Reviewerly/exHarmony huggingface dataset:
Cucconi Feature Extracted Random Decision Forest Classification for Efficient Sentiment Analysis

Sentiment is the method of conveying the opinion by the customer for particular product e-commerce website. Sentiment analysis is otherwise termed as opinion mining that identifies the polarity of extracted opinions. Nowadays, opinion mining concept received large interest by many researchers to identify the polarity of the statements. A number of scholars have conducted their research on efficient sentiment analysis with better accuracy. But the space complexity and time complexity performance did not reduced using existing classification techniques. To deal with these issues, CFDFC (Cucconi Feature Extracted Random Decision Forest Classification) Approach is presented. The primary aim of CFDFC Approach is to perform effective sentiment analysis with improved accuracy and reduced time complexity. The number of user review statement is collected from the input dataset. The proposed CFDFC Technique comprises of pre-processes, feature extractions, and classifications. Pre-processes eliminate stop words and stem words from user reviews. After pre-processing, the features (i.e. keywords) extraction process is performed to minimize the dimensionality and time consumption for opinion classification. This work uses cucconi projective feature extractions in CFDFC to squeeze out opinion words from reviews where cucconi projective feature extraction is generally a dimensionality reduction technique used for identifying the projections in multidimensional data. Finally, the classification process is carried out using random decision forest classifier. The random decision forest classifier uses the ID3 DT (decision tree) as a weak learner to classify the review statements with the extracted features. By using the voting scheme, random decision forest classifications finally classify instances with higher accuracy. This again is useful to boost the performance of sentiment analysis. The comprehensive experimental assessment is carried out using various parameters including accuracy, error rates, recall values , time and space complexities with regard to the number of review statements gathered from the dataset.The statistical results obtained show that the proposed CFDFC Technique achieves remarkable accuracy, recall, and minimal time complexity than the current methods.
Credit card fraud detection system

Credit card fraud is a significant problem for financial institutions and cardholders and can result in significant financial losses and damage to reputation. Detecting and preventing fraud in credit card transactions is critical to minimizing losses and maintaining customer trust. The main objective of this project is to build a model (a web-based system) that can effectively identify fraudulent transactions. This involves collecting and processing large datasets of credit card transactions, including both legitimate and fraudulent transactions, and using machine learning algorithms like decision trees and random forests to train a model to recognize patterns and anomalies. In addition, the system will have strong user authentication protocols that must be in place to prevent unauthorized access to the system.
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition

Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e.g., different device placements). This paper proposes a Deep Heterogeneous Contrastive Hyper-Graph Learning (DHC-HGL) framework that captures heterogenous Context-Aware HAR (CA-HAR) hypergraph properties in a message-passing and neighborhood-aggregation fashion. Prior work only explored homogeneous or shallow-node-heterogeneous graphs. DHC-HGL handles heterogeneous CA-HAR data by innovatively 1) Constructing three different types of sub-hypergraphs that are each passed through different custom HyperGraph Convolution (HGC) layers designed to handle edge-heterogeneity and 2) Adopting a contrastive loss function to ensure node-heterogeneity. In rigorous evaluation on two CA-HAR datasets, DHC-HGL significantly outperformed state-of-the-art baselines by 5.8% to 16.7% on Matthews Correlation Coefficient (MCC) and 3.0% to 8.4% on Macro F1 scores. UMAP visualizations of learned CA-HAR node embeddings are also presented to enhance model explainability. Our code is publicly available1 to encourage further research.
Semantic Loss

Deep Learning models are a standard solution for sensor-based Human Activity Recognition (HAR), but their deployment is often limited by labeled data scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate these issues by infusing knowledge about context information into HAR deep learning classifiers. However, existing NeSy methods for context-aware HAR require computationally expensive symbolic reasoners during classification, making them less suitable for deployment on resource-constrained devices (e.g., mobile devices). Additionally, NeSy approaches for context-aware HAR have never been evaluated on in-the-wild datasets, and their generalization capabilities in real-world scenarios are questionable. In this work, we propose a novel approach based on a semantic loss function that infuses knowledge constraints in the HAR model during the training phase, avoiding symbolic reasoning during classification. Our results on scripted and in-the-wild datasets show the impact of different semantic loss functions in outperforming a purely data-driven model. We also compare our solution with existing NeSy methods and analyze each approach's strengths and weaknesses. Our semantic loss remains the only NeSy solution that can be deployed as a single DNN without the need for symbolic reasoning modules, reaching recognition rates close (and better in some cases) to existing approaches.
TS2ACT

Human Activity Recognition (HAR) based on embedded sensor data has become a popular research topic in ubiquitous computing, which has a wide range of practical applications in various fields such as human-computer interaction, healthcare, and motion tracking. Due to the difficulties of annotating sensing data, unsupervised and semi-supervised HAR methods are extensively studied, but their performance gap to the fully-supervised methods is notable. In this paper, we proposed a novel cross-modal co-learning approach called TS2ACT to achieve few-shot HAR. It introduces a cross-modal dataset augmentation method that uses the semantic-rich label text to search for human activity images to form an augmented dataset consisting of partially-labeled time series and fully-labeled images. Then it adopts a pre-trained CLIP image encoder to jointly train with a time series encoder using contrastive learning, where the time series and images are brought closer in feature space if they belong to the same activity class. For inference, the feature extracted from the input time series is compared with the embedding of a pre-trained CLIP text encoder using prompt learning, and the best match is output as the HAR classification results. We conducted extensive experiments on four public datasets to evaluate the performance of the proposed method. The numerical results show that TS2ACT significantly outperforms the state-of-the-art HAR methods, and it achieves performance close to or better than the fully supervised methods even using as few as 1% labeled data for model training. The source codes of TS2ACT are publicly available on GitHub1.
Blockchain Application in Fishery Supply Chain

With 1.07% of the country's overall GDP coming from fishing, the fishing industry has huge significance in India. The supply chain and overall exports to foreign nations will be improved by revolutionizing this sector of the economy. Consumers are nowadays very much concerned about their health and in knowing about the quality of the goods they purchase or consume, particularly when it comes to nutrition. Fish must be consumed within a certain time frame and under the right circumstances because it is a perishable food. The government officials must also keep an eye on illicit fishing, which is another serious problem that disrupts ecosystems in many different places. In other words, consumers, the government, and society in general demand transparency throughout the entire value chain of fish products. In this research, the use of blockchain using Ethereum-based smart contracts to trace fish packages back and forth throughout the entire fisheries value chain is explored.
Non-Fungible Token valuation: State of the art and future insight

Purpose: This paper aims to provide an exploratory analysis of Non-Fungible Tokens (NFTs) valuation. NFTs are a new kind of digital asset born out of the dis-ruptive technologies' introduction (i.e., blockchain). A lot of small and medium en-terprises (SMEs), as innovative start-ups, are involved in this domain. Nowadays, several issues in the evaluation field remain unclear. To fill this gap, this research adopts a holistic approach is crucial to draw a clear picture of the first-time ac-counting treatment of these new digital assets. Design/methodology/approach: Using a structured approach, this research considers some of the state-of-the-art international practices and reviews some major scholars' opinions on the matter. Particularly, the study analyses the main contributions provided by international entities (e.g., European Financial Reporting Advisory Group - EFRAG, Chartered Business Valuators institute - CBV, PriceWa-terhouseCoopers - PWC), digital operators, and academia. Findings: Based on the two main strands defined for the NFT's nature, NFTs valuation issues can be resumed in twofold. The first one that considers NFTs as intangible assets suggests following the traditional valuation approaches (cost, in-come, or market) that is already a part of international accounting standards. The second strand that considers NFTs as financial assets proposes a different valua-tion approach based on quantitative methods coming mainly from finance fun-damentals. Originality/value: The originality of this study includes the different NFT val-uation approaches, which enrich the literature and can help SMEs in managing and accounting for this new kind of digital asset.
An extensive overview on dark web

The dark web has become an infamous cybercrime platform, enticing criminals with the promise of privacy and encryption. This review paper will delve into the dark web's history and evolution, along with the characteristics that make it an attractive arena for cybercriminals. It will explore various types of crimes occurring on the dark web, such as cyberattacks, data breaches, and online fraud, examining their societal impacts. Additionally, this paper will scrutinize law enforcement activities, cybersecurity technologies, and public education programs designed to thwart crimes on the dark web. It will propose suggestions for future research and policy actions aimed at mitigating the adverse effects of cybercrime on the dark web. This study aims to foster a deeper understanding of the dark web and the societal risks posed by cybercrime, influencing the development of effective strategies to prevent such occurrences.
Oversight crypto-virtual storage application

The Oversight Crypto-Virtual Storage application is designed as a virtual storage solution aimed at curtailing crimes involving data, such as data theft and tampering. The application enables users to privately store files by creating an encrypted box. Data storage is available for a predetermined amount of time. Once this period ends, the user's Box of files will prompt the user to decide whether to extend the storage duration. If the user chooses not to renew their service, the box becomes accessible to everyone on the platform.
Automatic Content Creation System for Augmented Reality Maintenance Applications for Legacy Machines

Augmented reality (AR) applications have great potential to assist maintenance workers in their operations. However, creating AR solutions is time-consuming and laborious, which limits its widespread adoption in the industry. It therefore often happens that even with the latest generation machines, instead of an AR solution, the user only receives an electronic manual for the equipment operation and maintenance. This is commonplace with legacy machines. For this reason, solutions are required that simplify the creation of such AR solutions. This paper presents an approach using an electronic manual as a basis to create fast and cost-effective AR solutions for maintenance. As part of the approach, an application was developed to automatically identify and subdivide the chapters of electronic manuals via the bookmarks in the table of contents. The contents are then automatically uploaded to a central server and indexed with a suitable marker to make the data retrievable. The prepared content can then be accessed for creating context-related AR instructions via the marker. The application is characterized by the fact that no developers or experts are required to prepare the information. In addition to complying with common design criteria, the clear presentation of the contents and the intuitive use of the system offer added value for the performance of maintenance tasks. Together, these two elements form a novel way to retrofit legacy machines with AR maintenance instructions. The practical validation of the system took place in a factory environment. For this purpose, the content was created for a filter change on a CNC milling machine. The results show that inexperienced users can extract appropriate content with the software application. Furthermore, it is shown that maintenance workers, can access the content with an AR application developed for the Microsoft HoloLens 2 and complete simple tasks provided in the manufacturer's electronic manual.
Cycle time reduction through a novel view planning concept for hybrid white light interferometry-based inspection

Inspecting large areas with a white light interferometer is time-consuming. To shorten inspection times, additionally, a coarser sensor can be used in a preceding step to identify possible defects. These are afterwards accurately measured with the white light interferometer. Often, several points of interest can be grouped and scanned with a single white light interferometer measurement. Grouping approaches found in the literature show potential for further improvement. In this paper, a novel algorithm for view planning concerning a two-step inspection is presented. Respective literature is reviewed and discussed. Afterwards, the functionality of the algorithm is demonstrated through an example and its performance is evaluated based on comparison to other solutions. The approach generates results that are promising for reducing inspection times further than other approaches.
Automatic offline path planning of robots grinding multi-curved surfaces on large ship propellers – A human-in-the-loop approach

Multi-curved components represent a particular challenge when machining with robots, as the machining is subject to a wide range of technical limits depending on the robot system used. An example of such a multi-curved component can be found in the ship propeller, which is one of the most important components of a ship and represents a relatively complex part in the production process. Manufacturing in the casting process is resource- and time-consuming and requires extensive finishing, which also includes grinding to final size. Technologically, this operation is still performed manually or mechanically assisted and requires employees with a high level of experience in the operation and handling of grinding technology. In order to prevent the increasing shortage of skilled workers and to make the finishing process independent from employee-specific knowledge, it is necessary to develop an automatic grinding process. In this context, offline path planning for multi-curved surfaces has emerged as a particular challenge, since existing approaches do not sufficiently consider the robot axis positions nor the pose limits of the end effectors. This work starts by presenting the current state of research and then introduces a human-in-the-loop approach that solves the problem of 3D trajectory planning using two-dimensional viewing planes. The trajectories thus found are projected onto the surface of the propeller and analyzed for potential collisions with the robot, grinding efficiency and reachability. Furthermore, it is possible to calculate an automatic removal characteristic by overlaying a simplified casting model and ideal model of a propeller.
Sample size prediction for anomaly detection in locks

Artificial intelligence in manufacturing systems is currently most used for quality control and predictive maintenance. In the lock industry, quality control of final assembled cylinder lock is still done by hand, wearing out the operators’ wrists and introducing subjectivity which negatively affects reliability. Studies have shown that quality control can be automated using machine-learning to analyse torque measurements from the locks. The resulting performance of the approach depends on the dimensionality and size of the training dataset but unfortunately, the process of gathering data can be expensive so the amount collected data should therefore be minimized with respect to an acceptable performance measure. The dimensionality can be reduced with a method called Principal Component Analysis and the training dataset size can be estimated by repeated testing of the algorithms with smaller datasets of different sizes, which then can be used to extrapolate the expected performance for larger datasets. The purpose of this study is to evaluate the state-of-the-art methods to predict and minimize the needed sample size for commonly used machine-learning algorithms to reach an acceptable anomaly detection accuracy using torque measurements from locks. The results show that the learning curve with the best fit to the training data does not always give the best predictions. Instead, performance depends on the amount of data used to create the curve and the particular machine-learning algorithm used. Overall, the exponential and power-law functions gave the most reliable predictions and the use of principal component analysis greatly reduced the learning effort for the machine-learning algorithms. With torque measurements from 50-150 locks, we predicted a detection accuracy of over 95% while the current method of using the human tactile sense gives only 16% accuracy.
Improving the credibility of the extracted position from a vast collection of job offers with machine learning ensemble methods

The main aim of this paper is to evaluate crawlers collecting the job offers from websites. In particular the research is focused on checking the effectiveness of ensemble machine learning methods for the validity of extracted position from the job ads. Moreover, in order to significantly reduce the training time of the algorithms (Random Forests and XGBoost), granularity methods were also tested to significantly reduce the input training dataset. Both methods achieved satisfactory results in accuracy and F1 measures, which exceeded 96%. In addition, granulation reduced the input dataset by more than 99%, and the results obtained were only slightly worse (accuracy between 1% and 5%, F1 between 3% and 8%). Thus, it can be concluded that the considered methods can be used in the evaluation of job web crawlers.
THE MAIN STAGES OF THE IMPLEMENTATION OF A SCIENTIFIC PROJECT ON THE FORMATION OF PROFESSIONAL SELF-DETERMINATION OF STUDENTS WITH SPECIAL EDUCATIONAL NEEDS

The main stages of the implementation of a scientific project on the formation of professional self-determination of students with special educational needs are discussed in the article. The project "Formation of professional self-determination of students with special educational needs (SEN) as a national strategy for inclusive education in the Republic of Kazakhstan" was launched in 2022. The purpose of the project: scientific substantiation and practical implementation of the program-methodical support of psychological and pedagogical accompaniment for the process of forming the readiness of students with special educational needs for professional self-determination, taking into account the strategy of inclusive education in the Republic of Kazakhstan. The project involves lecturers of the Department of Pedagogy and Educational Management and the Department of General and Applied Psychology of the Faculty of Philosophy and Political Science of Al-Farabi Kazakh National University. The article was prepared as a part of the funding of the Project: ИРН АР 14872130 "Formation of professional self-determination of students with special educational needs (SEN) as a national strategy in the field of inclusive education in the Republic of Kazakhstan", which won the Grant following the results of the Competition of the Ministry of Education and Science of the Republic of Kazakhstan for grant funding of scientific and ( or) scientific and technical projects for 2022-2024, as well as the goal of the sustainable development goals (“Sustainable Development Goals”, 2023), i.e. education, where among the indicators is ensuring access to inclusive education, within the framework of the concept of lifelong education. Keywords: advanced training courses, professional self-determination, students with special educational needs
SurfShare

Shared Mixed Reality experiences allow two co-located users to collaborate on both physical and digital tasks with familiar social protocols. However, extending the same to remote collaboration is limited by cumbersome setups for aligning distinct physical environments and the lack of access to remote physical artifacts. We present SurfShare, a general-purpose symmetric remote collaboration system with mixed-reality head-mounted displays (HMDs). Our system shares a spatially consistent physical-virtual workspace between two remote users, anchored on a physical plane in each environment (e.g., a desk or wall). The video feed of each user's physical surface is overlaid virtually on the other side, creating a shared view of the physical space. We integrate the physical and virtual workspace through virtual replication. Users can transmute physical objects to the virtual space as virtual replicas. Our system is lightweight, implemented using only the capabilities of the headset, without requiring any modifications to the environment (e.g. cameras or motion tracking hardware). We discuss the design, implementation, and interaction capabilities of our prototype, and demonstrate the utility of SurfShare through four example applications. In a user experiment with a comprehensive prototyping task, we found that SurfShare provides a physical-virtual workspace that supports low-fi prototyping with flexible proxemics and fluid collaboration dynamics.