Reviewerly/exHarmony huggingface dataset:
Imbalanced Data Stream Classification using Dynamic Ensemble Selection

Modern streaming data categorization faces significant challenges from concept drift and class imbalanced data. This negatively impacts the output of the classifier, leading to improper classification. Furthermore, other factors such as the overlapping of multiple classes limit the extent of the correctness of the output. This work proposes a novel framework for integrating data pre-processing and dynamic ensemble selection, by formulating the classification framework for the nonstationary drifting imbalanced data stream, which employs the data pre-processing and dynamic ensemble selection techniques. The proposed framework was evaluated using six artificially generated data streams with differing imbalance ratios in combination with two different types of concept drifts. Each stream is composed of 200 chunks of 500 objects described by eight features and contains five concept drifts. Seven pre-processing techniques and two dynamic ensemble selection methods were considered. According to experimental results, data pre-processing combined with Dynamic Ensemble Selection techniques significantly delivers more accuracy when dealing with imbalanced data streams.
GAN-based Homogenous Transfer Learning Method for Regression Problems<sup>*</sup>

In industrial scenarios, the difficulty of collecting a sufficient amount of data for data analysis is a long-standing problem. Transfer learning (TL) is believed to be a potential solution to this problem. TL is a methodology that transfers useful knowledge from previous data and/or tasks in a source domain to help build a more accurate model in a target domain. So far most research is conducted in the fields of computer vision and natural language processing, and thus images or text are taken as inputs. In this study, we aim to propose a TL method for industrial scenarios; specifically, a regression task that has signal data (continuous variables) as inputs. Generative adversarial network (GAN) is widely known for its ability to generate artificial data (images, text, audio, etc) that is similar to real data. However, few previous studies have been done to investigate the performance of GAN in a regression problem setting. This study proposes a GAN-based TL method where an adjusted GAN is designed to tackle the regression problems. To evaluate the model performance of the proposed method, artificial datasets were generated to simulate different types of domain discrepancy. The experimental results suggested that the proposed method can improve the prediction accuracy in the target domain by utilizing a source domain that has similar input distributions and moderately different input-output relationships.
Hybrid Deepfake Detection Utilizing MLP and LSTM

The growing reliance of society on social media for authentic information has done nothing but increase over the past years. This has only raised the potential consequences of the spread of misinformation. One of the growing methods in popularity is to deceive users through the use of a deepfake. A deepfake is a new invention that has come with the latest technological advancements, which enables nefarious online users to replace one's face with a computer-generated, synthetic face of numerous powerful members of society. Deepfake images and videos now provide the means to mimic important political and cultural figures to spread massive amounts of false information. Models that are able to detect these deepfakes to prevent the spread of misinformation are now of tremendous necessity. In this paper, we propose a new deepfake detection schema utilizing two deep learning algorithms: long short-term memory and multilayer perceptron. We evaluate our model using a publicly available dataset named 140k Real and Fake Faces to detect images altered by a deepfake with accuracies achieved as high as 74.7%.
Source Localization in Linear Dynamical Systems using Subspace Model Identification

We study the problem of localizing sources of unknown forced inputs in linear dynamical systems with unknown system matrices. This problem is relevant in several real-world dynamical systems, including power networks and mechanical systems, where the unknown inputs could be forced oscillations or malicious attacks. Localizing sources is key to mitigating the impact of these unwanted inputs on the system’s performance. To this aim, we develop an algorithm that finds sources based on the modal information in the inputs. We obtain this information from the eigenvalues of the (sampled) system matrices, which we estimate using a subspace identification method. Importantly, our algorithm relies on a key assumption that inputs can be appropriately modeled as outputs of some latent linear systems. This assumption allows us to go beyond periodic inputs that are a mainstay in the literature of source localization problems. We illustrate our findings via multiple numerical studies.
Total Commit and Transaction Off-Chains (TCTOC): A Blockchain Scalability Solution

As an evolving technology, blockchain has found its way into multiple fields such as finance and trade and is set for integration in many fields today. It is limited though, by how poorly it performs when network size scales. Poor blockchain performance when experiencing network growth is manifested as a trilemma; transaction throughput, network storage demands and network data propagation. In this paper, existing literature with proposed solutions for the scalability trilemma is identified and analyzed to bring forth a new possible solution. Total Commit and Transaction Off-chains (TCTOC) is proposed as a synthesis of two solutions in the literature: 1) node committees for improved computation, and 2) off-chain transaction channels for improved transaction management. The feasibility of TCTOC is confirmed using the works that constitute the synthesis.
Solving the angular velocity of the physical pendulum motion by some numerical methods

The study of the pendulum problem lies at the intersection of mathematics and physics, with a focus on its representation through differential equations. However, finding analytical solutions using conventional techniques is often challenging. Typically, the analysis is confined to the simplest case of small oscillation amplitudes, where harmonic motion can be accurately described using less complex techniques. Yet, when dealing with large oscillations, the complexity increases, necessitating the use of numerical methods to approximate the solutions of the nonlinear differential equations involved. In this research, we aim to numerically solve for the angular velocity of a damped oscillating rigid pendulum rotating on a pivot under the influence of a uniform gravitational force in the vertical direction. We will propose initial conditions for the differential equation governing the pendulum’s motion and implement classical numerical algorithms, including first-order methods like Euler and Euler Backward, second-order methods such as Runge-Kutta and Adams-Bashforth, and third-and fourth-order methods like Adams-Moulton, Trapezium, and Runge-Kutta. To evaluate the performance of these numerical algorithms, we will compare their results with the solutions obtained using the ODE45 solver from the MATLAB software, known for solving ordinary differential equations. The objective is to identify the most efficient algorithm for accurately solving this mathematical model.
FPGA Processing of Decision Tree Ensembles Stored in External DRAM

Ensemble methods are an interesting alternative to neural networks, especially due to their inherent parallelization capabilities. Forming ensembles from decision trees (e.g. in so-called random forests) has the additional advantage that processing a decision tree can be performed with low-complexity operations. When compared to other machine learning approaches such as (deep) neural networks, tree-based ensemble methods can deliver similar accuracy while being considerably simpler in terms of implementation effort and computational complexity. A major challenge for implementation in resource-constrained hardware such as FPGAs are the memory requirements of decision trees. While on-chip memories on current FPGAs are typically very fast, the available space is often not enough for complex problems, leading to a need to integrate external dynamic memories into the system architecture. In this paper, we propose a hardware architecture and an accompanying subtree-based memory layout that considers the inherent problems of decision trees and the memories’ physical latency constraints to speed up the processing of decision tree ensembles by increasing the bandwidth efficiency of memory transfers. We show that this can increase the processing speed by up to 3.38 times when compared to a naive (linear) memory layout for a single tree and 3.72 in a parallel scenario. This way it enables efficiently implementing tree-based ensemble learners for practical problem sizes on FPGAs.
Accelerating Federated Learning via Modified Local Model Update Based on Individual Performance Metric

The privacy-preserving federated learning (FL) algorithm is considered one of the most widely used distributed training algorithms. Its effectiveness is primarily observed when the datasets on the clients are independent, identically distributed (IID), and balanced. However, in real-world situations, the datasets on the clients are often non-IID, leading to varied data and feature distributions, thereby adversely affecting the performance of federated learning. To address this challenge, this work applies a modified local model update mechanism based on the individual performance metric of all clients’ models under training calculated on a reference testing dataset that resides on the server side. The proposed modification enhances each client model’s performance individually, regardless of its data distribution. For instance, if a client model poorly predicts certain classes, it will receive a higher percentage and weighting factor from other models that perform better in predicting those classes. The empirical studies of the modified algorithm for FedAvg and FedProx aggregation methods under IId and non-IID data distribution with MNIST and CIFAR10 datasets show that this approach can speed up the training process, increase the overall system accuracy, and reduce the number of communication rounds.
Digital transformation: How has telemedicine impacted UAE's healthcare sector during the Covid-19 pandemic?

Over the years, digital transformation has tremendously influenced the health sector. Over time, some new technologies have emerged, enabling medical facilities to provide patients with the best care possible. A rapidly developing area of medicine, telemedicine offers patients an alternative option for receiving high-quality medical treatment. With the COVID-19 outbreak, telemedicine has become more prevalent in connecting patients and medical professionals.This study used a mixed approach to evaluate telemedicine acceptance and satisfaction using the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theories to a range of topic categories and created the outcomes in the form of statistics. The objective is to investigate users' acceptance and satisfaction toward telemedicine in the United Arab Emirates and to analyze relationships between perceived usefulness, perceived ease of use, behavioral intention of use, and attitude toward telemedicine technology.Analysis and conclusions suggested high levels of telemedicine satisfaction in the UAE. This study may be considered one of the first of its kind in the UAE based on 224 survey participants through Qualtrics and 12 patient interviews that direct this report to a conclusion on the future of telemedicine. The report concludes that it found cohesiveness in the qualitative and quantitative analysis results and that telemedicine was well received in the UAE.Many participants indicated the intention to have telemedicine consultations with a doctor with whom they already had a rapport. This should encourage health systems to prioritize continuity when using telemedicine. The findings and analysis have been conducted through IBM SPSS Statistics and Dedoose software. The research outcomes were shared with healthcare organizations, enabling them to determine the telemedicine future, demand, and challenges of telemedicine in the UAE.
Improving interoperability by business process analysis

Interoperability assessment is most often based on the definition of maturity models of involved systems taken individually or together. Research on interoperability has led to the development of technical solutions based on APIs that now allow applications to communicate with each other. However, the technical solutions only integrate the actors whose activities have been implemented at the software level. Not all activities of the business process are considered, so one can assume that the technical solutions partially solve the interoperability problem. The purpose of this study is to propose a methodology for assessing interoperability based on business activity.
Fuzzy Associational Rules and reasoning logic in computer vision models

Many researchers aim towards solving human tasks with machine learning algorithms. In this paper we equipped YOLOV-5 with fuzzy logic rules that can be used to solve reasoning problems in classification (such as bottom-up classification) and linguistic feedback. Our method uses fuzzy associational rules that allow YOLOV-5 to deduce knowledge from a partial view of an object and can also be used to evaluate loss from fuzzy evaluations.
Adaptive Auto-Cinematography in Open Worlds

The increasing demand among players for greater freedom within virtual environments has resulted in a need for automated cinematography in open-world video games. Contemporary wearable devices aid in addressing these requirements by interpreting players' movements and translating them into ingame interactive actions. This increased freedom introduces significant complexities to automatic cinematography computation. In this paper, we introduce a novel Generative Adversarial Network (GAN) based model, AACOGAN, to tackle this challenge effectively. AACOGAN model establishes a relationship between player interactions, object locations, and camera movements, subsequently generating camera shots that augment player immersion. Experimental results demonstrate that AACOGAN can enhance the correlation between player interactions and camera trajectories by an average of 73%, improve multi-focus scene quality up to 32.9%. Consequently, AACOGAN is established as an efficient and economical solution for generating camera shots appropriate for a wide range of interactive motions in open-world settings. An exemplary video footage can be found at https://youtu.be/Syrwbnpzgx8.
Toward a More Robust Fine-Grained Fashion Retrieval

This paper aims to improve the robustness of fine-grained fashion retrieval (FGFR). The existing FGFR methods focus on designing complex attention modules for enhancing retrieval accuracy. They pay little attention to the characteristics of the real-world fashion data, which always contain item images and try-on images, and all images are taken from different viewpoints. This reduces the accuracy of existing FGFR models when applied to real-world applications. To solve this problem, we propose a novel contrastive learning method. Specifically, we first apply several geometric distortions (GD) to the input anchor image. Then, the distorted images are passed into existing FGFR models to learn an attribute-aware embedding. Finally, a contrastive loss is applied to ensure the distorted image is embedded close to the anchor image. The proposed method can be easily applied to existing FGFR models. Experiments on the FashionAI dataset verified its effectiveness in improving existing FGFR models by an average of approximately 5 percentage points over almost all the attributes. The proposed method also outperforms simply applying data augmentations to the input image by a significant margin. The source code is available in https://github.com/Dr-LingXiao/GD
DB-Drift: Concept drift aware density-based anomaly detection for maritime trajectories

Anomalies in maritime surveillance operations are often high-risk, and need to be detected quickly from real-world, incoming data sources. Hence it is critical that we develop unsupervised anomaly detection algorithms that both operate on a data stream and adapt to it. Real-world maritime data streams involve multiple, intersecting forms of concept drift, meaning that the underlying data distribution changes over time. We introduce DB-Drift, a novel algorithm for adapting existing density-based unsupervised anomaly detection pipelines to handle gradual and seasonal drift simultaneously.
Performance Evaluation of Reinforcement Learning and Graph Search-based Algorithm for Mobile Robot Path Planning

Mobile robot path planning is a vital problem in robotics that involves determining a collision-free path for a robot from a starting point to a goal point in environments populated with obstacles. In this paper, a comparative analysis of two path planning approaches for mobile robots is presented. Q-learning is used as a reinforcement learning (RL) approach, and A* as a graph search method. The performance of each technique is evaluated in Gazebo environment on a simulated Ackermann drive mobile robot that navigates through an environment containing obstacles. The generated paths are compared based on the path length, computational time, travel time, the robustness of the planning technique to changes in the environment, and path smoothness. The results show that Q-learning outperforms A* in terms of computation time. The computed paths by the Q-learning based approach are more smooth with fewer number of turns. Moreover, A* is not robust to environmental complexity because the computational time, and length of the computed path increases as the complexity of the environment increases. Hence, the acquired findings suggest that Q-learning can be a promising approach for mobile robot path planning, particularly in scenarios where path smoothness, robustness, and computational time are critical factors.
Transformer Based Image-Text Consistency Analysis for Infographic Articles

We present a multi-modal T5 Transformer-based method for image-text semantic consistency analysis that are targeted at infographic articles. Infographics provide graphical presentations of information and have wide applications in visualization and news media. Many media forensics methods are developed for consistency analysis among texts and images, however methods that work specifically for infographic semantic consistency are still scarce. We integrate the Natural Language Inference (NLI) of the T5 Transformer with scene text extraction for infographic consistency analysis. Experimental study is performed on the DARPA Semantic Forensics (SemaFor) Evaluation dataset for the task of news article inconsistency detection. The test set contains 248 infographic articles that are evenly split between manipulated and pristine samples. For evaluation, low False Positive Rate (FPR) is desired in media forensics. We use the metrics of True Positive Rate (TPR) at low 0.08 FPR, as well as the standard ROC AUC and Equal Error Rate (EER). Results show that our method outperforms mainstream methods from other SemaFor performer teams.
A Secure Lightweight Authentication Mechanism for IoT Devices in Generic Domain

The Internet of Things prompt deployment enhances the security concerns of these systems in recent years. The enormous exchange of sensory information between devices raises the necessity for a secure authentication scheme for Internet of Things devices. Despite many proposed schemes, providing authenticated and secure communication for Internet of Things devices is still an open issue. This research addresses challenges pertaining to the Internet of Things authentication, verification, and communication, and proposes a new secure lightweight mechanism for Internet of Things devices in the generic domain. The proposed authentication method utilizes environmental variables obtained by sensors to allow the system to identify genuine devices and reject anomalous connections.
Weight Perception and Controlling Experience in Loaded Virtual Rehabilitation: Effect of Biased Weight Feedback and Modulated Movement

Virtual reality (VR) exercise-based programs, such as virtual rehabilitation, may require users to perform movements while wearing a rehabilitation device or exoskeleton for effective training outcomes. However, these external weights posed on users are burdensome and may result in fatigue, generating undesired user experiences. We investigated the effect of biased weight feedback with movement augmentation on the weight perception, the feeling of control, and perceived exertion of a VR exercise task for the lower limb. The experiment involved twelve participants and showed that it is possible to deceive the real weight of a weight attached to a person's foot by manipulating the appearance and the movement performed. The reduced weight feedback with movement augmentation to be controlled produced the lightest weight perception of the whole task. The feeling of control can be lower in augmented movement conditions but can be slightly higher when enhanced weight feedback is used. The study result shows promise to be applied in virtual exergames such as virtual rehabilitation that is integrated with physical rehabilitation devices.
DE-C3: Dynamic Energy-Aware Compression for Computing-In-Memory-Based Convolutional Neural Network Acceleration

Convolutional neural networks (CNNs) are leveraged in many applications, such as image classification and natural language processing (NLP) tasks. However, the hardware implementation of CNNs not only occupies a considerable amount of memory space but also incurs high computational demands, which can lead to significant energy cost for data transfer. Therefore, an emerging computing-in-memory (CIM)- based architecture has been proposed to alleviate the energy bottleneck of CNNs. Various model compression methods have recently been studied to enhance energy efficiency of the CIM-based accelerator, including quantization and pruning. However, these works are separately discussed with the static inference scenario. In this paper, the proposed DE-C3 can dynamically and jointly design the compression strategy. Besides, it adopts trainable energy-aware thresholds for both quantization and pruning scenarios. Experimental results based on the CIFAR-10 dataset show that our DE-C3 achieves up to 3.4× the energy reduction compared to state-of-the-art works.
iStream: A Flexible Container-Based Testbed for Multimedia Streaming

Multimedia streaming is growing at a phenomenal rate, especially with the emergence of augmented/virtual reality applications and the growing demand for YouTube-like services. This stimulates research in all aspects of streaming systems, including but not limited to real-time and resource-efficient transcoding, server efficiency, network optimization, and client adaptation. Research and development of any part of the system require repetitive and tedious tasks in setting up the rest of the system for validation and evaluation of new ideas. This not only complicates the research work but also challenges the reproducibility and sharing of the research results. Existing proposed testbeds all have a specific focus and do not address all components of multimedia streaming. In this paper, we propose iStream, a flexible testbed for multimedia streaming system that integrates various components relevant to such systems. The components are plug-&-playable and customizable to compose various streaming systems and setups. iStream provides multimedia system researchers and developers with a flexible playground to explore their innovative ideas. Along with the design, we present a diverse set of case studies demonstrating the wide-range use of iStream.