Reviewerly/exHarmony huggingface dataset:
Mathematical insights into interval solutions of fuzzy relation inequalities for effective Information resource sharing in business education

To examine the connectivity of educational and business institution networks and assess their resource accessibility, we have employed min-max fuzzy relation systems. This piece aims to delve into the outcomes resulting from various factors within a three-compartmental financial system. The research undergoes the dynamical max-min fuzzy solutions, which are expressed in an interval format. These solutions take into consideration variables such as the price index, interest rate, investment demand, fluctuations, cost per investment derived from saved funds, and the elasticity of market demand within the commercial sector. These variables are essential for comprehending the dynamics of the three-compartment financial system. It is difficult to obtain the entire set of solutions for the max-min fuzzy relation system using only minimal solutions. Some of these systems can be solved using optimized fuzzy relation models for both maximal and minimal solutions. Therefore, by converting the business market's financial system into a max-min fuzzy relation inequality, the strategy presented in this article tries to get the broadest and changing solution fluctuating between minimal and maximal solutions. With this suggested strategy for solving the problem, the limiting upper and lower values within which the given quantities fluctuate are identified. By measuring the breadth of the resulting interval, the range of fluctuation may be identified, giving a thorough insight into the system's behaviour. Due to the potential instability of market agents and their respective influences, fluctuations may occur in the demand, supply, and prices of indices in the financial system. Consequently, presenting the financial system in the format of the widest fuzzy interval solution can provide easily accessible information to the general public by illustrating the max-min fuzzy relation system of financial dynamics, outlining the necessary steps, and including a numerical example.
Dynamic self-correcting key performance indicator anomaly detection algorithm

The operation and maintenance of the background system is always an important link to ensure the system's high availability. With the increasing number of background systems, their operation, and maintenance have to develop from the initial huge-crowd strategy to the direction of intelligence. The key to intelligent operation and maintenance is the abnormal detection of key performance indicators (KPI), such as CPU utilisation. However, the existing KPI anomaly detection algorithms not only cannot select the dynamic threshold under the non-parametric methods but also have no false-positive correction mechanism to correct the false alarms. In order to overcome the above shortcomings, this work proposes a dynamic self-correcting Key Performance Indicator (KPI) anomaly detection algorithm, hereafter referred to as DSCAD. To the best of our knowledge, in the field of KPI anomaly detection, the DSCAD algorithm is the first dynamic threshold algorithm that does not rely on the assumption of normal distribution. Compared with the existing KPI anomaly detection methods, the F-score of the DSCAD algorithm increased by 3% and had the best performance.
Profile Clone Detection on Online Social Network Platforms

Successful profile cloning attacks have far-reaching consequences for the victims. People whose profiles are cloned suffer defamation, mistrust, loss of job, interdiction, public disgrace, dent of reputation, and defrauding. This chapter aims to identify and propose a model that detects profile cloning attacks on online social network platforms. The proposed model is based on unsupervised machine learning clustering and statistical similarity verification methods for the filtration of profiles. The model computes statistical values for attribute similarity measure (ASM) and friends network similarity measure (FNSM). The model has a precision score of 100%. The attribute weight and friends network similarity measures show percentile figures ranging from 0.45 to 1.00. Profile accounts that fall within this range for both ASM and FNSM measures are likely to turn out to be cloned. The higher the figures, the more the suspicion of being a fake account to the supposed original one. The strength of the model is that it exposes the actual clone using the outcome of the computation.
Risk Detection and Assessment in Digital Signature

Nowadays people are living in a digital era in which all aspects of our lives depend on computers, networks, software applications and other electronic devices. Due to the technological development the security tasks and threat intelligence is a challenging task. Information theft is one of the most expensive segments of cybercrime. It is essential to protect information from cyber threats. In business, the digital signature plays an important role in authorizing the digital data. The receiver or some third party can verify this binding. In larger business, the signing process supports regulating the business. It uses asymmetric cryptography, which provides a layer of validation and security to the message communication through a non-secure channel. There are some risks in digital signature like forgery, fraud, and exclusions. This chapter discusses how to identify the risks and methods practices to mitigate the risks in adopting digital signature. Reasonable risk analysis can enhance the chance of successful electronic business.
Synthetic Data Generation Based on RDB-CycleGAN for Industrial Object Detection

Deep learning-based methods have demonstrated remarkable success in object detection tasks when abundant training data are available. However, in the industrial domain, acquiring a sufficient amount of training data has been a challenge. Currently, many synthetic datasets are created using 3D modeling software, which can simulate real-world scenarios and objects but often cannot achieve complete accuracy and realism. In this paper, we propose a synthetic data generation framework for industrial object detection tasks based on image-to-image translation. To address the issue of low image quality that can arise during the image translation process, we have replaced the original feature extraction module with the Residual Dense Block (RDB) module. We employ the RDB-CycleGAN network to transform CAD models into realistic images. Additionally, we have introduced the SSIM loss function to strengthen the network constraints of the generator and conducted a quantitative analysis of the improved RDB-CycleGAN-generated synthetic data. To evaluate the effectiveness of our proposed method, the synthetic data we generate effectively enhance the performance of object detection algorithms on real images. Compared to using CAD models directly, synthetic data adapt better to real-world scenarios and improve the model’s generalization ability.
Identifying and Assessing Risk Factors to Lower Hazards in Cyber Security Penetration Testing

Penetration testing is a rapidly growing field. In today's technological exposure to cyber-attacks, there is a need for easily understandable metrics of cyber-attacks and the impact that it causes on enterprises. The measurement metrics are significant challenges in assessing a cyber-crime incident or a penetration testing audit. There are so many factors in the cyber security field that a penetration tester must consider determining the risk of a particular event or attack. Without any measurement or metrics, there is a chance that the pentesters or penetration testing can get stuck in a bottomless pit without arriving at a defined result. A tool called CVSS attempts to calculate risk based on specific parameters. There are complex technicalities involved in arriving at a risk index to be understood by the board members of an enterprise to make an informed decision about the enterprise's cyber security plan.
A Conceptual Framework for ICT Adoption in Tribal Areas

According to prior research on a developmental project in the tribal area in India, tribal people are said to be laggards in terms of technology acceptance and adoption. It is because of some factors such as geographical location and illiteracy rate. This chapter examines the whole issue using a new perspective - the use of theories of information systems (IS) for acceptance and adoption of technology in tribal areas. The chapter evaluates through these IS theories the basic understanding of the reasons behind the laggard behavior of tribal people, keeping the Indian context in mind.
An Implemented Example of XKMS With Web Services

Web service is a loosely coupled system, which gets remote request for a particular service and returns results after executing that service. The data transfer of the web service is a request/response system which carries input parameters through request message and carries executed results of the web services. Both request and response messages are carried in the form of SOAP message which is XML based. XML signature and XML encryption are two methods of securing data transfer through simple access protocol (SOAP) messages from web services. The main goal of the XKMS initiative is to make it possible to create XML-based trust web services for handling and administration of PKI-based cryptographic keys. By making it possible to develop such web services, XKMS helps to simplify working with PKI and makes it simpler for web services to integrate security features into their programmes. This chapter provides a thorough working of the implemented draught of the W3C working group's XKMS specification.
Implementation of Image Steganography and Combination of Cryptography and Steganography

Information transmission over the net is a very common process and securing the information plays an important role. Cryptography and Steganography are the two important techniques that provides secure transmission of data over the internet medium. Cryptography converts the data into an unreadable format, steganography is used to hide the encrypted text into any file like an audio, video, image, etc. In this paper we have explained Cryptography and Steganography as well as combined cryptography and steganography.
Assessing the Impact of GeoAI in the World of Spatial Data and Energy Revolution

Geospatial is going to be the absolute heart of making sense of trillions of bits of data that are going to be surveyed by big machines. The buzz word of the last 4-5 years has been artificial intelligence (AI) and is influencing every marketplace including GIS, healthcare, pollution, and the list is truly endless. It is the world of collaborative and multidisciplinary research where technology is applied in almost every domain and has proved extremely useful to end-users. The diversity of themes identified in this chapter can be grouped into the categories of renewable energy mapping: spatiotemporal analysis, and data mining. This chapter gives a comprehensive account of transformation from the classical ML clustering techniques with the potential of quantum clustering (QC) which can be applied or mapped to renewable energy solutions driven by use of GIS, or narrating the importance of GIS and quantum. This chapter highlights the relationship between GeoAI, cybersecurity, and quantum computing in the world of spatial data.
Pilgrimage to the “Vi” Center of Bulverket's Water Square

At the cusp of Norse – Christian landscape transformation and pilgrimage, one investigates a technical ability to survey across land and ice. Two existing large bisects (Norse “vis”) with vertexes at significant natural or prehistoric features, statistically compare with the likelihood of identical geometry created by random points. The Bulverket platform in Tingstäde Trask and stone church on shore create vi centered points to map three-point alignments with churches. Alignments from the Bulverket are separately tested against random phenomena statistically proving accurate, surveyed lines of churches. Thus, land surveyed azimuths to either of the vi centers or points in their systems might create the varied orientations of these churches, ranging from 14° north to 18° south of due east. At each church, pairs or triads of azimuths from other lat/long points create squares or parallelograms whose diagonals mathematically lie within one degree of church orientations. Geometric determination of church orientation may have begun with the sacred Bulverket platform square in 1130.
Nonequilibrium dynamics of the Jaynes-Cummings dimer

We investigate the nonequilibrium dynamics of a Josephson-coupled Jaynes-Cummings dimer in the presence of Kerr nonlinearity, which can be realized in the cavity and circuit quantum electrodynamics systems. The semiclassical dynamics is analyzed systematically to chart out a variety of photonic Josephson oscillations and their regime of stability. Different types of transitions between the dynamical states lead to the self-trapping phenomenon, which results in photon population imbalance between the two cavities. We also study the dynamics quantum mechanically to identify characteristic features of different steady states and to explore fascinating quantum effects, such as spin dephasing, phase fluctuation, and revival phenomena of the photon field, as well as the entanglement of spin qubits. For a particular "self-trapped" state, the mutual information between the atomic qubits exhibits a direct correlation with the photon population imbalance, which is promising for generating photon mediated entanglement between two non interacting qubits in a controlled manner. Under a sudden quench from stable to unstable regime, the photon distribution exhibits phase space mixing with a rapid loss of coherence, resembling a thermal state. Finally, we discuss the relevance of the new results in experiments, which can have applications in quantum information processing and quantum technologies.
esCorpius-m: A Massive Multilingual Crawling Corpus with a Focus on Spanish

In recent years, transformer-based models have played a significant role in advancing language modeling for natural language processing. However, they require substantial amounts of data and there is a shortage of high-quality non-English corpora. Some recent initiatives have introduced multilingual datasets obtained through web crawling. However, there are notable limitations in the results for some languages, including Spanish. These datasets are either smaller compared to other languages or suffer from lower quality due to insufficient cleaning and deduplication. In this paper, we present esCorpius-m, a multilingual corpus extracted from around 1 petabyte of Common Crawl data. It is the most extensive corpus for some languages with such a level of high-quality content extraction, cleanliness, and deduplication. Our data curation process involves an efficient cleaning pipeline and various deduplication methods that maintain the integrity of document and paragraph boundaries. We also ensure compliance with EU regulations by retaining both the source web page URL and the WARC shared origin URL.
A Systematic Review of Literature for Technology Adoption and Use

Digital technologies can allow for more proactive and engaged learning for students. The study analyzed recent publications related to technology use at all levels in Nigerian classrooms. The PRISMA Guideline was followed to analyze 25 quantitative studies published between 2015 to 2023. Five thematic areas related to ICT implementation in schools were explored. The review showed that there was some adoption of technology, but the level of adoption would increase with the provision of hardware for faculty, a steady supply of electricity, and professional development for teachers.
A Naive approach: Translation of Natural Language to Structured Query Language

A database is a major source of information which plays an important role in our life. Information retrieval from the database requires formulating a querythatisunderstandablebythecomputerinordertoproducedesiredoutput. Generally, databases work with structured query language (SQL). But a naive user usually unfamiliar with the structured query language as well as structure of the table in the database. Hence, it becomes very difficult for the naïve-user to collect the desired information. This paper provides a solution to this problem and it enables users to retrieve information through natural language, such as English language. Being able to access information from the database by using natural language bridges the man-machine gap. Tokenization, lexical analysis, syntactic analysis, semantic analysis, and other complex stages are all involved in converting a natural language query into a SQL query. The purpose of this paper is to translate natural language queries into Structure Query Language queries, allowing non-technical people to get connected to databases and to gather the required information.
Cloud Computing Deployment Model for Ethio Telecom

Successful adoption of cloud computing requires identifying a cloud computing deployment model that best fits its needs. This research aims to propose a cloud deployment model that fits the need of Ethio telecom and to assess its readiness to adopt the potential deployment model. Data were collected via questionnaire survey, document analysis, and interviews. A document analysis is conducted to understand Ethio telecom infrastructure and services. A questionnaire was used for conducting a readiness assessment of Ethio telecom to adopt the proposed model in three major phases using Gartner Magic Quadrant. Result of the assessment shows that core business/competitive position is a barrier to six IT services and applications; standardization is a barrier to four IT services and applications, compliance is a barrier to four IT services and applications, service type is a barrier to four IT Service and applications, and importance or availability are barrier to three IT Service and applications. Based on the results, recommendations for practice are forwarded for management intervention.
A Hybrid Reinforcement Learning and PSO Approach for Route Discovery of Flying Robots

Route discovery for flying robots is one of the major concerns while developing an autonomous aerial vehicle. Once a path planning algorithm is built and the flying robot reaches the destination point from the target point successfully, it is again important for the flying robot to come back to its original position and that is done through route discovery algorithms. Reinforcement learning is one of the popular machine learning methods in which the flying robot has to interact with the environment and learn by exploring the possibilities and maximum reward point method, without the requirement of a large amount of prior training data. Particle swarm optimization is an artificial intelligence inspired algorithm which finds optimal solution in a multi-dimensional space. This chapter has discussed a random exploration reinforcement learning approach combined with PSO algorithm that has been used to discover the optimum path for a flying robot to return from the destination point to the target point after it had traversed its best path from an already defined swarm intelligence technique. PSO+Reinforcement Learning (RL-PSO) is an optimization technique that combines the global search capability of PSO with the exploitation and exploration strategy of RL. Here higher reward points were assigned to the already defined best path obtained from the path planning technique, so that while returning from the destination point it will try to find the route with the highest reward point. With several iterations, it will optimize and find the best route for backpropagation. The algorithm is built using a python environment and the convergence result with the number of iterations has been validated.
Lightweight Sheep Head Detection and Dynamic Counting Method Based on Neural Network

To achieve rapid and precise target counting, the quality of target detection serves as a pivotal factor. This study introduces the Sheep's Head-Single Shot MultiBox Detector (SH-SSD) as a solution. Within the network's backbone, the Triple Attention mechanism is incorporated to enhance the MobileNetV3 backbone, resulting in a significant reduction in network parameters and an improvement in detection speed. The network's neck is constructed using a combination of the Spatial Pyramid Pooling module and the Triple Attention Bottleneck module. This combination enhances the extraction of semantic information and the preservation of detailed feature map information, with a slight increase in network parameters. The network's head is established through the Decoupled Head module, optimizing the network's prediction capabilities. Experimental findings demonstrate that the SH-SSD model attains an impressive average detection accuracy of 96.11%, effectively detecting sheep's heads within the sample. Notably, SH-SSD exhibits enhancements across various detection metrics, accompanied by a significant reduction in model parameters. Furthermore, when combined with the DeepSort tracking algorithm, it achieves high-precision quantitative statistics. The SH-SSD model, introduced in this paper, showcases commendable performance in sheep's head detection and offers deployment simplicity, thereby furnishing essential technical support for the advancement of intelligent animal husbandry practices.
Fake Profile Detection Using Logistic Regression and Gradient Descent Algorithm on Online Social Networks

One of the most challenging issues on online social networks is identifying spam accounts. The concern stems from the fact that these personas pose a significant threat, as they may engage in harmful activities against other users, extending beyond mere annoyance or low-quality advertisements. The demand for accurate and effective spam detection algorithms for online social networks is increasing due to this risk. To address the problem of spam detection in online social networks, this research proposes a hybrid machine learning model based on logistic regression and a contemporary metaheuristic method called the Gradient Descent Algorithm. The proposed approach automates spammer identification and provides insights into the factors that have the greatest impact on the detection process. Additionally, the model is evaluated and implemented on multiple datasets, and the experiments and findings demonstrate that the proposed model outperforms many other algorithms in terms of accuracy and delivers robust results in terms of precision, recall, f-measure, and AUC. It also aids in identifying the factors that influence detection the most.
FACTORS AND STRUCTURE OF PROFESSIONAL IDENTITY OF ADMINISTRATORS IN LOCAL APPLIED UNDERGRADUATE UNIVERSITIES AND THE SCALE COMPATION

The occupational identity of administrators in local applied undergraduate colleges is related to how Chinese local applied undergraduate colleges and universities can better move from management to governance. Based on the social identity theory perspective, this study uses a combination of semi-structured interviews and questionnaires to extract the elements and structure of the occupational identity of administrators in local applied undergraduate colleges. It compiles a measurement scale that conforms to the characteristics of the occupational group. The research results show that the professional identity of administrators in local applied undergraduate colleges and universities is a four-dimensional structure consisting of cognitive professional identity, emotional professional identity, competency professional identity, and self-esteem professional identity. Based on this, the "Professional Identity Scale for Administrators of Local Applied Undergraduate Colleges and Universities" has good reliability and validity, and can be used as a measurement tool for the occupational identity of administrators of local applied undergraduate colleges.&#x0D;