Reviewerly/exHarmony huggingface dataset:
An Image-based Fall Detection System for the Elderly using YOLOv5

A system’s capacity to identify whether a person has fallen is known as fall detection. People who fall have difficulty moving their muscles. Even without injuries, the elderly people are too exhausted to stand up. Currently, there is an increasing amount of focus on the protection of old people living alone. The fall detection system using wearable gadgets and environmental monitoring devices has been introduced. Yet, there are certain problems, such as high invasion, poor accuracy, and weak resilience, due to the usage of the Internet of Things and early fall detection warnings. Elderly people frequently need assistance getting up after falling, and if that assistance is delayed, the consequences of falling could be highly serious. Despite extensive studies on the subject conducted over the past few years, creating non-invasive technologies for automatic fall detection that are trustworthy and acceptable to the people being monitored remains difficult. The geriatric population has continued to increase as a result of medical technological advancements. Because falls can result in fractures and have catastrophic repercussions have received a lot of attention. Therefore, it is crucial for both older individuals and those who care for them to identify fall accidents quickly. Some of the fall detection scenarios are people who are irresponsible and stumble, those who are afflicted with ailments like heart attacks or strokes, children playing on the ground, and senior citizens.
A Study on Brain Tumor in Various Fields using Machine Learning

Brain tumors are the result of cells developing fast and irregularly. If it is not treated in the early stages, it could result in death. Precision classification and segmentation remain challenging jobs, despite numerous large initiatives and good outcomes in this discipline. Brain tumor detection is significantly hampered by the variations in tumor location, size, and shape. The identification of anomalies in the brain is a vital undertaking in the medical sciences. The anatomy of brain tumors, feature extraction, augmentation techniques, transfer learning, deep learning, public databases, quantum machine learning and classification of the research of brain tumors were all included in this survey. Medical image processing gives fundamental details on brain abnormalities and aids in the best possible therapy strategy for the patient. There are multiple processes in the image-processing methodology employed to detect brain tumors. Classification, segmentation, feature extraction, and pre-processing were the steps. Several attribute extraction strategies are explored in this survey. The classification and feature extraction methods utilized for image analysis for brain tumor diagnosis are properly reviewed in more than fifteen research articles. Additionally, this study provides all pertinent information for the diagnosis of brain tumors, along with its advantages, disadvantages, developments, and prospects.
Automated Classification of Pathological Types of Lung Cancer - A Machine Learning Approach

Accurate machine learning models can decrease the workload of radiologists and boost the efficiency of the diagnosis process for lung cancer. This study aims to improve the accuracy of the machine learning models specifically Artificial Neural Networks (ANN), Deep Convolutional Neural Networks (DCNN) and Transfer Learning for detecting and categorizing three types of lung cancer - Adenocarcinoma, Squamous cell carcinoma, and Large cell carcinoma. The study uses CT scan image datasets of lung cancer patients in DICOM and PNG formats. Preprocessing steps were performed on the images to improve the accuracy of the models, including conversion to Hounsfield Unit (HU), windowing, conversion to PNG, removal of salt-pepper noise, and masking. The model's performance was evaluated against various metrics such as accuracy, sensitivity and f1 score. We aimed to choose a model which gave the highest accuracy and stability. The results concluded that the Transfer Learning model achieved the highest accuracy of 97%, followed by ANN and DCNN which showed accuracy in the range of 95% - 96% and 94% - 95% respectively. Overall, this study has the potential to improve the accuracy of lung cancer detection and classification, leading to earlier detection and treatment of the disease.
Smart Land Registration Using BlockChain

Land administration systems play a critical role for a wide range of stakeholders involved in land management. However, a significant issue affecting these systems is the accuracy of their stored data, which often fails to align with the actual legal, spatial, and topographic reality. The main causes of data discrepancies in land administration systems stem from challenges related to data collection and compilation, data processing, and data misuse. This paper addresses the challenges of data tampering, lengthy transaction registration times, and the potential for double spending, all contributing to inaccuracies within land administration systems. Our research explores the potential of blockchain technology as a solution, specifically distributed ledger technology, to tackle these problems. We propose a Solidity programming language-based smart contract that addresses specific use cases in land administration systems, such as ownership sharing, partial ownership transfers, real estate splitting or merging, and the imposition of restrictions on real estate trading. The proposed smart contract serves as an implementation of a programming interface that combines elements from ERC-20 and ERC-721 token standards, catering to the specific needs of land administration systems.
Exploring Flush+Reload Side Channel Attack Vulnerabilities: Detection and Countermeasures

Attacks using flush+reload may be used to obtain private data, including encryption keys, cryptographic operations, and more. Because attackers may obtain protected data by analyzing memory access patterns rather than by directly defeating encryption techniques, this presents a serious security risk. Hardware designers may need to include countermeasures at the microarchitecture level to prevent Flush+Reload attacks. This may include modifying cache management rules, adding cache partitioning, or adding further security measures. The performance and design of CPUs may be impacted by these developments. Computer architects and security professionals continue to adjust and improve their defenses against these and related side-channel attacks as technology develops. This research paper presents a detailed study of Flush+Reload vulnerabilities, and how attackers exploit these vulnerabilities to gain access to secret keys of cryptographic algorithms. The methodologies utilized by researchers for information leakage with cryptographic technique AES and RSA while software implementation is analyzed to mitigate the Flush + Reload attacks. Cryptographic libraries may need to use constant-time algorithms, which carry out operations in a set period of time regardless of the quantity of data being processed, to defend against Flush+Reload attacks. These algorithms may be less effective and have an effect on how quickly cryptographic operations are carried out. This research paper also includes the role of artificial intelligence in countering this specific type of cache side channel attack.
Audio Assist: Enabling Object Detection Through Speech for the Visually Impaired

Object detection based on deep learning is an essential application in deep learning technology, which is distinguished by its superior feature learning and feature representation capabilities when compared to traditional object identification approaches. Generally, the normal people can identify where the objects are located easily but it’s not that easy for visually impaired people to identify the objects and its location. Also, it is very challenging to create a computer model to identify the objects. Actually, the object detected will be appear on the screen but it’s not useful for visually impaired that’s why we are using Audi Assist which helps us to convert text to speech. And we are enabling the audio assist technology to gives the audio output after identifying the object. And it describes where the object is it located. This study utilizes a large dataset of objects like mobiles,water bottle, cups and many more. This model is not only trained to detect objects also detects persons. This technology has enormous promise for increasing the freedom and safety of visually impaired people by allowing them to explore and interact with their surroundings more confidently and efficiently. It is a big step towards inclusion and accessibility in our quickly expanding technology ecosystem. The objects detected are translated into meaningful audio cues, providing immediate to user.
Accident Prevention System using Eye Blink Sensor and Alarm for Drivers with Vehicle Auto stop

This project aims to address the increasing rate of accidents by leveraging technology. The system is designed to prevent accidents by using an eye blink sensor for drivers. If the sensor does not detect an eye blink, it triggers an alarm using a buzzer to awaken the driver. Furthermore, the vehicle is programmed to stop automatically under these circumstances. As a precaution the system includes a GSM and Wi Fi module that can send a notification to a designated authority, like the driver’s family members. feature enhances safety by ensuring timely intervention to potentially save the driver’s life. The project thus presents a comprehensive solution to enhance road safety and reduce accidents.
Exploring Team-based Classroom Experiences in Virtual Reality

Remote classrooms are increasingly taking a bigger role in student learning as the world transitions into a post-COVID era. There is hence an increasing need to explore more avenues to improve the learning experiences in such remote classrooms. To provide an initial understanding of the disparities in learning experiences between virtual reality (VR) and traditional mediums, this paper presents a study with 11 participants in team-based classroom learning tasks across three conditions - face-to-face, video conferencing and VR. The findings highlight several benefits and limitations of VR-based classrooms, e.g., the expressivity of avatars and customizability of learning environments in VR allows for a more personalized experience, but excessive interactivity sometimes lead to distractions. These findings shed light on how VR may be best designed to enhance remote classroom experiences.
Embedded FPGA Diagnostics of Multiple Logic Elements using the Stm32 Microcontroller

The article discusses the built-in diagnostics of multiple FPGA logic elements by a single microcontroller. Formulas for estimating the memory for exhaustive search and for diagnostic sequence are presented. The results of testing the microcontroller for multiple functions of logic elements are presented. A genetic algorithm is used to create diagnostic sequences.
Rule-Based Error Classification for Analyzing Differences in Frequent Errors

Finding and fixing errors is a time-consuming task not only for novice programmers but also for expert programmers. Prior work has identified frequent error patterns among various levels of programmers. However, the differences in the tendencies between novices and experts have yet to be revealed. From the knowledge of the frequent errors in each level of programmers, instructors will be able to provide helpful advice for each level of learners.In this paper, we propose a rule-based error classification tool to classify errors in code pairs consisting of wrong and correct programs. We classify errors for 95,631 code pairs and identify 3.47 errors on average, which are submitted by various levels of programmers on an online judge system. The classified errors are used to analyze the differences in frequent errors between novice and expert programmers.The analyzed results show that, as for the same introductory problems, errors made by novices are due to the lack of knowledge in programming, and the mistakes are considered an essential part of the learning process. On the other hand, errors made by experts are due to misunderstandings caused by the carelessness of reading problems or the challenges of solving problems differently than usual.The proposed tool can be used to create error-labeled datasets and for further code-related educational research.
Sports Event Data Acquisition Method Based on Wireless Sensor Technology and BP Neural Networks

By and large, tantamount games were pursued and accumulated by various gatherings in various regions of the planet, prompting the improvement of a great many ethnic sports. Rivalry results for competitors are impacted by a wide assortment of conditions. It is trying for mentors to rapidly get more exact sports execution expectation discoveries and comparing data investigation utilizing the traditional technique, making it inconceivable for them to concoct designated and logical preparation run plans for players' issues. Thus, in this exploration, we construct a model to foresee athletic execution and afterward test and dissect it utilizing data from different sources, all with the assistance of the BP neural organization procedure. The exploratory outcomes show that the BP neural organization technique beats the BP neural organization as far as combination speed and can achieve the normal blunder exactness in a more limited measure of time. Simultaneously, this paper combines the fields of wireless sensor networks and sports preparing to introduce a keen sports preparing framework that works on the back of such networks.
Effect of Electronic Customer Relationship Management Towards Customer Experience with Mediating Factor Service Quality in The Insurance Industry

Electronic customer relationship management (E-CRM) has emerged as a crucial tool for companies seeking to provide a superior customer experience in the insurance sector in India. This study aims to investigate how E-CRM affects customer experience while service quality serves as a mediator. The research methodology is purposive sampling and utilises a quantitative approach, surveying a sample of 300 insurance policyholders. The data was gathered using a meticulously designed questionnaire and subsequently put to analysis employing structural equation modelling using SMART PLS. The study's findings show that E-CRM significantly improves Service Quality and Customer Experience in the insurance industry. This finding is consistent with previous research highlighting the importance of E-CRM in enhancing customer experience in various sectors. Additionally, it has been determined that service quality serves as a partial mediator between E-CRM and customer experience. This implies that service quality is vital in transforming E-CRM resources into an enhanced customer experience. The findings also indicated several implications for insurance companies operating in India. Firstly, the study highlights the importance of investing in E-CRM technologies such as chatbots, mobile apps, and social media platforms to enhance customer experience. Secondly, the study emphasises the importance of maintaining a high level of service quality, which can be achieved by investing in staff training, improving processes, and leveraging technology.
Multilabel Clustering Analysis of the Croatian-English Parallel Corpus Based on Latent Dirichlet Allocation Algorithm

A parallel corpus of Croatian EU legislative documents translated automatically to English over 28 years with a year of creation and hierarchical classifier tags including descriptors, document types, and fields considered as meta information assigned to each text. Only two third part of around 1.5 thousand texts have all the fields completed, accomplishing the required manual work too time-consuming for human administration. Similar incompleteness of legal texts may appear in official legal sites operated as regular service provisioning databases. In this paper we proposed an artificial cognitive and multilabel classification method to automatically find the necessary tags for the corpus with just a tiny fraction of the manual tagging time. The Latent Dirichlet Allocation algorithm assigns field values or tags to incompletely labelled documents. The dependence of the quantitative linguistics properties was presented in the function of the type and specialty of pre-processing tasks. We successfully applied this algorithm built on no error correcting optimising codes to predict a mixture of topic probabilities of these legal texts on the basis of Hamming distance of the binary feature vectors created using the legal fields of the EUROVOC multilingual thesaurus.
A Lightweight Blockchain-Based Cooperative Demand Response and Energy Trading Model for Smart Grid

Public blockchain networks have been used for peer-to-peer energy trading; however, most of these networks are inefficient. The power-intensive consensus algorithm, Proof-of-Work (PoW), is one of the reasons for power inefficiency. This article proposes a lightweight consensus algorithm for demand response and energy trading models to improve efficiency in the smart grid. A cooperative approach encompassing all energy entities is also necessary for demand response management strategies to be more efficient. In the proposed model, we redesign the Proof-of-Fit (PoF) consensus algorithm for a smaller number of message exchanges and computations. We also propose a service-based use of grid infrastructure for energy trading and exchange by all energy trading entities in a cooperative approach while free energy price negotiation is exercised. The simulation results show that the redesigned consensus algorithm, Energy-Proof-Fit, reduces the average number of message broadcasts required to complete a consensus by 35 percent. Furthermore, the peak-to-average ratio of peak hour demand is reduced in the proposed model.
B2B Financial Sector Behavior Concerning Cognitive Chatbots. Personalized Contextual Chatbots in Financial Sector

There are numerous customer service articles on chatbots, but only a few studies deal with their acceptability and the cognitive components of personalization and context have been neglected so far. Due to the increasing usage of chatbots across all industries, our study reveals that B2B firms are beginning to implement intelligent chatbots to enhance customer service. 483 responses were collected from employees of B2B banking organizations. This study investigates the acceptance of cognitive technology in unsupervised settings, revealing that perceived information quality significantly impacts customer experience, while perceived system and service quality do not exhibit notable influence. Regarding cognitive chatbots' contextual knowledge, the findings highlight that factors like ease of use, usefulness, and trust do not significantly impact user attitudes. Furthermore, the research demonstrates that risk perception interacts with system characteristics, notably influencing customer experience and intention to adopt, emphasizing the importance of addressing risk concerns for successful cognitive chatbot integration. This paper provides major theoretical advances to chatbot literature by combining two models into one. Intelligent chatbots boost automation because consumers are more willing to utilize them if they build confidence in them.
Studying the Role of Non-Player Users (Spectator and Operator) in XR Single and Multiplayer Experiences

This paper presents a study on non-player users (NPUs) in experiences where active users (players) use Virtual Reality (VR), Augmented reality (AR) or any other spectrum of Extended Reality (XR). The spectator, a type of NPU, is hidden from players and does not actively participate in the virtual environment but observes the world and the activities of other users to understand their behaviour further, while the operator, another type of NPU, can manipulate the virtual environment without participating in the gameplay and is responsible for a smooth run of the experience. Both types can be implemented in AR or VR but usually have a desktop design. In most XR experiences, the presence of NPUs is unnecessary due to the nature of the application leading to the user base being composed of only players. However, in cases like location-based entertainment, XR training, or onboarding new hires, NPUs can significantly improve the process. The study examines the impact of NPUs on the user experience and the potential benefits and drawbacks of including them in XR experiences. Specifically, in a VR multi-user experience for assessing soft skills, the study found that with a desktop mode of the spectator, the preset camera views, timestamped notes, and audio activity visualization were the most appreciated functionalities.
An Analysis of the Concept of Cognitive Enterprise Architecture Foundation of Cognitive Enterprise Archtecture

There are several approaches to dealing with the interactions between humans and computers in the context of Information Systems and Enterprises. The paper proposes an integrated approach and definition of a research field that combines the available cognitive approaches in IT and architectures for Information Systems and Enterprises. There are established scientific and research disciplines in the said domains such as Enterprise Architecture; Analysis and Design Information Systems; Business Process Management and Modelling; Data Management and Administration; Human-computer interaction and Cognitive Infocommunication. The rapid development of Artificial Intelligence (Machine learning) and Cognitive Informatics and their applications in enterprise environments necessitates defining a research framework that can support the alignment of the elements of this puzzle of components for the purpose of research and practical applications.
Language of the Market: NLP-Driven Sentiment Analysis of Hungarian Economy

This paper introduces deep learning-based natural language processing approach to correlate sentiment trajectory derived from business news with macroeconomic indicators, thereby contributing to a low-latency and nuanced understanding of economic trends. Thus, our work contributes to the field of cognitive infocommunications by establishing a connection between human cognitive processes and an artificially cognitive system. First we labeled the sentiment of a relatively small number of business related sentences. We then trained neural networks for sentiment classification. Generally, FastText- and transfomer-based approaches were investigated. Using the trained sentiment classifiers, we made predictions for over thirty years of news and introduce a monthly aggregation method. As a final step, we compare the predictive sentiment trajectories with macroeconomic indicators such as GDP and PMI. The results of this study indicate that the transformer-based sentiment classifier along with the proposed sentiment aggregation method follows the trajectory of the inspected macroeconomic indicators.