.50 percent respectively. Benchmarking is the high tech target of each employee should perform their own talents, skills and achievement in the specific industry. The industry is providing the white collar jobs with comparative salary, international mobility based on knowledge centric skills possession in comfortable environment. Employee performance focuses the creative support and lead to better workforce environment.
Deep Reinforcement Learning in Competing Cognitive Tactical Networks

These instructions give you guidelines for preparing papers for DRP. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further at DRP. Paper titles should be written in uppercase and lowercase letters, not all uppercase. Avoid writing long formulas with subscripts in the title; short formulas that identify the elements are fine (e.g., "Nd-Fe-B"). Do not write “(Invited)” in the title. Full names of authors are preferred in the author field, but are not required. Put a space between authors’ initials. The abstract must be a concise yet comprehensive reflection of what is in your article. In particular, the abstract must be self-contained, without abbreviations, footnotes, or references. It should be a microcosm of the full article. The abstract must be between 100 - 300 words. Be sure that you adhere to these limits; otherwise, you will need to edit your abstract accordingly. The abstract must be written as one paragraph, and should not contain displayed mathematical equations or tabular material. The abstract should include three or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases as this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.
<scp>Multilayer privacy‐preserving</scp> model for security and privacy access control in edge computing

Summary In a recent scenario of a computing environment, the Internet of Things (IoT) provides a greater impact in several applications and services. For that Edge Computing techniques are effectively incorporated, in which the sensitive data are stored on servers. As a result, edge computing allows for data storage and operation at the network's edge to emerge as optimistic advancement. Regardless, the distinctive characteristics of edge computing including real‐time computing, content perception and parallel processing have introduced a number of innovative challenges in the domain of privacy preservation and data security. Edge computing provides an extension of the cloud for designing and deploying new security resolutions in the area of IoT. In this paper, a Trusted Platform Module with Ciphertext‐policy attribute‐based encryption technique is introduced to provide a solution to a multilayer privacy‐preserving model for efficient data storage. The developed model is adaptable and has a reliable data access control mechanism. Further, the results of the experiments demonstrate that the suggested technique operates better and give 83% results in terms of storage cost, load testing, effective runtime execution, higher security, credibility, and efficiency.
A low-resolution real-time face recognition using extreme learning machine and its variants

Extreme Learning Machine (ELM) is an excellent candidate for its exemplary performance as a generalized Single Layer Feed-forward Network (SLFN). In this regard, ELM has attracted widespread attention for tackling multiclass classification and regression problems with relative ease. Due to the random allocation of weights to the input and hidden layer biases, the running time spans of ELM are observed to be in seconds and agree with real-time events whilst giving a competitive performance. In this work, the suitability of ELM and its Online Sequential variant for recognition of unknown face samples is investigated under uncontrolled environments, including varying pose, variance, and illumination. The presented face recognition approach is evaluated sing four datasets from YALE, CMU, BIOID, and LFW. To track the face features of a sample face in its spatial domain with varying windows corresponding to different datasets, the Viola-Jones object detection framework is used. The face features are then extracted and evaluated using a Histogram of Oriented Gradients, which forms the dataset to be fed to ELM and OS-ELM classifiers. The running time for training the proposed face recognition approach is found to span within milliseconds to seconds with the computed time complexity of O(P+mLN), justifying the suitability of ELM and OS-ELM classifiers for real-time face recognition applications with a high recognition rate outcome.
Argumentation models and their use in corpus annotation: Practice, prospects, and challenges

Abstract The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora.
HEATT©. A free software for thermal design of liquid flows inside pipes

The main goal of this work is to present the new web platform for numerical simulation of thermal flow in fluids with a laminar regime that runs inside pipes HEATT© (Thermal Analysis and Evaluation Tool for Pipes, in Spanish: Herramienta de Evaluación y Análisis Térmico en Tuberías) to the international scientific and professional technical and teaching community. It consists of free software based on the Network Simulation Method (NSM) available internationally and financed by the 2021 call for Proofs of Concept of the Seneca Foundation-Agency for Science and Technology of the Region of Murcia (Spain). It is aimed at fill the existing gap between the widely known existing large and expensive software, and small professionals, SMEs and researchers with few resources to perform complex thermal behavior calculations. The platform has been extensively tested by comparison with both experimental and bibliographic data. The conclusions have been published in this and previous papers.
LexID: The Metadata and Semantic Knowledge Graph Construction of Indonesian Legal Document

The Legal Fiction principle stipulates that the government needs to ensure the public availability of all of their legal documents. Unfortunately, the text-based search services they provide cannot return satisfactory answers in retrieval scenarios requiring proper representation of relationships between various legal documents. A key problem here is the lack of explicit representation of such relationships behind the employed retrieval engines. We aim to address this problem by proposing LexID knowledge graph (KG) that provides an explicit knowledge representation for Indonesian legal domain usable for such retrieval purposes. The KG contains both legal metadata information and semantic content of the legal clauses of the legal document's articles, modeled using formal vocabulary from the LexID ontology also presented in this paper. The KG is constructed from thousands of Indonesian legal documents. Since the procedure of writing a legal document regulated by the government is clear and detailed, we use a rule-based approach to construct our KG. At the end, we describe several use cases of the KG to address different retrieval needs. In Addition, we evaluated the quality of our KG by measuring its ability to answer questions and got that LexID can answer questions with the macro average F1 score is about 0.91.
Ordinal Regression Based on the Distributional Distance for Tabular Data

Ordinal regression is used to classify instances by considering ordinal relation between labels. Existing methods tend to decrease the accuracy when they adhere to the preservation of the ordinal relation. Therefore, we propose a distributional knowledge-based network (DK-net) that considers ordinal relation while maintaining high accuracy. DK-net focuses on image datasets. However, in industrial applications, one can find not only image data but also tabular data. In this study, we propose DK-neural oblivious decision ensemble (NODE), an improved version of DK-net for tabular data. DK-NODE uses NODE for feature extraction. In addition, we propose a method for adjusting the parameter that controls the degree of compliance with the ordinal relation. We experimented with three datasets: WineQuality, Abalone, and Eucalyptus dataset. The experiments showed that the proposed method achieved high accuracy and small MAE on three datasets. Notably, the proposed method had the smallest average MAE on all datasets.
Calculation Solitaire is NP-Complete

Calculation is a solitaire card game with a standard 52-card deck. Initially, cards A, 2, 3, and 4 of any suit are laid out as four foundations. The remaining 48 cards are piled up as the stock, and there are four empty tableau piles. The purpose of the game is to move all cards of the stock to foundations. The foundation starting with A is to be built up in sequence from an ace to a king. The other foundations are similarly built up, but by twos, threes, and fours from 2, 3, and 4 until a king is reached. Here, a card of rank i may be used as a card of rank i + 13j for j ∈ {0, 1, 2, 3}. During the game, the player moves (i) the top card of the stock either onto a foundation or to the top of a tableau pile, or (ii) the top card of a tableau pile onto a foundation. We prove that the generalized version of Calculation Solitaire is NP-complete.
Aggregation Path Search using Multiple Large RDF Datasets with Equivalence Relations

In the Semantic Web, items are represented by uniform resource identifiers (URIs) on the Web in order to describe relationships between things in resource description framework (RDF) graphs. Large scale open RDF data (linked data) describing information about various things are available. Hence, relational discovery research has been conducted using RDF data to explore relationships between things. The SPARQL protocol and RDF query language (SPARQL) is usually employed to process RDF data; however, SPARQL is not suitable for relational discovery using linked data because it is difficult to describe unknown vocabularies and relationships in SPARQL queries. In this paper, we propose an aggregation path search for RDF graphs as an extension of shortest path keyword search. The aggregation path search is devised to find RDF paths by merging multiple resource URIs into commonly reachable nodes in an RDF graph. This search method can discover new relationships between items that are connected by a common node. We present a method for treating equivalent resource URIs as a single resource URI; these equivalent URIs essentially represent the same thing based on equivalence relations in different RDF datasets. In our experiments, we show in the integration of multiple large scale RDF datasets that the integration of equivalent resources enables richer retrieval of aggregated paths.
A Study of the Education of Model Based Development Thinking in Junior High School Technology Education

In order to make the realization of Society 5.0, which contributes to the achievement of the SDGs, a focus has been placed on human resource development to create new value in the Sixth Science, Technology and Innovation Plan. In addition, a development method called "Model Based Development", in which simulations are conducted using mathematical models, is being promoted in industry. In this study, we develop and practice Model Based Development Thinking education at the junior high school level, and verify its educational effectiveness. Specifically, it is the development of learning materials for proportional control focusing on the characteristic of a sensor, using Advanced Emergency Braking System as the subject. In addition, this research is aimed at the SDGs goals : (4) Quality Education, (7) Affordable and Clean Energy, and (9) Industry, Innovation and Infrastructure.
INVESTIGATION ON TRENDS IN FILING PATENTS IN VARIOUS INDUSTRIES REGARDING CHLORIDE ATTACK OF CONCRETE

Every time a new problem arises in concrete, solutions for it have been sought. Although handling processes for it have been left in historical timelines of academic associations, the efforts of companies have not been included. As a result, when a new problem arises, we cannot utilize the experience and have quick response.
Examination of Scale of Modeled Objects for the 3D Printers in School Education

The use of 3D printers has been spreading in schools as well, and 3D printers were newly introduced in the revised Junior High School Course of Study Guidelines for Technology and Home Economics and the Guidelines for the Development of Teaching Materials. However, when using a 3D printer in class at school, there is a problem that it takes time to model the creation of all students. In this letter, Measure the modeling dimensions and the roundness of the corners when the scale of the model is changed, and consider the possibility of using it at school based on the obtained data.
Quaternion attention multi-scale widening network for endoscopy image super-resolution

Objective. In the field of endoscopic imaging, Super-Resolution (SR) plays an important role in Manufactured Diagnosis, physicians and machine Automatic Diagnosis. Although many recent studies have been performed, by using deep convolutional neural networks on endoscopic SR, most of the methods have large parameters, which limits their practical application. In addition, almost all of these methods treat each channel equally based on the real-valued domain, without considering the difference among the different channels. Our objective is to design a SR model named Quaternion Attention Multi-scale Widening Network (QAMWN) for endoscopy images to address the above problem.Approach. QAMWN contains a stacked Quaternion Attention Multi-Scale Widening Block, that composed of Multi-scale Feature Widening Aggregation Module (MFWAM) and Quaternion Residual Channel Attention (QRCA). The MFWAM adopts multi-scale architecture with step-wise widening on feature channels for better feature extraction; and in QRCA, quaternion is introduced to construct Residual Channel Attention Mechanism, which obtains adaptively scales features by considering compact cross-channel interactions in the hyper-complex domain.Main results. To verify the efficacy of our method, it is performed on two public endoscopic datasets, CVC ClinicDB and Kvasir dataset. The experimental results show that our proposed method can achieve a better trade-off in model size and performance. More importantly, the proposed QAMWN outperforms previous state-of-the-art methods in both metrics and visualization.Significance. We propose a lightweight SR network for endoscopy and achieves better performance with fewer parameters, which helps in clinical diagnosis of endoscopy.
Umbrellalike Hierarchical Artificial Bee Colony Algorithm

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.
Digitally Designed Forensic Procedure a Panacea to Cyber Fraud Control in Nigeria

The study examines digitally designed forensic procedure contributes to cyber fraud control in Nigeria. The study established that digitally designed forensic audit are helpful for identification, acquisition, collection and analysis of digital evidence relating to cyber fraud as majority of the respondents had Agree responses. The study concluded that digital forensic auditing is effective in the control of cyber fraud in Nigeria. It was recommended that reviewer of the forensic expert reports should scrutinize the academic qualifications of the forensic examiners to be sure they are qualified to carry out the exercise, this is to avoid the use of quacks.
Classifying Cyberattacks on Financial Organizations Based on Publicly Available Deep Web Dataset

The financial sector is one of the main targets of malicious persons stealing money and data from individuals and organizations. The lack of knowledge of end users, expertise of attackers, and availability of tools and publicly accessible data make it serious about keeping all online transactions safe. Hence, it causes massive damage in terms of data and fund loss. Surprisingly, the attackers make this database public to show their ability, threaten a specific community, or find potential buyers on the dark web. However, studying this dataset reveals the current situation and upcoming threats in a broader picture. Moreover, it may help to take precautions for subsequent potential attacks. The study solely focuses on data sets for both surfaces web - ’Paysim’ and ’Banksim,’ and dark web - SOCRadar dataset to analyze cyberattacks mainly targeting transactions. It also classifies cyberattacks on financial organizations. The research team finds there are mainly 71 cyberattacks that are generated around the world. Most cybercriminals mainly target the United States, Russia, China, the United Kingdom, Spain, and Germany. However, the US ranks at the top stated-backed hackers based on the dark web dataset. The study also shows an overview of cyberattacks based on transaction type, gender, and fraud distributions. The team uses the Python-based Beautiful Soup package to scrap web data. The team highly analyzes the datasets and visualizes using Seaborn. The research team also proposes a comprehensive four-step framework, FinFrame, for securing financial organizations. Finally, the team suggests ensuring a minimum ground level of knowledge in terms of cybersecurity for the end users to keep safe from being hacked.
Determination of SME Tax with Naïve Bayes Method

The level of compliance of SMEs taxpayers should be addressed by the Regional Development Planning Authority (BAPPEDA). From field observations, one of the tasks of the Regional Technical Implementation Unit BAPPEDA UPTD in Central Bandung Region is to identify taxpayers of SMEs actors in the region. Most of the SMEs business actors in central Bandung do not know the business category and therefore do not understand the level and percentage when paying the tax rate. The head of BAPPEDA UPTD in Central Bandung Region hopes his unit can address these issues according to organizational functions in technical implementation. It is very appropriate to build a web-based taxpayer determination system using naive Bayes method because we can understand the nature of taxpayer problems generated by SMEs actors in Central Bandung. The results of this study determined variables to provide an understanding of tax rate payments according to the size of the SMEs actor’s business. The created system can meet the expectations of the person responsible for the BAPPEDA UPTD Central Bandung Area, depending on the organizational capabilities of the technical implementation. The classification function is the result of using the naive Bayesian method in the calculation process to determine SMEs taxpayers.