Reviewerly/exHarmony huggingface dataset:
GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training

Distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model.The periodic synchronization at each iteration incurs considerable overhead, exacerbated by the increasing size and complexity of state-of-the-art neural networks.Although many gradient compression techniques propose to reduce communication cost, the ideal compression factor that leads to maximum speedup or minimum data exchange remains an open-ended problem since it varies with the quality of compression, model size and structure, hardware, network topology and bandwidth.We propose GraVAC, a framework to dynamically adjust compression factor throughout training by evaluating model progress and assessing gradient information loss associated with compression.GraVAC works in an online, black-box manner without any prior assumptions about a model or its hyperparameters, while achieving the same or better accuracy than dense SGD (i.e., no compression) in the same number of iterations/epochs.As opposed to using a static compression factor, GraVAC reduces end-to-end training time for ResNet101, VGG16 and LSTM by 4.32×, 1.95× and 6.67× respectively.Compared to other adaptive schemes, our framework provides 1.94× to 5.63× overall speedup.
Evolutionary Multitasking Bi-Directional Particle Swarm Optimization for High-Dimensional Feature Selection

Feature selection is an important data processing technique, aiming to reduce the redundant and irrelevant features of data. However, as the number of features increases, feature selection algorithms based on particle swarm optimization (PSO) face the challenges of low search efficiency and huge computational consumption due to the enormous search space. A recently proposed bi-directional feature fixation (BDFF) framework for PSO has shown its effectiveness in solving high-dimensional feature selection problems, but it may mislead the particles to search in the wrong direction and requires a long time to find a small feature subset. Utilizing the prior knowledge of feature selection is expected to further enhance the performance of BDFF. Therefore, this paper first designs two tasks to introduce the prior knowledge of feature selection into BDFF while retaining its global search ability. Then, the multitasking bi-directional PSO (MBDPSO) is proposed by combining BDFF and the evolutionary multitasking optimization (EMTO) technique, which can help transfer knowledge between the two tasks effectively. Experimental results on 10 public classification datasets demonstrate that the proposed MBDPSO has an excellent performance on high-dimensional feature selection problems.
Formal Models for the Energy-Aware Cloud-Edge Computing Continuum: Analysis and Challenges

Cloud infrastructures are rapidly evolving from centralised systems to geographically distributed federations of edge devices, fog nodes, and clouds. These federations (often referred to as the Cloud-Edge Continuum) are the foundation upon which most modern digital systems depend, and consume enormous amounts of energy. This consumption is becoming a critical issue as society's energy challenges grow, and is a great concern for power grids which must balance the needs of clouds against other users. The Continuum is highly dynamic, mobile, and complex; new methods to improve energy efficiency must be based on formal scientific models that identify and take into account a huge range of heterogeneous components, interactions, stochastic properties, and (potentially contradictory) service-level agreements and stakeholder objectives. Currently, few formal models of federated Cloud-Edge systems exist - and none adequately represent and integrate energy considerations (e.g. multiple providers, renewable energy sources, pricing, and the need to balance consumption over large-areas with other non-Cloud consumers, etc.). This paper conducts a systematic analysis of current approaches to modelling Cloud, Cloud-Edge, and federated Continuum systems with an emphasis on the integration of energy considerations. We identify key omissions in the literature, and propose an initial high-level architecture and approach to begin addressing these - with the ultimate goal to develop a set of integrated models that include data centres, edge devices, fog nodes, energy providers, software workloads, end users, and stakeholder requirements and objectives. We conclude by highlighting the key research challenges that must be addressed to enable meaningful energy-aware Cloud-Edge Continuum modelling and simulation.
Understanding an organizational change and development intervention applied in a Global Software Industry: A case study

CONTEXT: Organizational changes and development interventions are being used as adaptive mechanisms for answering to the market and internal demands to remain flexible, and adaptable without losing productivity and quality. However, applying appropriate transitions involving team restructuring and project reorganization is still a challenge for most software industry companies. OBJECTIVE: This study aims to carry out a case study to identify the motivations and actions that supported an episodic organizational change (EOC) in a software industry company that end up in an adoption of a new model of team structure and project organization, called Model B. In addition, we intend to identify the main impacts and lessons learned perceived by the stakeholders during the transition process and the use of Model B for 2 years. RESULTS: The 5 identified motivations for this EOC were answered by an intervention composed of 3 main actions. The efficacy of the intervention was evaluated by comparing the motivations and achieved results. In addition, we present the dynamic of the definition and implementation of Model B, as well as new improvements applied over the 2 years of its use. The lessons learned are composed of 3 factors that can boost and 8 factors that can hinder the transition from the prior model to Model B, followed by some extra suggestions to improve the company’s EOC process. CONCLUSION: Our findings show evidence that not adopting change management knowledge can make implementing EOC in the software industry more challenging and prone to inefficiency or failure. On the other hand, applying risk management considering known critical factors can help in generating a plan that is more efficient and able to take advantage of critical success factors and avoid or mitigate the disadvantages arising from critical failure factors.
Exploiting oscillatory dynamics of delay systems for reservoir computing

Nonlinear dynamical systems exhibiting inherent memory can process temporal information by exploiting their responses to input drives. Reservoir computing is a prominent approach to leverage this ability for time-series forecasting. The computational capabilities of analog computing systems often depend on both the dynamical regime of the system and the input drive. Most studies have focused on systems exhibiting a stable fixed-point solution in the absence of input. Here, we go beyond that limitation, investigating the computational capabilities of a paradigmatic delay system in three different dynamical regimes. The system we chose has an Ikeda-type nonlinearity and exhibits fixed point, bistable, and limit-cycle dynamics in the absence of input. When driving the system, new input-driven dynamics emerge from the autonomous ones featuring characteristic properties. Here, we show that it is feasible to attain consistent responses across all three regimes, which is an essential prerequisite for the successful execution of the tasks. Furthermore, we demonstrate that we can exploit all three regimes in two time-series forecasting tasks, showcasing the versatility of this paradigmatic delay system in an analog computing context. In all tasks, the lowest prediction errors were obtained in the regime that exhibits limit-cycle dynamics in the undriven reservoir. To gain further insights, we analyzed the diverse time-distributed node responses generated in the three regimes of the undriven system. An increase in the effective dimensionality of the reservoir response is shown to affect the prediction error, as also fine-tuning of the distribution of nonlinear responses. Finally, we demonstrate that a trade-off between prediction accuracy and computational speed is possible in our continuous delay systems. Our results not only provide valuable insights into the computational capabilities of complex dynamical systems but also open a new perspective on enhancing the potential of analog computing systems implemented on various hardware platforms.
LRS_PKI: A novel blockchain-based PKI framework using linkable ring signatures

In recent years, numerous security vulnerabilities have emerged within the PKI system. For example, a compromised CA can issue illegal or fake certificates for any domains, and a CA can issue unauthorized certificates without the consent of the domain owner. In addition, some high-value target domains, such as banks and government agencies may have been frequently attacked, and the adversaries can launch the targeted attacks by making use of the disclosure of the issuing CAs. To address these pressing issues or challenges, in this work, we propose a novel blockchain-based PKI framework using linkable ring signatures, called LRS_PKI. Specially, we propose a novel certificate issuance mechanism that utilizes linkable ring signatures to hide the issuing CA, so as to reduce the risk of the PKI system being attacked. Additionally, we introduce the blockchain as a public log to record the certificate operations, and adopt the decentralized storage IPFS to store the certificates to decouple the blockchain layer and storage layer. In order to prevent the CA from issuing unauthorized certificates, we have added a condition to verify whether the issuing CA is consistent with the previous issuing CA in the certificate verification. The security analysis and experimental results show that LRS_PKI is both secure and efficient.
A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques

The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image texture features, impacting clinical judgment. Thus, maintaining clear lesion boundaries while eliminating speckle noise is a challenging task. This paper presents an innovative approach for denoising ultrasound images using a novel noise reduction network model called content-aware prior and attention-driven (CAPAD). The model employs a neural network to automatically capture the hidden prior features in ultrasound images to guide denoising and embeds the denoiser into the optimization module to simultaneously optimize parameters and noise. Moreover, this model incorporates a content-aware attention module and a loss function that preserves the structural characteristics of the image. These additions enhance the network's capacity to capture and retain valuable information. Extensive qualitative evaluation and quantitative analysis performed on a comprehensive dataset provide compelling evidence of the model's superior denoising capabilities. It excels in noise suppression while successfully preserving the underlying structures within the ultrasound images. Compared to other denoising algorithms, it demonstrates an improvement of approximately 5.88% in PSNR and approximately 3.61% in SSIM. Furthermore, using CAPAD as a preprocessing step for breast tumor segmentation in ultrasound images can greatly improve the accuracy of image segmentation. The experimental results indicate that the utilization of CAPAD leads to a notable enhancement of 10.43% in the AUPRC for breast cancer tumor segmentation.
Multi-language transfer learning for low-resource legal case summarization

Abstract Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- and cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries.
RMT: A Semi-Automated Tool for Refactoring Design Patterns

When done manually, software refactoring becomes a thorough job and can introduce chances of human failure during its application. Automated or semi-automated tools for this purpose can facilitate the refactoring process. This paper presents the semi-automated Refactoring and Measurement Tool (RMT) that performs pattern-based refactoring in source code written in Java. RMT detects and applies design patterns using literature methods and evaluates the benefits of pattern application regarding quality attributes using software metrics. Five tests were performed with Java projects, introducing candidate classes for refactoring the Strategy and Template Method patterns. Link for video: youtu.be/tDWTmFJCfg8.
Improved YOLOv5s for Small Ship Detection With Optical Remote Sensing Images

Ship detection is of great importance in territorial security and marine environmental protection. However, the accurate detection of small ships is a challenging task in complex environments mainly due to small ships having few features on remote sensing images. In this letter, we propose a small ship detection model based on YOLOv5s. The major features of the proposed model include: (1) a detection layer is added with a shallow feature map in the Head and skip connections are employed in the Neck to improve the detection accuracy of small ships; (2) a novel and effective Hybrid Spatial Pyramid Pooling (HSPP) is proposed to fuse the local and global information of feature maps; (3) a Coordinate Attention mechanism is employed in the Backbone to augment the representations of small ships, and EIOU is used as the loss function for bounding box regression to enhance the localization accuracy of the proposed model; (4) K-means++ algorithm is used to obtain more reasonable anchors for small ship detection. We introduce the multi-scale ship dataset OSSD, which contains 10133 images. Experiments on LEVIR-Ship and OSSD validate the effectiveness of our proposed model. The code and OSSD will be available at https://github.com/wenjieo/Improved-YOLOv5s-for-small-ship-detection.
Fine-Grained Emotions Influence on Implicit Hate Speech Detection

Recent years brought an exponential growth of social media which revolutionized freedom of speech but significantly increased the propagation of hate speech and hate-based activities. Therefore, constructive countermeasures are necessary to prevent escalating hateful content on online social media. Many recent works target explicit hate speech, but only a few studies have utilized multiple fused features such as sentiment, targets, and emotions as attributes to enhance the detection of hate speech. In general, sentiment features help to discern feelings such as positivity or negativity, and emotion features provide a deeper level of granularity, focusing on a more comprehensive understanding of sensitivities. The aim of this paper is to investigate the significance of incorporating fine-grained emotions as an essential feature in improving the classification of implicit hate speech. First, we analyzed emotion variations of hateful and non-hateful content and explored their major fine-grained emotion discrepancies targeting implicit hateful content. Next, we introduce a multi-task learning approach that integrates emotions and sentiment features to classify implicit expressions of hatred. To evaluate the effectiveness of our multi-task learning approach, we compared it with baseline models using single-task learning approaches. The experimental results show that our multi-task approach outperformed in classifying implicit hate speech compared to the baseline models and demonstrates that fine-grained emotional knowledge decreases the classification error across multiple implicit hate categories.
Imperfect CSI: A Key Factor of Uncertainty to Over-the-Air Federated Learning

Over-the-air computation (AirComp) has recently been identified as a prominent technique to enhance communication efficiency of wireless federated learning (FL). This letter investigates the impact of channel state information (CSI) uncertainty at the transmitter on an AirComp enabled FL (AirFL) system with the truncated channel inversion strategy. To characterize the performance of the AirFL system, the weight divergence with respect to the ideal aggregation is analytically derived to evaluate learning performance loss. We explicitly reveal that the weight divergence deteriorates as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(1/\rho ^{2})$ </tex-math></inline-formula> as the level of channel estimation accuracy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> vanishes, and also has a decay rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(1/K^{2})$ </tex-math></inline-formula> with the increasing number of participating devices, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> . Building upon our analytical results, we formulate the channel truncation threshold optimization problem to adapt to different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> , which can be solved optimally. Numerical results verify the analytical results and show that a lower truncation threshold is preferred with more accurate CSI.
MTD-DHJS: Makespan-Optimized Task Scheduling Algorithm for Cloud Computing With Dynamic Computational Time Prediction

Cloud computing has revolutionized the management and analysis of data for organizations, offering scalability, flexibility, and cost-effectiveness. Effective task scheduling in cloud systems is crucial to optimize resource utilization and ensure timely job completion. This research presents a novel method for job scheduling in cloud computing, employing the Johnson Sequencing algorithm across three servers. Originally developed for scheduling tasks in a manufacturing context, the Johnson Sequencing method has proven successful in resolving task scheduling challenges. Here, we adapt this method to address job scheduling among three servers within a cloud computing environment. The primary objective of the algorithm is to minimize the makespan, representing the total time required to complete all tasks. This study considers a scenario where a diverse set of jobs, each with varying processing durations, needs to be distributed across three servers using the Johnson Sequencing method. The algorithm strategically determines the optimal order for task execution on each server while accounting for job interdependencies and processing times on the individual servers. To put the Johnson Sequencing algorithm into practice for cloud computing job scheduling, we propose a three-step approach. First, we construct a precedence graph by analyzing the relationships among jobs. Subsequently, the precedence graph is transformed into a two-machine Johnson Sequencing problem by allocating jobs to servers. Finally, we employ the Dynamic Heuristic Johnson Sequencing method to determine the best order of jobs on each server, effectively minimizing the makespan. Through comprehensive simulations and testing, we compare the performance of our suggested Dynamic Heuristic Johnson Sequencing technique with existing scheduling algorithms. The results demonstrate significant improvements in terms of makespan reduction and resource utilization when employing our proposed method with three servers. Furthermore, our approach exhibits remarkable scalability and effectiveness in resolving complex job scheduling challenges within cloud computing settings. The outcomes of this research contribute to the optimization of resource allocation and task management in cloud systems, offering potential benefits to a wide range of industries and applications.
FPGA Implementation of Modified SNOW 3G Stream Ciphers Using Fast and Resource Efficient Substitution Box

Security plays a vital role in electronic communication, particularly in wireless networks like long term evolution (LTE), where safeguarding data and resources from malicious activities is crucial. Cryptographic algorithms are at the core of security mechanisms, ensuring the protection of sensitive information. While software implementations of these algorithms are relatively straightforward, they often need more speed in real-time applications for communication devices like mobile phones. Consequently, implementation of these cryptographic algorithms as hardware crypto processors becomes necessary. This letter presents a novel implementation of the SNOW3G crypto processor architecture for the 4G LTE security applications, focusing on area, power, and efficiency. The proposed modified SNOW3G architecture utilizes only 0.31% of the available area when implemented on an FPGA Zynq ZC702 and achieves 28.34 efficiency which quantifies as the ratio of throughput to the area. Furthermore, it consumes a total power of 0.142 mW. These low power and area requirements make the design highly suitable for integration into mobile devices, meeting their specific constraints and enabling efficient cryptographic operations.