Reviewerly/exHarmony huggingface dataset:
Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection

Cyberbullying has emerged as a pervasive issue in the digital age, necessitating advanced techniques for effective detection and mitigation. This research explores the integration of word embeddings, emotional features, and federated learning to address the challenges of centralized data processing and user privacy concerns prevalent in previous methods. Word embeddings capture semantic relationships and contextual information, enabling a more nuanced understanding of text data, while emotional features derived from text extend the analysis to encompass the affective dimension, enhancing cyberbullying identification. Federated learning, a decentralized learning paradigm, offers a compelling solution to centralizing sensitive user data by enabling collaborative model training across distributed devices, preserving privacy while harnessing collective intelligence. In this study, we conduct an in-depth investigation into the fusion of word embeddings, emotional features, and federated learning, complemented by the utilization of BERT, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) models. These techniques are applied in the context of cyberbullying detection, using publicly available multi-platform (social media) cyberbullying datasets. Through extensive experiments and evaluations, our proposed framework demonstrates superior performance and robustness compared to traditional methods. The results illustrate the enhanced ability to identify and combat cyberbullying incidents effectively, contributing to the creation of safer online environments. Particularly, the BERT model consistently outperforms other deep learning models (CNN, DNN, LSTM) in cyberbullying detection while preserving the privacy of local datasets for each social platform through our improved federated learning setup. We have provided Differential Privacy based security analysis for the proposed method to further strengthen the privacy and robustness of the system. By leveraging word embeddings, emotional features, and federated learning, this research opens new avenues in cyberbullying research, paving the way for proactive intervention and support mechanisms. The comprehensive approach presented herein highlights the substantial strengths and advantages of this integrated methodology, setting a foundation for future advancements in cyberbullying detection and mitigation.
Carbon Dependencies in Datacenter Design and Management

Building a sustainable datacenter requires coordinated decisions in its design and system management. Existing research work on datacenter sustainability often considers the design space in isolation and misses opportunities to minimize carbon footprint through coordinated design and management where sustainability is a first-class objective. Design decisions such as datacenter site selection, renewable energy investment portfolios, and the provisioning of energy storage are intertwined with complementary solutions for operation, including various forms of demand response and carbon-aware workload management. In this paper, we advocate for holistic frameworks that take into account both operational and embodied carbon to coordinate between datacenter design and system management decisions.
Effective Cloudlet Scheduling Algorithm for Load Balancing in Cloud Computing Using Fuzzy Logic

Clouds contain a huge number of virtualized resources that can be made available instantaneously. Cloud technology offers a wide array of services, encompassing platforms, hardware, and software, effectively providing almost anything as a service. A single host represents a physical computer component, while a datacenter comprises numerous hosts responsible for managing virtual machines throughout their life cycles. The efficient scheduling of virtual machine requests plays a crucial role in cloud, as it ensures that requested tasks are completed within the shortest time according to user-defined preferences. This chapter introduces an optimization model based on fuzzy logic for scheduling tasks in cloud computing. The proposed model was tested and evaluated using a fuzzy logic-based scheduling algorithm. The proposed algorithms were compared against scheduling algorithms without fuzzy logic. The experimental findings undeniably establish the superiority of the proposed algorithm.
Coordinated Multi-Robot Shared Autonomy Based on Scheduling and Demonstrations

Shared autonomy methods, where a human operator and a robot arm work together, have enabled robots to complete a range of complex and highly variable tasks. Existing work primarily focuses on one human sharing autonomy with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single</i> robot. By contrast, in this letter we present an approach for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-robot shared autonomy</i> that enables one operator to provide real-time corrections across two coordinated robots completing the same task in parallel. Sharing autonomy with multiple robots presents fundamental challenges. The human can only correct one robot at a time, and without coordination, the human may be left idle for long periods of time. Accordingly, we develop an approach that aligns the robot's learned motions to best utilize the human's expertise. Our key idea is to leverage <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning from Demonstration (LfD)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time warping</i> to schedule the motions of the robots based on when they may require assistance. Our method uses variability in operator demonstrations to identify the types of corrections an operator might apply during shared autonomy, leverages flexibility in how quickly the task was performed in demonstrations to aid in scheduling, and iteratively estimates the likelihood of when corrections may be needed to ensure that only one robot is completing an action requiring assistance. Through a preliminary study, we show that our method can decrease the scheduled time spent sanding by iteratively estimating the times when each robot could need assistance and generating an optimized schedule that allows the operator to provide corrections to each robot during these times.
Privacy Preservation and Cloud Computing

As the organization moves to cloud computing to take advantage of lower cost and higher efficiencies and availability, privacy and confidentiality concerns remain a challenge. Modern techniques to manage such challenges with careful evaluation are required to implement effective strategies for privacy protection in cloud computing. In this chapter, various needs for privacy protection of sensitive data are discussed. In turn, the fundamental principles of security protection measures in cloud computing are explored. Later in the chapter, various strategies for ensuring privacy in the cloud are compared and discussed. These methods are examined in the context of allowing authorized access. preventing unauthorized access and ensuring data integrity. Furthermore, the pitfalls and weaknesses of each technique and available workarounds to avoid such vulnerability have been discussed in detail. This chapter also covers a detailed comparative analysis of these privacy preservation and secure data storage techniques, exploring their interplay and potential effective combinations.
Simulated Annealing Based Area Optimization of Multilayer Perceptron Hardware for IoT Edge Devices

The deployment of highly parameterized Neural Network (NN) models on resource-constrained hardware platforms such as IoT edge devices is a challenging task due to their large size, expensive computational costs, and high memory requirements. To address this, we propose a Simulated Annealing (SA) algorithm-based NN optimization approach to generate area-optimized hardware for multilayer perceptrons on IoT edge devices. Our SA loop aims to change hidden layer weights to integer values and uses a two-step process to round new weights that are proximate to integers to reduce the hardware due to operation strength reduction, making it a perfect solution for IoT devices. Throughout the optimization process, we prioritize SA moves that do not compromise the model’s efficiency, ensuring optimal performance in a resource-constrained environment. We validate our proposed methodology on five MLP benchmarks implemented on FPGA, and we observe that the best-case savings are obtained when the amount of perturbation (p) is 10% and the number of perturbations at each temperature (N) is 10,000, keeping constant temperature reduction function ( $$\alpha $$ ) at 0.95. For the best-case solution, the average savings in Lookup Tables (LUTs) and filpflops (FFs) are 24.83% and 25.76%, respectively, with an average model accuracy degradation of 1.64%. Our proposed SA-based NN optimization method can significantly improve the deployment of area-efficient NN models on resource-constrained IoT edge devices without compromising model accuracy, making it a promising approach for various IoT applications.
A no-reference underwater image quality evaluator via quality-aware features

In this paper, we propose a novel no-reference evaluator based on quality-aware features, called QA-UIQE, for underwater image quality assessment. QA-UIQE extracts and fuses a set of quality-aware features including naturalness, color, contrast, sharpness, and structure. Technically, we first present a new color-cast weighted colorfulness measurement as well as color consistency measurement to characterize color, and design a saliency-weighted contrast measurement to improve the distinguishing ability of measuring contrast. Also, the locally mean subtracted and contrast normalized, maximum local variation, and local entropy are incorporated to measure naturalness, sharpness and structure, respectively. Afterward, we integrate the feature vectors extracted from the training set into Gaussian process regression to predict the image quality. Moreover, we collect a real-world underwater image dataset for testing the generalization ability of our method. The experimental results illustrate that our QA-UIQE has a superior prediction accuracy and is highly consistent with human visual perception.
Experiencing Virtual Reality for Perspective-Taking of Blind and Visually Impaired Learners

Understanding blindness or imagining being blind can be challenging for individuals without visual impairments. One of the affordances of immersive virtual reality (VR) is its ability to build empathy and facilitate perspective-taking. VR can be potent when providing narrative, experience-laden education that exposes users to ideas and settings they may not have otherwise had the chance to experience. This paper reports on a pilot study based on a series of workshops conducted at a university intended to give participants the opportunity to experience a VR non-fiction story, documenting the experience of a man who became blind after years of deteriorating vision. The workshops intended to trigger participants’ interest in VR technology and brainstorm ways that VR can address equity and inclusion of individuals with visual disabilities in education and the workforce. Findings from survey responses and group discussions of 34 workshop attendees revealed that participants' general empathy levels slightly increased after the VR experience. Participants’ perceptions of blind people’s productivity significantly increased following the VR experience, suggesting that immersive technology can be integrated in various other educational and training settings to enhance empathy and understanding of the experiences of diverse populations, including populations that are underrepresented in higher education and the workforce. This pilot study is a stepping stone for a larger project aiming to confirm the preliminary findings and to derive more exhaustive recommendations for educators and educational policy makers to address inclusion of people with visual disabilities in education and the workforce.
Anomaly Detection in Time Series Satellite Data Using a Deep Learning Method

Minimizing satellite failures and maintaining orbital health is crucial. Current systems spot certain anomalies, relying on expert insight. This paper suggests a data-driven anomaly detection framework, using Deviation Divide Mean over Neighbors (DDMN) to counter fake anomalies from data errors. Long Short-Term Memory (LSTM) models multifaceted time-series data, with a Gaussian model for anomalies. We applied our approach to a two year’s telemetry data from on in-orbit satellites and demonstrate its superiority. This approach is operational in ground stations for real-time monitoring.
ACF-TransUNet: Attention-based Coarse-Fine Transformer U-Net for Automatic Liver Tumor Segmentation in CT Images

Diagnosis of liver tumor via Computed Tomography (CT) imaging is crucial. However, manual delineation is time-consuming and presents inherent subjectivity. While deep learning models like U-Net have significantly enhanced image segmentation, they sometimes falter in capturing the nuanced variations in liver tumor morphology. Similarly, models combining Convolutional Neural Networks (CNNs) and the Transformer architecture have shown promise but still face challenges in balancing global and local feature extraction effectively. To address this problem, we propose a novel Attention-based Coarse-Fine TransUNet (ACF-TransUNet) architecture explicitly designed for liver tumor segmentation. ACF-TransUNet synergizes the strengths of CNNs and Transformers, emphasizing globally relevant spatial features while retaining detailed local information. Our architecture incorporates a channel attention module to weigh the importance of each feature channel, ensuring adaptive feature recalibration. Integrating a novel Coarse-Fine (CF) module enables the model to simultaneously produce coarse-grained and fine-grained feature maps, whose weighted fusion ensures a comprehensive and precise representation. Employing a two-stage strategy, we initially localize the liver region using the U-Net, and then segment the tumors using ACF-TransUNet. Our method achieves competitive performance on the LiTS dataset with a Dice score of 0.7752, demonstrating its potential to be a valuable tool in clinical liver tumor diagnosis.
Fusion of Attention Mechanism with Shape-Adaptive Rotational Object Detection

This study introduces a novel attention mechanism for object detection in optical remote sensing images. In aerial imagery, many small objects rely on environmental cues for recognition. Their successful recognition largely depends on their surrounding environmental backdrop since the environmental information can provide crucial clues about the object's shape, orientation, and other features. Therefore, we firmly believe in the enormous potential of the Swin Transformer in capturing these prior knowledge. Our strategy, based on RepPoints and using Swin Transformer as a backbone, engages in sample selection through the fusion of Shape-Adaptive Selection and Shape-Adaptive Measurement strategies. This strategy fully exploits the shape information of the targets, dynamically optimizing the process of positive and negative sample matching. We have also designed a boundary center loss function to enhance localization precision and incorporated an orientation classification prediction head to explicitly predict target angles. Experiments on a private SAR dataset and DOTA benchmark validate the superiority of our approach in improving accuracy, efficiency and reducing model size compared to other methods. This research paves the way for new directions in Transformer-based rotational object detection.
An improved genetic programming algorithm based on bloat control

Aiming at the disadvantages of long training time and high model complexity caused by individual bloat in genetic programming, an improved genetic programming algorithm based on bloat control is proposed. First, species are divided according to the differences between individuals, and individuals are evaluated by adjusted penalty fitness. Then, during the population evolution stage, individuals are selected by density and adjusted penalty fitness, and the improved search strategy is used to search and optimize the selected individuals. The survival mechanism of individuals retains the excellent evolutionary information in the population. Finally, the 7 benchmark functions are simulated and compared with other relevant bloat control algorithms. The experimental results show that the proposed algorithm compared with the 4 comparative algorithms can effectively control individual bloat on the basis of ensuring the optimization ability.
PELSI: Power-Efficient Layer-Switched Inference

Convolutional Neural Networks (CNNs) are now quintessential kernels within embedded computer vision applications deployed in edge devices. Heterogeneous Multi-Processor System-on-Chips (HMPSoCs) with Dynamic Voltage and Frequency Scaling (DVFS) capable components (CPUs and GPUs) allow for low-latency, low-power CNN inference on resource-constrained edge devices when employed efficiently. CNNs comprise several heterogeneous layer types that execute with different degrees of power efficiency on different HMPSoC components at different frequencies. We propose the first framework, PELSI, that exploits this layer-wise power efficiency heterogeneity for power-efficient CPU-GPU layer-switched CNN interference on HMPSoCs. PELSI executes each layer of a CNN on an HMPSoC component (CPU or GPU) clocked at just the right frequency for every layer such that the CNN meets its inference latency target with minimal power consumption while still accounting for the power-performance overhead of multiple switching between CPU and GPU mid-inference. PELSI incorporates a Genetic Algorithm (GA) to identify the near-optimal CPU-GPU layer-switched CNN inference configuration from within the large exponential design space that meets the given latency requirement most power efficiently. We evaluate PELSI on Rock-Pi embedded platform. The platform contains an RK3399Pro HMPSoC with DVFS-capable CPU clusters and GPU. Empirical evaluations with five different CNNs show a 44.48% improvement in power efficiency for CNN inference under PELSI over the state-of-the-art.
Error per single-qubit gate below 10−4 in a superconducting qubit

Abstract Implementing arbitrary single-qubit gates with near perfect fidelity is among the most fundamental requirements in gate-based quantum information processing. In this work, we fabricate a transmon qubit with long coherence times and demonstrate single-qubit gates with the average gate error below 10 −4 , i.e. (7.42 ± 0.04) × 10 −5 by randomized benchmarking (RB). To understand the error sources, we experimentally obtain an error budget, consisting of the decoherence errors lower bounded by (4.62 ± 0.04) × 10 −5 and the leakage rate per gate of (1.16 ± 0.04) × 10 −5 . Moreover, we reconstruct the process matrices for the single-qubit gates by the gate set tomography (GST), with which we simulate RB sequences and obtain single-qubit fidelities consistent with experimental results. We also observe non-Markovian behavior in the experiment of long-sequence GST, which may provide guidance for further calibration. The demonstration extends the upper limit that the average fidelity of single-qubit gates can reach in a transmon-qubit system, and thus can be an essential step towards practical and reliable quantum computation in the near future.
Wind Turbine Blade Damage Detection Based on the Improved YOLOv5 Algorithm

Regular inspection and maintenance of wind turbine blades can effectively avoid possible structural failures of wind turbines. A large number of high-resolution images of wind turbines can be obtained through drone inspection shots. This experiment performs data pre-processing and manual annotation of wind turbine blade damage for these images, and is based on YOLOv5 for the object detection of the blade damage. The experimental results show that the model can eventually predict the location and class of blade damage with almost human-level accuracy. It is further shown that for the smaller training set like this experiment, image enhancement before training can better improve the prediction accuracy.
Cyber Security and its cognitive ramifications on e-governance

With the advent of 6G technologies and augmentation of cyber crimes around the world, there persists an obligation for the uncompromising cyber security and laws to safeguard the confidential information from the mischiefs who threaten to knock down the nations with their alarming acts. E-governance is one such area which needs the utmost surveillance. The rate of augmentation of crimes has increased manifolds and the persistence of adept personnel to safeguard the organization is the need of the hour as e-governance is the protuberance of the endeavour of the governments to bind themselves together with their citizens and strengthen their bonds. Security compliances, policies, regulatory frameworks, laws must well be in place for the effective implementation and working of the government machinery to match up with the technological advancement. The conceptualization of the psychology behind such attacks needs to be understood very well. The paper focuses on the e-governance and the psychology of the cyber criminals to enact such acts of violence.
Optimization-Inspired Learning With Architecture Augmentations and Control Mechanisms for Low-Level Vision

In recent years, there has been a growing interest in combining learnable modules with numerical optimization to solve low-level vision tasks. However, most existing approaches focus on designing specialized schemes to generate image/feature propagation. There is a lack of unified consideration to construct propagative modules, provide theoretical analysis tools, and design effective learning mechanisms. To mitigate the above issues, this paper proposes a unified optimization-inspired learning framework to aggregate Generative, Discriminative, and Corrective (GDC for short) principles with strong generalization for diverse optimization models. Specifically, by introducing a general energy minimization model and formulating its descent direction from different viewpoints (i.e., in a generative manner, based on the discriminative metric and with optimality-based correction), we construct three propagative modules to effectively solve the optimization models with flexible combinations. We design two control mechanisms that provide the non-trivial theoretical guarantees for both fully- and partially-defined optimization formulations. Under the support of theoretical guarantees, we can introduce diverse architecture augmentation strategies such as normalization and search to ensure stable propagation with convergence and seamlessly integrate the suitable modules into the propagation respectively. Extensive experiments across varied low-level vision tasks validate the efficacy and adaptability of GDC.