Abstract The HTTPS certificate ecosystem has long been a key topic in cybersecurity, yet the certificate landscape of Android applications remains insufficiently studied. In particular, while China has actively promoted the adoption of China’s national cryptographic algorithms in recent years, their actual deployment within the Chinese Android certificate ecosystem remains unclear. In this study, we analyzed TLS traffic from 19,980 applications in the Huawei App Market and extracted 131,933 certificate chains. While most certificates are properly configured, we identified 530 certificates with security risks, affecting 2043 applications. Notably, three SDK-related risk certificates were propagated across 1462 applications, substantially widening their security impact. Only 94 certificates using China’s national cryptographic algorithms were found, all within 89 financial applications, indicating deployment driven mainly by regulatory compliance. Furthermore, nearly 99% of leaf certificates chain back to foreign root Certificate Authorities, underscoring a strong dependency that may pose digital sovereignty risks under geopolitical uncertainty. This study highlights the existing challenges in the Chinese Android certificate ecosystem, particularly in terms of security and digital sovereignty, and offers relevant recommendations for improvement.
Dense foliage, severe illumination variations, and interference from multiple individuals with similar appearances in complex orchard environments pose significant challenges for vision-based following robots in maintaining persistent target perception and identity consistency, thereby compromising the stability and safety of fruit transportation operations. To address these challenges, we propose a novel framework, DeepDIMP-ReID, which integrates the Deep Implicit Model Prediction (DIMP) tracker with a person re-identification (ReID) module based on EfficientNet. This visual perception and autonomous following framework is designed for differential-drive orchard transportation robots, aiming to achieve robust target perception and reliable identity maintenance in unstructured orchard settings. The proposed framework adopts a hierarchical perception–verification–control architecture. Visual tracking and three-dimensional localization are jointly achieved using synchronized color and depth data acquired from a RealSense camera, where target regions are obtained via the discriminative model prediction (DIMP) method and refined through an elliptical-mask-based depth matching strategy. Front obstacle detection is performed using DBSCAN-based point cloud clustering techniques. To suppress erroneous following caused by occlusion, target switching, or target reappearance after occlusion, an enhanced HOReID person re-identification module with an EfficientNet backbone is integrated for identity verification at critical decision points. Based on the verified perception results, a state-driven motion control strategy is employed to ensure safe and continuous autonomous following. Extensive long-term experiments conducted in real orchard environments demonstrate that the proposed system achieves a correct tracking rate exceeding 94% under varying human walking speeds, with an average localization error of 0.071 m. In scenarios triggering re-identification, a target discrimination success rate of 93.3% is obtained. These results confirm the effectiveness and robustness of the proposed framework for autonomous fruit transportation in complex orchard environments.
Abstract Code summarization is an important task in software engineering that helps developers understand and maintain code by generating natural language summaries. Existing approaches predominantly rely on single models, facing a dilemma: directly deploying large language models (LLMs) incurs high training costs, while lightweight models specialized for summarization are constrained by the quality of training data and their ability to capture the complex structural semantics of code. This highlights the urgent need for synergistic collaboration between large and small models in cloud computing environments. To address these issues, this paper proposes a cloud-assisted code summarization framework. First, we achieve code enhancement by invoking cloud-deployed LLM services. The specific workflow involves using preset prompt templates to guide the model in evaluating code quality and automatically repairing defects based on its feedback, thereby constructing high-quality datasets Java-QE and Python-QE. Second, for efficient edge deployment, we introduce HiSum: AST Hierarchy-Aware Code Summarization model, a lightweight model. HiSum transforms code AST into Directed Syntax Graphs (DSG) to preserve structural semantics, encodes them via a directed graph convolutional network and decode to improve summary quality. Experimental results show that our framework significantly enhances code summarization performance. On the constructed Java-QE and Python-QE datasets, the HiSum model achieves notable improvements over state-of-the-art baselines in BLEU, METEOR, and ROUGE-L metrics (increases of 1.06%, 1.98%, 3.12% for Java-QE, and 1.46%, 3.24%, 2.20% for Python-QE, respectively). This research provides a solution that utilizes cloud LLM-assisted data enhancement to empower a lightweight hierarchical-aware model.
The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.
Mustafa Muhammed Hendawi, Nahla Elazab Elashker, Mervat Mohamed Adel Mahmoud
et al.
The division techniques play a key role in several computer‐implemented algorithms and applications; regrettably, they impose significant implementation constraints that hinder parallelization. The proposed binary search‐based division (BSBD) technique is inherited from the variable latency dividers, which have the potential to adjust the quotient bit’s retirement rate or the execution time in specific iterations, resulting in different conversion times across various dividend and divisor sets. This technique aims to accelerate the simple paper‐and‐pencil division by achieving significant latency reduction. This is accomplished by scanning more than one digit per iteration using the binary search algorithm on a sorted array due to the positional representation of a number. Binary search on an array is based on the divide‐and‐conquer concept, which breaks a problem into smaller subproblems that are addressed independently. A flowchart and a block diagram describing the sequence of the algorithm are included. The design is verified through simulation using the Vivado tool. Subsequently, it is synthesized and implemented on the contemporary field programmable gate array (FPGA) version, Virtex UltraScale VCU108, with extracting its performance metrics. Additionally, the design is synthesized on Synopsys and Cadence tools for its application‐specific integrated circuit (ASIC) implementation using UMC 45 nm technology. Furthermore, it is synthesized using Virtex‐4 and Kintex‐7 FPGAs to evaluate its performance against the state‐of‐the‐art, considering the utilization of the identical FPGA chips, referenced in the literature. In this context, the comparisons reveal a significant improvement in division speed. The results also suggest that an integrated processing unit is the optimal environment for this division approach. The suggested hardware implementation technique for the division operation can achieve a latency of 1.3 ns and a chip area of 2,352 μm 2 using 45 nm UMC ASIC technology, operating at a frequency range of 0.769 GHz. This makes the BSBD technique suitable for high‐speed applications. On the other hand, for the FPGA implementation, the average area reduction by the BSBD technique represents a remarkable 93.98% compared to a recent novel design in the literature. With respect to the latency, the average latency reduction achieved by the BSBD design is 140.14 ns, which represents a 58.86% decrease compared to the recent divider in the literature.
Abstract The Internet of Things (IoT) has been proposed to pose a greater risk of cyberattacks due to the large amounts of data traffic and the diverse range of devices. The main limitations of traditional centralized intrusion detection systems (IDSs) are attributed to privacy risks, high communication costs, and poor scalability. The research presents a distributed, privacy-preserving framework for intrusion detection, which combines Federated Learning (FL) with a new Deep Learning model that performs and optimizes network intrusions to collect and analyze aspects of “federated” augmentation, then improve security in Web usage. The particular method includes Recursive Feature Elimination (RFE) for the reduction in characteristics, the Federated Kalman Filter (FKF) to reduce noise, and an Adaptive Artificial Fish Swarm optimized Long Short-Term Memory (AdapAFS-LSTM) model for accurate detection of multi-type network intrusions. The model parameters are distributed based on IoT model nodes and do not share raw data. Model parameters learn from IoT nodes, which are combined based on the Federated Proximal (FedProx) algorithm and can be applied toward the development of a robust global IDS. Experimental evaluation of the distributed and privacy-preserving intrusion detection framework on the Multi-Type Network Attack Detection (M-TNAD) dataset demonstrated superior performance in achieving 99.79% accuracy, F1-score, precision, and recall, showing low resource consumption in the final execution time and performance metrics. This work demonstrates the potential of implementing a federated, optimization-driven deep learning method to effectively develop an IDS solution against IoT networks through optimization methodology and machine learning.
Serverless computing provides just-in-time infrastructure provisioning with rapid elasticity and a finely-grained pricing model. As full control of resource allocation is in the hands of the cloud provider and applications only consume resources when they actually perform work, we believe that serverless computing is uniquely positioned to maximize energy efficiency. However, the focus of current serverless platforms is to run hundreds or thousands of serverless functions from different tenants on traditional server hardware, requiring expensive software isolation mechanisms and a high degree of overprovisioning, i.e., idle servers, to anticipate load spikes. With shared caches, high clock frequencies, and many-core architectures, servers today are optimized for large, singular workloads but not to run thousands of isolated functions. We propose rethinking the serverless hardware architecture to align it with the requirements of serverless software. Specifically, we propose using hardware isolation with individual processors per function instead of software isolation resulting in a serverless hardware stack that consumes energy only when an application actually performs work. In preliminary evaluation with real hardware and a typical serverless workload we find that this could reduce energy consumption overheads by 90.63% or an average 70.8MW.
Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.
Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio
et al.
The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for generating domain models from textual descriptions. We use search-based methods to tune hyperparameters for a specific medical data model, resulting in a notable quality improvement over the baseline LLM. We then test the optimized hyperparameters across ten diverse application domains. While the solutions were not universally applicable, we demonstrate that combining hyperparameter tuning with prompt engineering can enhance results across nearly all examined domain models.
Quantum computing, which has the power to accelerate many computing applications, is currently a technology under development. As a result, the existing noisy intermediate-scale quantum (NISQ) computers suffer from different hardware noise effects, which cause errors in the output of quantum programs. These errors cause a high degree of variability in the performance (i.e., output fidelity) of quantum programs, which varies from one computer to another and from one day to another. Consequently, users are unable to get consistent results even when running the same program multiple times. Current solutions, while focusing on reducing the errors faced by quantum programs, do not address the variability challenge. To address this challenge, we propose Anchor, a first-of-its-kind technique that leverages linear programming to reduce the performance variability by 73% on average over the state-of-the-art implementation focused on error reduction.
Many barriers exist when new members join a research community, including impostor syndrome. These barriers can be especially challenging for undergraduate students who are new to research. In our work, we explore how the use of social computing tools in the form of spontaneous online social networks (SOSNs) can be used in small research communities to improve sense of belonging, peripheral awareness, and feelings of togetherness within an existing CS research community. Inspired by SOSNs such as BeReal, we integrated a Wizard-of-Oz photo sharing bot into a computing research lab to foster community building among members. Through a small sample of lab members (N = 17) over the course of 2 weeks, we observed an increase in participants' sense of togetherness based on pre- and post-study surveys. Our surveys and semi-structured interviews revealed that this approach has the potential to increase awareness of peers' personal lives, increase feelings of community, and reduce feelings of disconnectedness.
Lia Morra, Antonio Santangelo, Pietro Basci
et al.
Social networks are creating a digital world in which the cognitive, emotional, and pragmatic value of the imagery of human faces and bodies is arguably changing. However, researchers in the digital humanities are often ill-equipped to study these phenomena at scale. This work presents FRESCO (Face Representation in E-Societies through Computational Observation), a framework designed to explore the socio-cultural implications of images on social media platforms at scale. FRESCO deconstructs images into numerical and categorical variables using state-of-the-art computer vision techniques, aligning with the principles of visual semiotics. The framework analyzes images across three levels: the plastic level, encompassing fundamental visual features like lines and colors; the figurative level, representing specific entities or concepts; and the enunciation level, which focuses particularly on constructing the point of view of the spectator and observer. These levels are analyzed to discern deeper narrative layers within the imagery. Experimental validation confirms the reliability and utility of FRESCO, and we assess its consistency and precision across two public datasets. Subsequently, we introduce the FRESCO score, a metric derived from the framework's output that serves as a reliable measure of similarity in image content.
This research is based on the results of observations that the author did at SMK N 1 Bukittinggi. From the results of observations the authors know that in the subject of Basic Network Computers do not use learning media in the learning process. The learning process is carried out in the form of direct practice accompanied by an explanation from the subject teacher. This is seen as less effective in its implementation. Thus the purpose of this study is to design learning media for computer assembly using Augmented Reality technology in order to increase the effectiveness of the learning process. The research method used is the Research and Development (R&D) research method, which is a method used to produce products. The R&D model used is the 4D version, namely, define, design, develop, desseminate with the Luther Sutopo development model which consists of 6 stages, namely conceptualization (concept), design, material collection, manufacture (assembly), testing (testing), distribution (distribution). ). And the product test consists of 3 tests, namely validity test, practicality test, and effectiveness test. Based on the results, the author succeeded in designing Augmented Reality-based assembly learning media. This learning media can be used by teachers and students in Basic Computer Networking subjects. The form of this learning media is an application (apk) that is run using Android, while the validity results obtained from 3 validators are 0.86 which is declared valid, the practical results obtained from 2 examiners are 85.33 which are declared practical, and effectiveness was obtained from 10 students 0.87 which was declared effective.
Creating Computer Vision (CV) models remains a complex practice, despite their ubiquity. Access to data, the requirement for ML expertise, and model opacity are just a few points of complexity that limit the ability of end-users to build, inspect, and improve these models. Interactive ML perspectives have helped address some of these issues by considering a teacher in the loop where planning, teaching, and evaluating tasks take place. We present and evaluate two interactive visualizations in the context of Sprite, a system for creating CV classification and detection models for images originating from videos. We study how these visualizations help Sprite's users identify (evaluate) and select (plan) images where a model is struggling and can lead to improved performance, compared to a baseline condition where users used a query language. We found that users who had used the visualizations found more images across a wider set of potential types of model errors.
Tulsi Pawan Fowdur, Rosun Mohammad Nassir-Ud-Diin Ibn Nazir
Weather forecasting is an important application in meteorology and has been one of the most scientifically and technologically challenging problems around the world. As the drastic effects of climate change continue to unfold, localised short term weather prediction with high accuracy has become more important than ever. In this paper, a collaborative machine learning-based real-time weather forecasting system has been proposed whereby data from several locations are used to predict the weather for a specific location. In this work, five machine learning algorithms have been used and tests have been performed in four different locations in Mauritius to predict weather parameters such as Temperature, Wind Speed, Wind Direction, Pressure, Humidity, and Cloudiness. The weather data were collected using the OpenWeather API from a mobile as well as a desktop edge device. The data were stored as a JSON file in both the IBM Cloudant database and a local MySQL database. Analytics were performed on both a local server that captures the incoming data from the edge device and via a servlet deployed on the IBM cloud platform. Five machine learning algorithms namely Multiple Linear Regression (MLP), Multiple Polynomial Regression (MPR), K-Nearest Neighbours (KNN), Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) were tested using both collaborative and non-collaborative methods. The experiments showed that the collaborative regression schemes achieved 5% lower Mean Absolute Percentage Error (MAPE) than non-collaborative ones and the Multiple Polynomial Regression (MLR) algorithm outperformed all the other algorithms with errors ranging from 0.009% to 9% for the different weather parameters. In general, the results showed that collaborative based weather forecasting with multiple predictor locations can potentially increase the accuracy of the predictions in machine learning algorithms.
Maxim Bonnaerens, Matthias Freiberger, Marian Verhelst
et al.
In this work we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner. Our comparison uses pareto fronts based on randomly sampled networks from a design space to capture the underlying accuracy/complexity trade-offs. We show that our approach allows to match the information obtained by previous comparison paradigms, but provides more insights in the relationship between hardware cost and accuracy. We use our methodology to analyze different building blocks and evaluate their performance on a range of embedded hardware platforms. This highlights the importance of benchmarking building blocks as a preselection step in the design process of a neural network. We show that choosing the right building block can speed up inference by up to a factor of 2x on specific hardware ML accelerators.
Magnetic separation is a method for separating particles based on their magnetic properties. Application of magnetic powders (MP) from coal fly ash (CFA) for wastewater treatment has been considered to be a promising approach to improving the separation efficiency. This paper aims to review the perspectives on application of magnetic powders from coal fly ash (MP-CFA) for wastewater treatment based on three major technological functions. MP-CFA can enhance the treatment ability of phosphorate removal through magnetic flocculation-sedimentation process. Among three commonly used phosphorus removal coagulants, i.e., ferric chloride, aluminum sulfate and poly aluminum chloride (PAC), the addition of MP results in the best performance for the floc settling of PAC coagulation. The alkali modified MP can further improve the coagulation precipitation for chemical phosphorus removal. MP-CFA also can enhance the removal of heavy metals by adsorption and chemical precipitation. The MP effectively adsorbs copper ions at low concentration by removing copper ions through chemical precipitation, which can be speed up by adding the magnetic powder, especially using the alkali modified MP. MP-CFA can be delivered to activated sludge process to improve the solid-liquid separation of sludge from treated wastewater. Activated sludge supplemented with MP exhibits excellent settleability and concentration of mixed liquid volatile suspended solids (MLVSS) can be greatly increased to enhance volumetric biotreatment capacity.
Chemical engineering, Computer engineering. Computer hardware