Reviewerly/exHarmony huggingface dataset:
Diverse ensemble classifier driven Email spam classification using multiple word embedding’s with COCOB optimizer

Spam Email is a serious concern which can steal user’s personal information and cause huge financial loss due to the increasing rate of internet users. Therefore, the demand for accurate spam filtering has become more sophisticated for the Email spam detection. In the existing techniques, it is difficult to intricate the relationship between words in the Email using certain word embedding techniques and learning rate tuning is one of the greatest challenges of stochastic optimization. To overcome this difficulty, the proposed framework uses diverse ensemble based Email spam classification by incorporating multiple word embedding’s with Continuous Coin Betting optimizer. Word2Vec is used to produce the first set of 200D, next set of 200D word embedding is produced by Glove and 768D is produced by using Bidirectional Encoder Representations from Transformers (BERT) respectively. After generating word embedding, then it is classified through diverse ensemble based classifier with base level classifier consists of Long Short Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU) and Bi-directional Gated Recurrent Unit (Bi-GRU) and LSTM as Meta-classifier using COCOB optimizer. Experiments were conducted on 3 benchmark Email dataset and result shows that the proposed system outperforms well with a low false positive rate.
Ontologies in the era of large language models – a perspective

The potential of large language models (LLM) has captured the imagination of the public and researchers alike. In contrast to previous generations of machine learning models, LLMs are general-purpose tools, which can communicate with humans. In particular, they are able to define terms and answer factual questions based on some internally represented knowledge. Thus, LLMs support functionalities that are closely related to ontologies. In this perspective article, I will discuss the consequences of the advent of LLMs for the field of applied ontology.
Improving entity linking by combining semantic entity embeddings and cross-attention encoder

Entity linking is an important task for information retrieval and knowledge graph construction. Most existing methods use a bi-encoder structure to encode mentions and entities in the same space, and learn contextual features for entity linking. However, this type of system still faces some problems: (1) the entity embedding part of the model only learns from the local context of the target entity, which is too unique for entity linking model to learn the context commonality of information; (2) the entity disambiguation part only uses similarity calculation once to determine the target entity, resulting in insufficient interaction between the mentions and candidate entities, and ineffective recall of real entities. We propose a new entity linking model based on graph neural network. Different from other bi-encoder retrieval systems, this paper introduces a fine-grained semantic enhancement information into the entity embedding part of the bi-encoder to reduce the specificity of the model. Then, the cross-attention encoder is used to re-rank the target mention and each candidate entity after the entity retrieval model. Experimental results show that although the model is not optimal in inference speed, it outperforms all baseline methods on the AIDA-CoNLL dataset, and has good generalization effects on four datasets in different fields such as MSNBC and ACE2004.
Application of fuzzy reinforcement learning in IoT-based robotics for autonomous navigation and collision avoidance

In this study, we investigate the viability of applying fuzzy reinforcement learning (FRL) to Internet of Things-based robots for purposes of autonomous navigation and collision avoidance. The proposed approach utilises FRL, IoT, and a sensor network to give the robot the ability to learn from its environment and act in accordance with those policies. The authors used FRL to train a mobile robot with wheels to move around and avoid obstacles, and then they put the robot through its paces in a virtual world. Results showed that the FRL-based technique improved the robot’s navigation and collision avoidance performance compared to traditional rule-based approaches. The results of this study indicate that FRL may be a viable technique for enabling autonomous navigation and obstacle avoidance in IoT-based robotics.
PREREQUISITES FOR THE CREATION OF A SINGLE STUDENT QUARTER WITHIN THE BOUNDARIES OF DIEDRICHSON, MECHNYKOVA, NISHCHINSKY AND DYUKIVSKA STREETS

The possibility of renovating the student quarter by creating common spaces while addressing the existing problems of the territory of the initial mortgages is being considered. The main idea is: the creation of a single campus on the basis of current student communities in the world. Implementation of new functions in the territories of the initial buildings, competent development of the local-planning structure with additional renovation and rehabilitation of the territory, leading to an influx of foreign students, which will become a business card for institutions. The article presents the specifics of the design and renovation of the student quarter, identifies the main problems of the quarter and their solutions, as well as the identified advantages of the historical student quarter. The applications of Campus projects are brought to light. &#x0D; The aim of this work is to create a United Campus based on the example of modern campuses around the world. To create additional pedestrian connections between streets and provide visitors to educational institutions with open spaces for recreation and work. To create a single space that is favourable for finding and learning. To give a new and modern look to educational institutions that will contribute to the development of the surrounding areas. To attract the attention of future students and teachers. &#x0D; The implementation of the Single Campus idea can also improve the prestige of educational institutions, increase their competitiveness. Overall, the merger of educational institutions into a Single Campus can have a positive impact on the learning environment, promoting synergy, innovation and societal development.
Design and Control of Autonomous Flying Excavator

This study presents a drone-based excavation platform prototype with the key objectives of balancing stability during excavation, sensing, and digging the soil pile autonomously without human intervention. The whole platform was first designed in CAD software, and then each part of the excavator assembly was 3D printed by using PLA filament. The physical system was then combined with numerous electronic components and linked to various software applications for a drone to perform autonomous excavations. Pixhawk Orange Cube served as the main controller for the drone, while Nvidia Jetson Nano was used for processing data and controlling the tip of the bucket at a specified location for the autonomous excavator. Two scenarios were considered to validate the functionality of the developed platform. In the first scenario, the drone flies independently to a construction site, lands, senses the soil, excavates it, and then travels to another location specified by the mission to deposit the soil.
Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain

Low-light image enhancement is an important task in computer vision. Deep learning-based low-light image enhancement has made significant progress. But the current methods also face the challenge of relying on a wide variety of low-light/normal-light paired images and amplifying noise while enhancing brightness. Based on existing experimental observation that most luminance information concentrates on amplitudes while noise is closely related to phases, an unsupervised low-light image enhancement method in the Fourier transform domain is proposed. In our method, the low-light image is firstly transformed into the amplitude component and phase component via Fourier transform. The luminance of low-light image is enhanced by CycleGAN in the amplitude domain, and the phase component is denoising. The cycle consistency losses both in the Fourier transform domain and spatial domain are used in training. The proposed method has been validated on publicly available test sets and shows that our method achieves superior results than other approaches in low-light image enhancement and noise suppression.
Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis

To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively.
A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters

Navigation map generation based on remote sensing images is crucial in fields such as autonomous driving and geographic surveying. Style transfer is an effective method for obtaining a navigation map of the current environment. However, there is lack of robustness of the map-style transfer model, resulting in unsatisfactory quality of the generated navigation maps. To address these challenges, we average the parameters of generators sampled from different iterations with a dense sampling strategy in the Generative Adversarial Network (CycleGAN). The results demonstrate that the training efficiency of our method on the MNIST and generation quality on the Google Map dataset are significantly superior to traditional style transfer methods. Moreover, it performs well in multi-environment hybrid mapping. Our method improves the generalization ability of the model and converts existing navigation maps to other styles of maps precisely. It can better adapt to different types of urban layout and road planning, bringing innovative solutions for traffic management and navigation systems.
The role of organizational antecedents in fostering accounting intelligence adoption: The mediating influence of organizational culture

BACKGROUND: The insurance sector in Jordan stands to gain significant benefits from the adoption of Accounting Intelligence (AI) systems, which can transform decision-making processes, enhance operational efficiency, and elevate customer satisfaction. OBJECTIVE: This study investigates the mediating role of organizational culture in the relationship between three key organizational factors—top management support (TMS), information technology (IT), and organizational size (OS)—and AI system adoption (AIA) in Jordan’s insurance companies. METHODS: Using a quantitative approach, the study surveyed 274 employees from Jordanian insurance companies. The data was analyzed using structural equation modeling (SEM) and confirmatory factor analysis (CFA) to assess the study’s hypotheses and ensure data reliability and validity. RESULTS: The findings revealed substantial support for the relationships between TMS, IT, OS, and organizational culture (ORC). Additionally, organizational culture emerged as a mediating factor influencing AI system adoption (AIA) adoption. However, certain direct relationships, such as ORC and AIA, and the mediating role of organizational culture in the relationship between IT and AIA, were context-dependent and lacked support. IMPLICATIONS: Organizational culture plays a critical role in mediating the relationship between key organizational factors and AIA in Jordan’s insurance sector.
Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices

Wearable technologies have slowly invaded our lives and can easily help with our day-to-day tasks. One area where wearable devices can shine is in human activity recognition, as they can gather sensor data in a non-intrusive way. We describe a real-time activity recognition system based on a common wearable device: a smartwatch. This is one of the most inconspicuous devices suitable for activity recognition as it is very common and worn for extensive periods of time. We propose a human activity recognition system that is extensible, due to the wide range of sensing devices that can be integrated, and that provides a flexible deployment system. The machine learning component recognizes activity based on plot images generated from raw sensor data. This service is exposed as a Web API that can be deployed locally or directly in the cloud. The proposed system aims to simplify the human activity recognition process by exposing such capabilities via a web API. This web API can be consumed by small-network-enabled wearable devices, even with basic processing capabilities, by leveraging a simple data contract interface and using raw data. The system replaces extensive pre-processing by leveraging high performance image recognition based on plot images generated from raw sensor data. We have managed to obtain an activity recognition rate of 94.89% and to implement a fully functional real-time human activity recognition system.
Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning

The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform.
The Potential of AI-Driven Assistants in Scaled Agile Software Development

Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities.
Reversible data hiding in encrypted images with multi-prediction and adaptive huffman encoding

With the rapid development of multimedia technology and the massive accumulation of user data, a huge amount of data is rapidly generated and shared over the network, while the problems of inappropriate data access and abuse persist. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that embeds protected data in an encrypted domain and accurately extracts the embedded data without affecting the original content. However, the amount of embedded data has been one of the major limitations in the performance and application of RDHEI. Currently, the main approaches to improve the capacity of RDHEI are either to increase the overall capacity or to reduce the length of the auxiliary information. In this paper, we propose a novel RDHEI scheme based on multi-prediction and adaptive Huffman encoding. To increase the overall capacity, we propose a multi-prediction, called MED+GAP predictor, to generate the label map data of non-reference pixels prior to image encryption. Then, an adaptive Huffman coding is designed to compress the generated labels in order to reduce the embedding length of the auxiliary information used for the extraction and recovery. Experiments show that the proposed method with MED+GAP predictor and adaptive Huffman coding improves 0.052 bpp, 0.023 bpp, and 0.047 bpp on average over the other state-of-the-art methods on the BOSSBase, BOWS-2, and UCID datasets, respectively, while maintaining security and reversibility.
Ship detection and recognition based on high-resolution remote sensing imagery

Ship detection plays a critical role in maritime safety and surveillance. Targets in high-resolution remote sensing imagery frequently have complex textures and shapes due to the rapid advancement of remote sensing satellite technology. These features can be efficiently captured and accurately detected by deep learning models. Due to the complicated harbor environment, a variety of ship types, and a huge volume of ships, ship detection based on high-resolution remote sensing photos is still a difficult problem when compared to generic object detection. For the purpose of detecting ships in high resolution remote sensing photos, this research suggests the EBENet method. In order to increase the model's precision, the entire method employs the lightweight network EfficientNet as the YOLOv5 backbone network and integrates the BiFPN and ECA attention mechanisms. The attention mechanism module helps the model to focus more on critical features, boosting the accuracy of ship detection, while the bi-directional feature pyramid network module integrates multiscale features to enhance the precision of object recognition. Additionally, research was done utilizing publicly accessible optical remote sensing image datasets to classify different ship types into three groups: Cargo ship, Warship, and Civilian ship. Comparative tests using the YOLOv5 technique were conducted. The experimental findings show that in ship detecting tasks, EBENet beats the YOLOv5 algorithm, with a mAP50 of 93.6% and FPS of 104.189, performs better in reducing missed detections and false alarms while retaining high detection efficiency. Therefore, EBENet hence has potential for use in a variety of maritime monitoring and safety duties.
Boosting Constrained Horn Solving by Unsat Core Learning

The Relational Hyper-Graph Neural Network (R-HyGNN) was introduced in [1] to learn domain-specific knowledge from program verification problems encoded in Constrained Horn Clauses (CHCs). It exhibits high accuracy in predicting the occurrence of CHCs in counterexamples. In this research, we present an R-HyGNN-based framework called MUSHyperNet. The goal is to predict the Minimal Unsatisfiable Subsets (MUSes) (i.e., unsat core) of a set of CHCs to guide an abstract symbolic model checking algorithm. In MUSHyperNet, we can predict the MUSes once and use them in different instances of the abstract symbolic model checking algorithm. We demonstrate the efficacy of MUSHyperNet using two instances of the abstract symbolic model-checking algorithm: Counter-Example Guided Abstraction Refinement (CEGAR) and symbolic model-checking-based (SymEx) algorithms. Our framework enhances performance on a uniform selection of benchmarks across all categories from CHC-COMP, solving more problems (6.1% increase for SymEx, 4.1% for CEGAR) and reducing average solving time (13.3% for SymEx, 7.1% for CEGAR).
A case for ideological coherence in regulating online speech: going back to basics to manage the (not so) difficult interplay between free speech and economic freedoms

The article draws an updated picture of the regulation and case-law on online speech, with a particular focus on the United States. It considers the most important regulatory innovations, several significant cases, and the main positions in the public debate, not only academic. The perspective offered is different from the most popular approaches to the regulation of the subject, and includes a reflection about whether the large platforms hosting user-generated content remain exclusively private entities, or if they should be subject to certain rules provided for the public administration. The conclusion reflects on the intersections that occur online between freedom of expression, ‘economic speech’ and freedom of economic initiative.