Reviewerly/exHarmony huggingface dataset:
Building the method for multi-target tracking based on the combination of PHD filter and JPDA filter using particle filter in 3D mixed coordinate system

Changing the target number, non-linear measurement models and non-Gaussian noise faces a challenge to multi-target tracking problems which are factors affecting the accuracy, execution time and deciding the success of the method as well. In this paper, the authors present a method to solve these problems. Wherein, the motion of targets is represented in the mixed coordinate system 3D base on combining PHD (Probability Hypothesis Density) and JPDA (Joint Probability Data Association). This method can track multiple targets in the most general case, that is to change the target number, system model and measurement model which is non-linear as the noise is non-Gaussian. The result of this work can be applied to the real-time response system when the targets are moving in close distances with rapid maneuvering.
Designing a pattern language to enhance model composability and reusability: An example with component-based probabilistic models

This paper presents a pattern language for developing Object-Oriented Bayesian Networks (OOBN), as a member of the component-based probabilistic models family, to tackle complex problems. The proposed pattern language integrates knowledge from various domains, such as modeling, software engineering, and Bayesian networks, to provide a comprehensive solution for developing OOBNs. The paper also provides a validation framework to evaluate the pattern language. As a practical application for the OOBN pattern language, a case study of using it to develop an OOBN is presented. The model in the case study aims to represent the complex interconnections among the Sustainable Development Goals (SDG), long-term sustainability and resilience. The results of the case study validate the effectiveness of the pattern language and highlight its potential for future applications. The proposed OOBN pattern language provides a systematic approach to the development of OOBN, reducing the complexity and increasing the efficiency of their modeling process
A Study on Distributed Machine Learning Techniques for Large-Scale Weather Forecasting

The weather data generated, processed, and collected by the sensor or shared by IoT devices and mobile devices has significantly increased weather data collection in daily life. The data generation speed also accelerated, and a vast amount of data has been collected and stored in distributed databases, improving weather forecasting. Still, the conventional processing method for massive data is distributed and centralized computing, and this chapter looks into how distributed machine learning techniques help as to increase the processing speed. Some distributed frameworks that play a significant role in massive data, like MapReduce, have been trained and tested to resolve various machine learning problems in a distributed environment. The aim of this chapter will provide different information about datasets, issues, platforms, and optimized approaches in a distributed environment. So, researchers can use and deploy new techniques in machine learning algorithms. It helps the researchers develop new strategies in distributed computing environments.
Ethical Value Exchange in HCI

The aim of this panel is to facilitate a discussion on the ethical implications of our activities as HCI researchers and designers. These implications can be formulated in terms of an ethical value exchange, in which research and design decisions are transactions where all stakeholders give and obtain some value. The panel will generate a thought-provoking debate based on different experiences and reflections on whether and how ethical value exchange happens in HCI research and design practice.
Retrofitting AMD x86 Processors with Active Virtual Machine Introspection Capabilities

Active virtual machine introspection mechanisms intercept the control flow of a virtual machine running on top of a hypervisor. They enable external tools to monitor and inspect the state at predetermined locations of interest synchronous to the execution of the system. Such mechanisms, in particular, require support from the processor vendor by facilitating interpositioning. This support is missing on AMD x86 processors, leading to inferior introspection solutions. We outline implicit assumptions about active introspection mechanisms in previous work, offer constructions for solution strategies on AMD systems and discuss stealthiness and correctness. Finally, we show empirically that such retrofitted software solutions exhibit performance metrics in the same order of magnitude as native hardware solutions.
SamS-Vis: A Tool to Visualize Summary View Using Sampled Data

Many recent visual analytics tools use exploratory model analysis workflow to enable users exploring set of potential machine/deep learning models. As part of the workflow, these tools provide summary view of underlying dataset to enable the users to better understand trends in their data. Due to the iterative nature of such workflows, users may need to go back to data exploration phase multiple times. In order to save time and resources at data pre-processing and visualization time, we propose to use sampled data rather than complete dataset for showing trends in data summary views. As a proof-of-concept, we built a visualization tool, called SamS-Vis, that uses five sampling techniques to collect sampled data and then shows the summary views using histogram line-charts. It enables the users to see the whole data summary view of the selected field(s) using histogram bar-chart based on demand.
Integrating Augmented Reality in Learning Type of Roses

Roses are commonly known for their ornamental looks and their usage in the perfumery industry.There are many types of roses available and some of them are rarely found, which limits the opportunity for people to recognize and learn about them.Thus, Learning About Types of Roses Using Augmented Reality is developed which focuses on learning about their species, types and color using mobile augmented reality technology.The project's main goal is to provide an alternative by providing engaging interactive learning to capture the attention of modern generations.The objectives of this project are to identify the scientific name, color, family, shape, and facts about the rose, to develop an augmented reality (AR) application using QR marker on Android platform, and to evaluate the effectiveness of the augmented reality mobile application.The methodology for this project is MADLC which is mobile applications development life cycle.It consists of five phases, which are to identify, design, develop, prototype, test and deploy.Overall, the system's design and execution were successful and performed as intended.The test results demonstrate that the project was successful in adhering to the project objectives.Perhaps, using the Augmented Reality technology would help people learn about roses effectively because they will be able to visualize the roses and learn their features easily.
Forward-backward steps in signal processing

This talk provides an overview of the forward-backward (FB) algorithm, which is a prominent tool for solving optimization problems in signal and image processing. This algorithm belongs to the class of proximal methods and puts in a unifying framework many traditional optimization schemes. In particular, the FB algorithm is instrumental for solving nonsmooth optimization problems which are encountered in sparse estimation or compressed sensing. A higher mathematical view of this algorithm can also be given through modern fixed point theory, making it possible to address sophisticated variational problems. Finally, it is shown that this algorithm is also closely related to feedforward neural networks. This link brings insight into the development of more robust and more explainable neural architectures.
Don't blame people – fix the system

It's easy to blame people when security incidents occur. But while many types of breaches do indeed involve a human element, we should stop to ask ourselves why people acted in the ways they did, and why that was allowed to become a problem. Our systems and processes need to be designed with security embedded deeply within them so that minor lapses on the part of staff members are prevented from becoming a problem.
Good cyber security governance is crucial for us all

It's time we moved on from the traditional focus on technical and operational controls that IT teams need to deploy to defend against attack, and adopt a proper cyber security governance programme.
The shadow of 2022 is still shaping cyberthreats

Recent research has shown how the global events of 2022 are still affecting the nature of cyberthreats today and possibly into the future. This article takes a closer look at the findings and what they mean for businesses, including a detailed rundown of the most prevalent threats, methods and attack vectors used.
Technological transformation in accounting firms: Utopian or strategic?

Technological changes have had a profound impact on the business world, bringing both challenges and opportunities. Technology has allowed innovative minds to develop ever more efficient and precise tools, capable of making accounting tasks more agile and automated. However, the changes have also brought new challenges for companies and entrepreneurs. With the large amount of data available, business management has become a constant concern, as it is necessary to adapt quickly so as not to become obsolete and lose ground in the market. As a result, the creation of auxiliary tools, business strategies and business innovation in the world of accounting services stands out as a game changer.
Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python

Explainable artificial intelligence aims to describe an artificial intelligence model and its predictions. In this research work, this technique is applied to a subject of a Computer Science degree where the programming language changed from Octave to Python. Experiments are performed to analyze the explainability using the SHapley Additive exPlanations algorithm for XGBoost regressor model (for numerical grade prediction) and XGBoost classifier model (for class grade prediction). After the validation and training process, several conclusions are drawn that validate the idea of changing the programming language to a more popular one such as Python. For example, regarding classification problems, the most important feature for the insufficient class in the Octave courses is the practical exam.
An Efficient Multifactor Authentication System

Most typical authentication mechanisms in this digitized world use a username and password to protect information. Two-factor authentication (2FA) mechanisms were introduced to add an extra layer of security. As 2FA is vulnerable to advanced attacks such as real-time replay, multifactor authentications (MFA) are being introduced and require multiple factors or proofs of identity during authentication. Thus, herein, we propose a multifactor authentication methodology using text-based encryption with a self-generated algorithm-Twine and image encryption with GENETIC algorithms. In our approach, when a user attempts to register on a Web page, they must enter details along with essential user id and password, further taking them to an image selection page where they can choose the image of their choice as a second layer of the authentication protocol. Hence, when a user attempts a login into the portal, they can be provided with options to enter via a graphical or text-based password depending on the feasibility and usage of the Web page. The introduction of a lightweight cryptographic algorithm here ensures an improbable zone for machine guessing strategies, and an innovative component for the user’s password, since both graphical and textual, will be hashed and stored and will have no permanent space in the database. It is observed that graphical passwords are easier to use and remembrance. Hence, this could be the next revolution in admin panels for multitude of security driven purposes.
The AI algorithm for text encryption using Steganography

The human being sought to find a human variety of techniques to assure data access with complete confidentiality. The transition from the use of. regular text and audio data to digital media has improved data access and transport. It has become relatively simple to intercept data sent across networks or get access to a variety of machines. Steganography is the study of concealing the existence of communication by embedding hidden data in multimedia payloads such as text, image, audio, and video. This project investigates how to improve steganography by merging text and image production to provide invisible encryption, security, and robustness in digital images. Text hide into another text using quality in the proposed system include Mean Square Error (MSE), Normalized Correlation (NC), and Normalized Cross–correlation Mean Squared Error, Histogram Analysis, Standard Deviation, and Statistical Test and Analysis. The algorithm of our suggested system seems to meet the greatest standards of security, perceptions, and capability. Using the standard of ASCII Control characters for a cover word, the procedure for using the processed system is supplied from the cover sentence. The same method is used for other words from different cover sentences, given the line numbers of each cover sentence and Stego sentence. Keywords: Algorithm, Secret Text, Steganography,
The Role of IT Readiness, Big Data, and Blockchain Technology in Fostering Innovation and Social Sustainability Within UAE Banking Sector: A Comprehensive Analysis’

The main aim of this research is to examine the relationship among IT readiness, big data, blockchain technology, innovation and social sustainability in the context of UAE banks. This research also examined the mediating role of innovation as well. The data was collected from the customers of UAE banks. The questionnaire developed on a five-point Likert scale was distributed among customers using simple random sampling. The response rate of the study was 65.6%. The gathered data was examined using Smart PLS. The study's findings revealed that innovation directly impacts social sustainability. Moreover, Big data adoption. Blockchain adoption and IT readiness have a positive impact on innovation. This study fills the gap of very few studies discussed the block chain in the context of UAE banking sector. This research is also among very limited studies highlighting mediating role of Innovation in the proposed model. These findings are helpful for academicians and policymakers to develop policies regarding social sustainability through technological factors.
Software simulation and visualization of quantum multi-drone reinforcement learning

Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) features non-stationarity and uncertain properties. Therefore, this paper presents a simulation software framework for novel QMARL to control autonomous multi-drones, i.e., quantum multi-drone reinforcement learning. Our proposed framework accomplishes reasonable reward convergence and service quality performance with fewer trainable parameters. Furthermore, it shows more stable training results. Lastly, our proposed software allows us to analyze the training process and results.
Robustness of Intensity-Modulation/Direct-Detection Secret Key Distribution against Spontaneous Raman Scattering in Wavelength-Multiplexed Systems with Existing Optical Transmission Signals

Quantum key distribution or secret key distribution (SKD) has been studied to deliver a secrete key for secure communications, whose security is physically guaranteed. For practical deployment, such systems are desired to be overlaid onto existing wavelength-multiplexing transmission systems, without using a dedicated transmission line. This study analytically investigates the feasibility of the intensity-modulation/direction-detection (IM/DD) SKD scheme being wavelength-multiplexed with conventional wavelength-division-multiplexed (WDM) signals, concerning spontaneous Raman scattering light from conventional optical signals. Simulation results indicate that IM/DD SKD systems are not degraded when they are overlaid onto practically deployed dense WDM transmission systems in the C-band, owing to the feature of the IM/DD SKD scheme, which uses a signal light with an intensity level comparable to conventional optical signals unlike conventional quantum key distribution schemes.
Development of Applications Media Interactive Based on Articulate Storyline 3 for Early Reading

The rapid development of technology can have a positive influence on the learning process. Technology support in learning can be a means in the learning process. The existence of technology can be an instructional scaffold, namely as a form of teacher support to students during the learning process. The purpose of this research is to develop a product in the form of an interactive media application based on articulate storyline for initial reading. This developed product is used by students at the stage of learning to read. The research method used in this study is using 4D research methods. Starting from the define (discovery), design (design), develop (development), and dessiminate (deployment) stages. This product that has been developed has gone through the validation stage, namely validation by media experts and validation by learning content experts (material). The results of validation by media experts of 91% and the results of validation by learning content experts (material) of 93% which shows that this interactive media application is very feasible to use.
Blockchain Application In Management Of Electricity Data

This paper presents a model of using smart electronic meters and network tools to store information about power, price, and customer code in information blocks of Blockchain applications to manage electricity bills. Price transactions will be stored as a data block. The following data block will be linked to the previous, eliminating third-party management. The web user interface is designed to allow customers and administrators to monitor and receive notifications of suspected data attacks. The web application is developed by using Visual Studio software, which supports the C# language, popular among programmers and web developers. The data obtained from the meter is stored using Microsoft SQL Server, a powerful and popular database management system widely used in web applications to improve database services and reduce problems encountered in application development. Experimental model on the EPM5500P meter offers high stability, transparent and secure data management, which can be applied to a large scale such as buildings or industrial areas.
FMEA‐Based Coverage‐Path‐Planning Strategy for Floor‐Cleaning Robots

Floor‐cleaning robots play a crucial role in both industrial and domestic spaces. However, these robots often face challenges due to hazardous components in their environment, which can cause them to fail and prevent them from performing at their best. This situation necessitates continuous research in the field of floor‐cleaning robots. However, most of these efforts focus on improving the robots’ perception capabilities by incorporating additional sensors. Nevertheless, incorporating more sensors is an expensive solution for most cleaning robots. Alternatively, in this research, the feasibility of introducing a safe path on a predefined hazard map is explored. The proposed method aims to trade‐off between area coverage and the safety of the cleaning robot. Herein, the failure mode and effect analysis (FMEA) method is introduced as a tool to classify the hazards and implement a safety‐ensured coverage path‐planning process. In this approach, the risk factor defined for a point in the environment serves as the key parameter to assess the safety of the algorithm's suggested path. To validate and evaluate the proposed method, this article utilizes the hTetro mobile robot. In the experimental results, it is demonstrated that the proposed method can reduce high‐risk movements of the robot compared to existing methods.
Attendance System Based on Face Recognition

Face recognition is one of the most important applications of image processing in the technical world. The process of identifying students using a face biostatistics system based on high-definition monitoring and other computer technologies is called a face recognition attendance system. A machine learning algorithm (Convolutional Neural Network) has been proposed to utilize the system in this paper. The identification of the human face is a current problem for verification purposes, particularly in the context of student attendance. The methods used today to take attendance are cumbersome and time-consuming. Manual recording makes it simple to alter attendance data. Both the current biometric methods and the conventional method of keeping attendance are susceptible to proxies. The development of this system aims to digitally replace the outdated method of taking attendance by calling names and keeping handwritten records. The proposed system for this research will help the students get enough time to focus on their studies, which can make them good contributors to our society.
DiffAds: An Interactive Platform for Personalized Visual Advertisement Generation

Deep understanding of the customer journey followed with the provision of dynamic personalization has been considered to be one of the most effective digital marketing strategies. In this demonstration, based on text-to-image diffusion models, we present DiffAds, an interactive platform for personalized visual advertisement generation. To simulate the process of personalized advertising in digital marketing channels, DiffAds consists of three main stages including style preference discovery, personalized advertising image generation, and user feedback collection. The first stage presents representative images of different visual styles for the user to choose from and the second stage creates the advertising images of a commercial product tailored to user preference. Finally, the third stage allows the user to explicitly rank the generated images as the preference feedback for updating the generative models. The advertising technique behind DiffAds has been practiced in real advertising campaigns of FamilyMart Taiwan (Taiwan's major convenience store chain).
Evaluating the Energy Requirements of Assistive Technologies for Older Adults

The successful adoption of assistive technologies for older adults requires considering and evaluating many different factors and dimensions such as effectiveness, usability, cost and equity of access, to name some of the most relevant. In line with this, the energy requirements to power such assistive technologies remains a hidden factor that might to some extent influence the success in their adoption and the user experience in a wider sense. Very often energy availability is taken for granted and its associated costs and operational requirements are mostly neglected. In this paper, the energy-related requirements of assistive technologies are analysed from a general perspective. This analysis is subsequently particularised for a use case within the SHAPES project, in the context of active and healthy ageing. This use case includes a wide variety of assistive technologies, namely: wearable devices, home sensors and a smart mirror, which provides connectivity and a set of software services. The energy requirements of all these technologies are evaluated and analysed to investigate their impact and relevance on the overall cost and user experience, following the proposed protocol.