Reviewerly/exHarmony huggingface dataset:
Label-Aware Aggregation for Improved Federated Learning

Federated Averaging (FedAvg) is the most common aggregation method used in Federated learning, which performs a weighted averaging of the updates based on the sizes of the individual datasets of each client. A raising discussion in the research community suggests that FedAvg might not be the optimal method since, for instance, it does not fully take into account the variety of the client data distributions. In this paper, we propose a label-aware aggregation method FedLA, that addresses the biased models issue by considering the variety of labels in the weighted averaging. It combines two main properties of the client data, namely data size and label distribution. Through extensive experiments, we demonstrate that FedLA is particularly effective in several heterogeneous data distribution scenarios. Especially when only a small group of the clients is participating in the Federated Learning process. Furthermore, we argue that accurately describing the data distribution is crucial in selecting the appropriate aggregation method. In this regard, we discuss various properties that can be used to describe data distribution and illustrate how these properties can guide the choice of an aggregation method for specific data distributions.
Bridging the Bubbles: Connecting Academia and Industry in Cybersecurity Research

There is a perceived disconnect between how ad hoc industry solutions and academic research solutions in cyber security are developed and applied. Why is that? Is there a difference in philosophy in how solutions to cyber security problems are developed by industry and by academia? What could academia and industry do to bridge this gap and speed up the development and use of effective cybersecurity solutions? This position paper provides an overview of the most critical gaps and solutions identified by an interdisciplinary expert exchange on the topic. The discussion was held in the form of the webinar “Bridging the Bubbles: Connecting Academia and Industry in Cybersecurity Research” in November 2022 as part of the Rogers Cybersecure Catalyst webinar series. Panelists included researchers from academia and industry as well as experts from industry and business development. The key findings and recommendations of this exchange are supported by the relevant scientific literature on the topic within this paper. Different approaches and time frames in development and lifecycle management, challenges in knowledge transfer and communication as well as heterogeneous metrics for success in projects are examples of the evaluated subject areas.
Smart Prompt Advisor: Multi-Objective Prompt Framework for Consistency and Best Practices

Recent breakthroughs in Large Language Models (LLM), comprised of billions of parameters, have achieved the ability to unveil exceptional insight into a wide range of Natural Language Processing (NLP) tasks. The onus of the performance of these models lies in the sophistication and completeness of the input prompt. Minimizing the enhancement cycles of prompt with improvised keywords becomes critically important as it directly affects the time to market and cost of the developing solution. However, this process inevitably has a trade-off between the learning curve/proficiency of the user and completeness of the prompt, as generating such a solutions is an incremental process. In this paper, we have designed a novel solution and implemented it in the form of a plugin for Visual Studio Code IDE, which can optimize this trade-off, by learning the underlying prompt intent to enhance with keywords. This will tend to align with developers' collection of semantics while developing a secure code, ensuring parameter and local variable names, return expressions, simple pre and post-conditions. and basic control and data flow are met.
Survey of the Importance of Implementing Blockchain Technology to Support Supply-Chain Management for Identifying and Tracking Counterfeit Products

The physical and online market is filled with products that are claimed to be genuine. The organizations that own the rights to these products paid huge amounts of money for these products to be delivered. Because of their cheap prices and their look very similar to the original products, counterfeits are invading the market. Unfortunately, some sellers are exploiting this by marketing their fake items claiming that they are genuine, unaware or sometimes not caring about the consequences. Therefore, this paper will study the importance of implementing a public supply chain ledger that is easy to access for everyone to investigate if the product in front of them is as genuine as it is claimed to be. For that, Blockchain technology is proposed here since it is built to be available to all authorized parties, easy to access and immutable.
Grading on a Curve: How Rust can Facilitate New Contributors while Decreasing Vulnerabilities

New contributors are critical to open source projects. Without them, the project will eventually atrophy and become inactive, or its experienced contributors will bias the future directions the project takes. However, new contributors can also bring a greater risk of introducing vulnerable code. For projects that have a need for both secure implementations and a strong, diverse contributor community, this conflict is a pressing issue. One avenue being pursued that could facilitate this goal is rewriting components of C or C++ code in Rust– a language designed to apply to the same domains as C and C++, but with greater safety guarantees. Seeking to answer whether Rust can help keep new contributors from introducing vulnerabilities, and therefore ease the burden on maintainers, we examine the Oxidation project from Mozilla, which has replaced components of the Firefox web browser with equivalents written in Rust. We use the available data from these projects to derive parameters for a novel application of learning curves, which we use to estimate the proportion of commits that introduce vulnerabilities from new contributors in a manner that is directly comparable. We find that despite concerns about ease of use, first-time contributors to Rust projects are about 70 times less likely to introduce vulnerabilities than first-time contributors to C++ projects. We also found that the rate of new contributors increased overall after switching to Rust, implying that this decrease in vulnerabilities from new contributors does not result from a smaller pool of more skilled developers, and that Rust can in fact facilitate new contributors. In the process, we also qualitatively analyze the Rust vulnerabilities in these projects, and measure the efficacy of the common SZZ algorithm for identifying bug-inducing commits from their fixes.
Hand Recognition Solution as Conference Room Intelligent Lighting Control Technique

In communication technologies, it can be solved in systems that are extremely simple, detailed, among the tools for use in the electronic environment, internet-connected, development and integrated systems. With the rapid development of technology, smart led lamps, smart office, smart conference room lighting, road lighting fixtures and therefore smart lighting systems have been the reason why smart lighting systems can enter daily life rapidly. Sensors which are the components of smart lighting systems, feedback from users, cloud services, smart algorithms become more widespread with artificial intelligence in an easier, faster, and more economical way, causing a change in traditional lighting control and improvements in energy saving, functionality, and user centered lighting. In this study, the mediapipe framework is intended to be used for office conference room. MediaPipe is indeed an open-source framework that enables the processing of computer vision data, including video and audio. The lighting system was built using the artificial intelligence supported mediapipe hand recognition solution, and artificial intelligence and image processing algorithms were used. Each hand gesture corresponds to a different command to control the light. There are 3 main menus are determined for the hand and defined to the system. Main menus can be reached with every hand movement. After entering the main menu, intermediate menus are processed. Lighting application can be realized through control commands obtained by hand movements, digital/analog information conversion and Dali control system. It is concluded that the ease of control brought by this method is more advantageous in terms of its purpose than other methods because it is contactless and does not require any hardware device in the hands of the presenter.
How Many Papers Should You Review? A Research Synthesis of Systematic Literature Reviews in Software Engineering

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time.
WCSim: A Cloud Computing Simulator with Support for Bag of Tasks Workflows

In this paper, we present WCSim, Workflow Cloud Simulator. Firstly, we argue that this cloud simulation tool offers a high level of accessibility by allowing the description of various components, such as users, infrastructures, and workload, of a given scenario simply by providing parameters at launch time, without requiring the extension of the simulator code. Then, we explain how we conceived the components for the simulation models and provide a detailed description of the implemented software. Additionally, we compare the results of a small scenario obtained from two other simulation tools with those provided by WCSim. Finally, we present a case study that illustrates the usage of WCSim. The paper also introduces the a abstraction to model workflows as a Direct Acyclic Graph of Bag of Tasks.
Quantifying impacts and benefits of advanced remote monitoring solutions for the operation of mini grids in sub-Saharan Africa

As of 2022, 675 million people do not have access to electricity globally. The large majority of this underserved population lives in Sub-Saharan Africa. Mini grids systems play a central role to close this energy access gap. The adoption of latest technologies such as remote monitoring solutions can contribute to strengthen mini grids business case by improving their operation, maintenance and performance. In this study we present the latest trends, opportunities and challenges of remote monitoring solutions for mini grids and we illustrate the potential of this technology in optimizing mini grids operations, making the case for wider adoption in the sector. The analysis is developed assessing primary data collected from seven mini grids developers, covering 49 photovoltaic autonomous hybrid rural mini-grids across seven sub-Saharan Africa countries. To better understand the advantages of remote monitoring solutions we present a classification based on their related benefits compared to manual/local monitoring options. Orientation toward the adoption of remote monitoring solutions by mini grids developers highlights the advantages from a market and from the operational and maintenance perspectives. Through a cost-benefit analysis of remote monitoring solutions, we show that can have a positive impact on mini grids operations, leading to an overall operational expenditures reductions of up to 17 %, with an investment payback time as short as 1 year. Finally, we provide an estimation of operational costs savings at country level by using remote monitoring solutions in future rural electrification efforts.
Unifying Defect Prediction, Categorization, and Repair by Multi-Task Deep Learning

Just-In- Time defect prediction models can identify defect-inducing commits at check-in time and many approaches are proposed with remarkable performance. However, these approaches still have a few limitations which affect their effectiveness and practical usage: (1) partially using semantic information or structure information of code, (2) coarsely providing results to a commit (buggy or clean), and (3) independently investigating the defect prediction model and defect repair model. In this study, to handle the aforementioned limitations, we propose a unified defect prediction and repair framework named COMPDEFECT,which can identify whether a changed function inside a commit is defect-prone, categorize the type of defect, and repair such a defect automatically if it falls into several scenarios, e.g., defects with single statement fixes, or those that match a small set of defect templates. Technically, the first two tasks in COMPDEFECT are treated as a multiclass classification task, while the last task is treated as a sequence generation task. To verify the effectiveness of COMPDEFECT, we first build a large-scale function-level dataset (i.e., 21,047) named Function-SStuBs4J and then compare COMPDEFECT with tens of state-of-the-art (SOTA) approaches by considering five performance measures. The experimental results indicate that COMPDEFECT outperforms all SOTAs with a substantial improvement in three tasks separately. Moreover, the pipeline experimental results also indicate the feasibility of COMPDEFECT to unify three tasks in a model.
Trace-Based Performance Analysis for Deep Learning in Edge Container Environments

In recent years, container-based deep learning has emerged as a trending technology in edge environments. Containers offer several advantages over traditional virtual machines, including improved resource utilization and mobility. However, containers still hinder efficient use of system resources in deep learning workloads, so it is important to adopt effective resource management techniques to avoid resource conflicts. In this paper, we extract system call and event traces while executing deep learning workloads in a container and compare the results with those of running the same workloads on a host machine. By comparing system calls invoked between the two environments, we quantify the overhead of containers with respect to resource consumption and interference. We then explore the impact of running multiple containers concurrently and highlight the issues that arise in a multi-tenant environment. Our findings show that container-based deep learning can be a viable solution for deep learning workloads, but it is important to carefully consider the resource requirements and performance impact of containerization. We recommend that cloud or edge providers use a combination of resource management techniques, such as resource quotas and limits, to avoid resource wastes of containers and interference with each other.
Software Entity Recognition with Noise-Robust Learning

Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation. While many approaches have been proposed to address this problem, they suffer from small entity vocabularies or noisy training data, hindering their ability to recognize software entities mentioned in sophisticated narratives. To address this challenge, we leverage the Wikipedia taxonomy to develop a comprehensive entity lexicon with 79K unique software entities in 12 fine-grained types, as well as a large labeled dataset of over 1.7M sentences. Then, we propose self-regularization, a noise-robust learning approach, to the training of our software entity recognition (SER) model by accounting for many dropouts. Results show that models trained with self-regularization outperform both their vanilla counterparts and state-of-the-art approaches on our Wikipedia benchmark and two Stack Overflow benchmarks. We release our models <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://huggingface.co/taidng/wikiser-bert-base; https.//huggingface.co/taidng/wikiser-bert-large., data, and code for future research. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://github.com/taidnguyen/software_entity_recognition
Hyperparameters Optimization for Federated Learning System: Speech Emotion Recognition Case Study

Context: Federated Learning (FL) has emerged as a promising, massively distributed way to train a joint deep model across numerous edge devices, ensuring user data privacy by retaining it on the device. In FL, Hyperparameters (HP) significantly affect the training overhead regarding computation and transmission time, computation and transmission load, as well as model accuracy. This paper presents a novel approach where Hyperparameters Optimization (HPO) is used to optimize the performance of the FL model for Speech Emotion Recognition (SER) application. To solve this problem, both Single-Objective Optimization (SOO) and Multi-Objective Optimization (MOO) models are developed and evaluated. The optimization model includes two objectives: accuracy and total execution time. Numerical results show that optimal Hyperparameters (HP) settings allow for improving both the accuracy of the model and its computation time. The proposed method assists FL system designers in finding optimal parameters setup, allowing them to carry out model design and development efficiently depending on their goals.
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data

Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Models (TBMs) such as XGBoost have historically performed better on tabular data. It has been shown that subsampling of training data when building trees can improve performance but it is an open problem whether such subsampling can improve performance in FL. In this paper, we evaluate a histogram-based federated XGBoost that uses Minimal Variance Sampling (MVS). We demonstrate the underlying algorithm and show that our model using MVS can improve performance in terms of accuracy and regression error in a federated setting. In our evaluation, our model using MVS performs better than uniform (random) sampling and no sampling at all. It achieves both outstanding local and global performance on a new set of federated tabular datasets. Federated XGBoost using MVS also outperforms centralized XGBoost in half of the studied cases.
Federated Object Detection for Quality Inspection in Shared Production

Federated learning (FL) has emerged as a promising approach for training machine learning models on decentralized data without compromising data privacy. In this paper, we propose a FL algorithm for object detection in quality inspection tasks using YOLOv5 as the object detection algorithm and Federated Averaging (FedAvg) as the FL algorithm. We apply this approach to a manufacturing use-case where multiple factories/clients contribute data for training a global object detection model while preserving data privacy on a non-IID dataset. Our experiments demonstrate that our FL approach achieves better generalization performance on the overall clients' test dataset and generates improved bounding boxes around the objects compared to models trained using local clients' datasets. This work showcases the potential of FL for quality inspection tasks in the manufacturing industry and provides valuable insights into the performance and feasibility of utilizing YOLOv5 and FedAvg for federated object detection.
Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion

Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propagate and escalate into failures, making faster-than-real-time anomaly detection highly desirable for expediting downstream analysis and intervention. This paper proposes Maat, the first work to address anomaly anticipation of performance metrics in cloud services. Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting of metric forecasting and anomaly detection on forecasts. The metric forecasting stage employs a conditional denoising diffusion model to enable multi-step forecasting in an auto-regressive manner. The detection stage extracts anomaly-indicating features based on domain knowledge and applies isolation forest with incremental learning to detect upcoming anomalies. Thus, our method can uncover anomalies that better conform to human expertise. Evaluation on three publicly available datasets demonstrates that Maat can anticipate anomalies faster than real-time comparatively or more effectively compared with state-of-the-art real-time anomaly detectors. We also present cases highlighting Maat's success in forecasting abnormal metrics and discovering anomalies.
Trusted Federated Learning Framework for Attack Detection in Edge Industrial Internet of Things

The edge Industrial Internet of Things (IIoT) is highly vulnerable to attacks due to the vast number of connected devices and the lack of security features. Attacks in edge IIoT can lead to significant damage, including data theft, malfunctioning, and privacy breaches. Federated Learning (FL) is a promising approach to detecting attacks by utilizing edge devices' collective intelligence. FL allows devices to collaboratively learn from multiple devices' data without centralized sharing, which preserves data privacy and reduces communication costs. However, FL has vulnerabilities that can compromise model accuracy, privacy, and security. Trusted FL is essential for collaboration among multiple edge IIoT devices while preserving data privacy and security. Trust plays a critical role in the success of FL, as edge IIoT devices must trust that the models are accurately learning and that their data is protected. To address this, we propose an FL framework that uses Federated Averaging (FedAvg) and Convolutional Neural Network (CNN) to detect attacks in edge IIoT. We also propose a mechanism to calculate trust for appropriate edge IIoT device selection by measuring each device's (a.k.a client's) performance during model training. The proposed edge IIoT device selection method, client selection, can fairly select clients for model training and improve trust in the entire system. Although the proposed FL approach does not outperform the centralized ResNet-18 CNN model on experimental analysis, improving its performance can be a promising solution for detecting attacks in edge IIoT.