Reviewerly/exHarmony huggingface dataset:
Image Steganography Based on Pixel Topology and Threshold on Selected Pixels Differences

In this paper, the author has suggested a steganography technique that exploits pixels topology with a pre-determined embedding threshold that uses RGB (Red, Green, Blue) values of the cover image, changes them to binary and uses the least significant bit (LSB) method of replacement for twenty four bit colored image to embed the confidential image's RGB bits in the cover-image's RGB bits. The method used in embedding and extracting the secret image is novel and will be discussed thoroughly in the paper, and the quality metric used was the Peak Signal to Noise Ratio (PSNR) to measure the difference in the cover before and after embedding the secret image. Experimental results denote impressive PSNR db values where the stego image in terms of visual is indistinguishable from the original, and the secret image extracted from the stego-image is identical copy of the original embedded secret image with 100-db PSNR.
Artificial Intelligence Enabled Scale Invariant Object Detection and Tracking Model on Color Images

In the zone of pattern recognition and machine learning, object detection and tracking are gaining interest because of its range of applications like visual surveillance. Recently, several deep learning-related techniques are provided for object classification however, there exists some set of concerns or problems that minimizes the overall classifier accuracy. Congest conditions, complex backgrounds, and similarities among various objects become some challenging problems. Large scale variation over object samples, and particularly, the task of detecting small objects turns out to be one such factor for the performance differences. This study introduces an Artificial Intelligence Enabled Scale Invariant Object Detection and Tracking (AIE-SIODC) model on Color Images. The presented AIE-SIODC model intends to recognize and track the objects exist in the color images irrespective of scaling. It follows a two stage process namely object detection and objects classification. At the initial stage, the presented AIE-SIODC technique applies path augmented RetinaNet (PA-RetinaNet) based object detection module, which improves the feature extraction process. To improvise the network potentiality of the PA-RetinaNet method, the deer hunting optimization (DHO) algorithm is utilized. Finally, extreme gradient boosting (XGBoost) classifier is exploited for classification procedures. For examining the effectual object detection outcomes of the AIE-SIODC technique, a wide ranging experimental validation process can be performed. The simulation values confirmed the enhancements of the AIE-SIODC method over other DL models.
ARTIFICIAL INTELLIGENCE AVATAR FOR CONVERSATIONAL AGENT

Artificial Intelligence Avatar for Conversation Agents Research consists of two phases: 1) synthesize the elements of artificial intelligence avatars for conversation agents; Step 1 Synthesize the elements of the artificial intelligence avatar; Step 2 Synthesize the conversation agent elements. Phase 2 Develop the Artificial Intelligence Avatar Conceptual Framework for Conversation Agents. The results showed that the elements of the artificial intelligence avatar consist of four elements: 1) Nonverbal, 2) Verbal, 3) Appearance characteristics, and 4) Machine Learning. The conversation agent elements consist of five elements: 1) User, 2) Application Programming Interface (API), 3) Database, 4) Natural Language Processing (NLP), 5) Content Management System (CMS) and 6) Admin. The Artificial Intelligence Avatar Conceptual Framework for Conversation Agents consists of two elements: 1) Artificial Intelligence Avatar and 2) Conversation Agents.
Solar Radiation Estimation for Renewable Energy Artificial Intelligence

The many techniques for estimating solar radiation, including empirical models, time series, artificial intelligence algorithms, and hybrid models, will all be thoroughly reviewed in this review article. Accurate understanding of solar radiation data is widely accepted as being essential to maximizing its advantages in a variety of fields, including agriculture, energy production, heat, health, and tourism. The study will assess each model's performance success in various places and emphasize its benefits and drawbacks. Also highlighted will be the research gaps, obstacles, and future prospects in solar radiation estimate. We shall underline that empirical models are preferred for their usability and inexpensive computing costs, although hybrid models typically tend to produce more accurate findings. The article will also discuss how crucial it is to estimate solar radiation while taking into account atmospheric, geographic, climatic, and historical data. To anticipate solar radiation in a certain area more precisely, this knowledge is crucial. The paper will also discuss the difficulties researchers have while choosing the best model and how to solve these difficulties. The study will also emphasize how important it is to carry out further research in this field in order to improve the precision of solar radiation estimation and its influence on sustainable development. Researchers, stakeholders, and policymakers may use the review to get important information that will help them decide how to use solar radiation for sustainable development. In the end, the article will give a thorough summary of the related work for estimating solar radiation and point out potential areas for further investigation.
Robust Content Authentication and Tampering Detection of Arabic Text Transmitted Through Internet

Text information is a natural language dependent. Thus, enhancing the reliability and security of text data interchanged with Internet networks becomes a stimulating challenge confronted by the researcher workers. Tamper detection, Content authentication, and integrity verification of digital content will be a primary concern in the information exchange and communication area through the Internet. For data security, several techniques and algorithms are presented, namely copyright protection, identification of owners, verification of integrity, access control, detection of tampering, and the authentication of content. Automated and steganography approaches to watermarking are widely used to resolve this shortcoming. This study developed a Robust Content Authentication, and Tampering Detection of Arabic Text (RCATD-AT) Transmitted Through the Internet. The presented RCATD-AT technique utilizes the 4th level order of word process depending upon the hidden Markov method. The derived features are utilized as watermark data and combined with the watermarking scheme. The presented RCATD-AT technique embeds the text in the watermark information and transmits it to the destination through the Internet. At the receiving end, the RCATD-AT technique performs an extraction process to retrieve the original Arabic text. A series of simulations were performed to portray the improvised performance of the RCATD-AT methodology. The comprehensive comparison study highlighted the improved outcomes of the RCATD-AT methodology.
A Testbench for Stereo-Processing Acceleration Based on PYNQ and the StereoPi

Stereo processing accelerators are often designed and implemented using Field Programmable logic. In this paper, a new setup for the implementation and practical testing of hardware stereo-processing solutions is presented. A PYNQ Z1 board featuring a Zynq SoC is combined with a low-cost StereoPi camera. It is shown that the system is versatile, since it can exploit the optimized OpenCV capabilities of the Raspberry Pi Compute Module for image pre-processing and the hardware acceleration capabilities of the Programmable Logic embedded in the Zynq chip. A series of experiments prove the feasibility of the concept. A state-of-the-art implementation of the SGM stereo algorithm in hardware description language is used in order to prove the feasibility of the design. The system can process depth maps with the nominal refresh rate of 60 fps, using stereo image resolution 640×480 and up to 96 levels of disparity.
Evaluating an In-Home Exercise Program Using Vision-Based Edge AI for Elderly Healthcare

In Japan, many in-home exercise programs have appeared to improve the motor function of the elderly through self-care. However, a challenging point always exists in evaluating the process of exercise programs quantitively and non-invasively for further reflecting the health status. As a preliminary progress, we present and discuss a vision-based novel method on edge that aims to evaluate an in-home exercise program in this paper. Our key idea is to integrate multiple different vision-based pre-trained models. In the proposed method, we mainly integrate and call multiple pre-trained models to recognize human facial, skeletal, and hand movements with edge computing. Also, we propose to evaluate an in-home exercise program process from the Euclidean distance, velocity, and angle of the two-dimensional coordinates of feature points in the time series. Through experiments, we estimate the physical health status from the analysis of body movements in the elderly.
Am Ende Der Welt: Shifting MR Boundaries With a Co-Located Multiplayer with Meta Quest 2

Am Ende der Welt emerged in 2023 from the collaboration between the research project XR_Unites from the Berlin University of Applied Sciences and an independent artist working group consisting mainly of Nora Krahl, Amir Baltić, and Christoph Clausen. This collaboration resulted in a mixed reality (MR) art application for the Meta Quest 2 headset that explores the boundaries of blending the entirely virtual and real worlds in a co-located multiplayer setup. This article exposes in which ways the virtual world mixes with the real world within the application and how they have been developed. Furthermore, it discusses how to exploit the passthrough technology and create a safe co-located multiplayer experience in the Unity game engine.
Implications of Agile Project Management on Performance Management

The objective of this paper is to identify shortcomings related to performance management in an agile project environment, e.g., how performance management affects individual performance; and evaluate them in the sense of potential negative impacts on project and organizational performance. On this basis, potential solutions, and measures for a redesign of performance management systems have been proposed. This research paper uses contextual research questions and qualitative analysis and suggests several ways to redesign performance management in agile projects.
Automated Verification of the Signatures' Authenticity Using Artificial Intelligence Methods

The paper presents the approach to identify fake signatures based on the image analysis. The problem is related with the forensics operations in order to distinguish handwritten signatures made by the human from the machine-originated counterfeits. The identification system is based on the selected artificial intelligence-based classifiers and processes features extracted from the signature images. The source material comes from either human or the 5-dimensional printer, being able to mimic a hand with the pen writing on the sheet of paper. Each image is then processed by the profilometer to extract important information allowing for distinguishing the original signature from the false one. Features include attributes related with the pen's position and inclination, obtained through the Fourier and wavelet transformations. The identification is then made by the intelligent classifier. For the project multiple algorithms were selected and tested regarding their accuracy, such as multilayered perceptron, decision tree or k Nearest Neighbors classifier. Presented experimental results show the ability of the system to support human during the task of the signature authenticity verification.
Multi-Stream Pill Recognition with Attention

We address the challenge of pill recognition by proposing a novel approach that utilizes a multi-stream network with EfficientNet-B0 and a self-attention mechanism. To get rid of the explicit training of printed or embossed patterns we used LBP features. For evaluation two datasets were utilized. In the test scenarios our proposed model outperformed the previous models in Top-1 and Top-5 accuracy, also the YOLOv7 network in a reference-quality use-case.
XR Real-Time Multiuser Collaboration Environment

This paper presents a developed prototype, which aims towards building a base for collaborative XR experiences. This prototype will be presented, within the context of a virtual tourism installation, where users can interact with spatially synchronized augmented reality objects and exchange information, while seeing additional information provided by projectors. Within this prototype, multiple users using smartglasses can see a model-size touristic destination. In it, an AR object controls what is displayed by the projectors through a set of virtual cameras, aiming towards surrounding the users with close-to-real-life perspectives of the touristic buildings of interest. Collaborative XR environments allow users to interact with the same digital environment from a variety of both devices, and physical locations. This paper will also cover some state-of-the-art approaches to implementing collaborative XR.
Combining Handcrafted and CNN Features for Robust Breast Cancer Detection Using Ultrasound Images

Breast cancer is a disease with an exponentially increasing risk of death and challenging to cure, at advanced stages. Therefore, early detection with adequate precision can considerably increase the probability of survival. Breast cancer diagnosis commonly involves the use of Breast Ultrasound (BUS) imaging. For interpretation of BUS images required expert radiologists. However, unavailability of expert radiologists is a major concern specifically for developing countries like India. Computer-Aided Diagnosis (CAD) systems play a crucial role in aiding clinicians with the early detection of breast cancer. Over the recent years, numerous research efforts have been directed towards devising solutions for detecting breast cancer, with a predominant focus on utilizing Convolutional Neural Networks (CNNs) The CNN based solutions have provided promising results due its automatic high level feature extraction capability; however incorporation of local handcrafted features can improve the classification performance because CNN extracts global features from an image which are not sufficient for accurate classification. Therefore, this paper proposes a breast cancer detection system named Hybrid Expert System for Breast Cancer (HESBC), which uses a combination of local hand crafted features and CNN features. For hand crafted feature Histogram Oriented Gradient (HOG) is utilized whereas the ResNet50 is employed for CNN feature extraction. Finally the fused feature vector is used to classify breast tumors using SVM. The HESBC is being tested using the BUS dataset, and achieving a mean classification accuracy of 85.76% with Precision, Recall, and F1-score of 0.8516, 0.8395, and 0.8428, respectively, using the 10-fold cross-validation method.
Ant Colony Optimization for Resource Allocation in Cloud Computing Environments

This article delves into cloud computing technologies, addressing the challenges and concerns that arise in this domain, specifically focusing on resource allocation. We investigate and compare three optimization algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA)—in the context of their applications, objectives, and mechanisms of action. Additionally, the article highlights the significance of each algorithm in optimizing cloud resources and discusses their advantages and drawbacks. For cloud computing with higher complexity, it becomes increasingly important to develop efficient strategies for managing and allocating resources, which in turn helps organizations control costs and improve performance. Prepared comparative analysis aims to provide a comprehensive understanding of the three algorithms, equipping decision-makers with the knowledge to select the most suitable optimization method for their specific cloud computing needs and challenges.
Solving the Two-Criteria Maximum Coverage Location Problem of Continuous Area Monitored by Sensor Network

The paper investigates the problem of optimizing a sensor network for monitoring a continuous area, considering the bounded coverage areas of sensors. This task is formulated in terms of the maximum coverage location problem. A mathematical model is proposed as a two-criteria optimization problem. The objective functions are the maximum area of the covered part of the region and the minimum total overlapping of sensor coverage areas. This model is transformed into an elastic (quasi-physical quasi-human) model, which differs from the known one in forming the extrusion potential energy function. To solve the problem, an original approach was implemented, combining local and global optimization stages. At the stage of local optimization, the Broyden-Fletcher-Goldfarb-Shanno method was used, in which the gradients were calculated analytically or from first-order differences depending on the shape of sensor coverage areas. At the stage of local optimization, the multistart method was used. The implementation of the approach has been tested for the polygonal shape of the region and elliptical shapes of the sensor coverage areas.
FPGA-based Accelerator for FFT-Processing in Edge Computing

Edge computing bridges the gap between end devices on the Internet of Things (IoT) and central, server-based IT services by accelerating tasks which otherwise could not be executed on the IoT device itself. The Fast Fourier Transformation (FFT) is used as an example for the arithmetic tasks to be supported. To support such specific applications, we envisage a concept for edge computing in which co-processing is implemented using Field Programmable Gate Arrays (FPGAs). This article first describes certain edge computing requirements using the example of moving drones. This involves the case-by-case reconfiguration of the computation hardware and a handover mechanism from one edge node to another. Then follows the description of an FPGA-based FFT accelerator unit and its extension to an FFT co-processor. The development took place under the proviso that, for example, drones can request different co-processors and that the drones can also change the edge node when changing a radio cell, which results in a handover of the co-processor context. Regarding the FPGA part this means that the co-processor is part of a partially reconfigurable environment, and the co-processor must support the handover mechanisms in terms of hardware. Finally, the need for FPGA resources is analyzed, also in comparison with alternative solutions.
Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection

Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets.
Artificial Intelligence Contribution to Forensic Science Crime Investigation

In this new digital era, now the world is of technology, and as time passes, we have seen tremendous growth or a blend of digitalization with technology. On the other hand, both traditional and modern crimes are exponentially growing worldwide. With a diverse variety of crimes in almost every domain, technological innovations come into the picture to identify, detect, analyse, and tackle the different types of crimes as per their nature. Artificial Intelligence (AI) is one of the most renounced technologies in the digital forensics landscape. AI plays a vital role in providing high accuracy, effectiveness, and efficiency. It provides the most authentic and reliable source from where the information has formed. Digital Forensics is a separate vast branch in the domain of Forensic Sciences. It has been a recognized contributor since the concept came into light. The paper reviews the significance of AI in digital forensic investigation. The paper examines the contribution of the technology and the hike in the success rate it has brought to the legal system to effectively handle these investigations. The paper draws attention towards the challenges faced by Digital forensics when they together pave a path for an effective criminal justice system. The paper mentions the efficacy of the perfect blend of Digital Forensics in the criminal justice system aiming to provide the efficient disposal of matters. The study provides clarity with suggestions and recommendations for tackling the challenges that the combination of AI and Digital forensics faces when implemented in the criminal justice system to investigate various kinds of criminal investigations.
Early Detection Of Breast Cancer By Using Machine Learning Algorithms

since antiquity, Cancer has been the proverbial ‘Gordian Knot’ for the doctors but a permanent cure of this malady is still an enigma. The essential pre-requisite for complete cure of this disease is its early detection. However, the early detection of a malignant tumor is fraught with many practical hiccups. Of late, the use of machine learning techniques has ushered in a silent but significant revolution in the arduous field of early detection of cancer including breast cancer also, which is the second most common form of cancer, plaguing the humankind. The cardinal objective of this study is to predict the occurrence of breast cancer in patients by using three classifiers i.e., Decision Tree, Logistic Regression, Random Forest. Results obtained from the current study categorically confirm that Random Forest Classifier is the most appropriate algorithm which can be employed to identify the occurrence of breast cancer among the patients.