Reviewerly/exHarmony huggingface dataset:
Towards Federated Ontology-Driven Data Integration in Continuous Software Engineering

Organizations have adopted Continuous Software Engineering (CSE) practices aiming at making software development faster, iterative, integrated, continuous, and aligned with the business. In this context, they often use different applications (e.g., project management tools, source repositories, and quality assessment tools) that store valuable data to support daily activities and decision-making. However, data items often remain spread in different applications that adopt different data and behavioral models, posing a barrier to integrated data usage. As a consequence, data-driven software development is uncommon, missing valuable opportunities for product and process improvement. In this paper, we explore an ontology network addressing CSE aspects to develop a data integration solution in which networked ontologies are the basis to build reusable and autonomous software components that work together in a system federation to provide meaningful integrated data. We achieve a comprehensive and flexible solution that can be used as a whole or partially, by extracting only the components related to the subdomains of interest.
Hyacinth macaw: a project-based learning program to develop talents in Software Engineering for Artificial Intelligence

Software Engineering for Artificial Intelligence (SE4A) uses SE principles to design and maintain AI systems, requiring analytical thinking for software complexity, while AI demands mathematical knowledge and algorithm adjustment. The IEEE Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering states that extracurricular elements impact students’ preparation. This study focuses on the first module of a project-based learning talent development program involving undergraduate students, two expert professors (in AI and SE), and mentors from sponsoring companies. An exploratory case study with 39 students from four courses was conducted, challenging them to deliver an MVP in machine learning within 1.5 months. Results showed high agreement (87.5%) in applying learned skills to future projects, recognizing SE’s benefits (96.9%) in AI, and acknowledging the connection between SE and AI (78.1%). Participants applied relevant knowledge in ML performance, data analysis, and software architecture for AI. We share strategies used by students to enhance developer experience.
UIProtoCheck: A Checklist for Semantic Inspection of User Interface Prototypes

User interface prototypes are widely used in software development to facilitate customer communication and explore ideas, especially in agile development teams. In addition, they often guide subsequent stages of the development process, such as coding, testing, and training. Given their effect on the software development cycle, UI prototypes must be included in quality assurance activities such as inspections. Existing UI inspection approaches aim to detect mainly usability problems and are designed to inspect implemented software. However, development costs could be reduced by early detection of design defects if prototypes were reviewed against software requirements before implementation. For this reason, we developed UIProtoCheck, a comprehensive checklist to inspect UI prototypes semantically according to the software requirements. To evaluate it, we conducted a study where 12 participants used our checklist to inspect three UI prototypes based on a given scenario. The results showed that teams with the best results achieved 67% effectiveness in identifying semantic errors previously included in the prototypes. These promising initial results indicate that UIProtoCheck can support the semantic inspection of UI prototypes.
Empowering Technical Skills and Soft Skills in Software Engineering Students through Problem-Based Learning

CONTEXT: The software industry has been increasing its employees’ demands regarding technical, behavioral, and social skills. Therefore, software engineering courses must exceed the limits of theory and academic examples, providing students with practical experiences to prepare them for the challenges they will encounter in real projects. OBJECTIVE: This paper aims to present the results of applying the Problem-Based Learning (PBL) methodology in the teaching of the Practice in Software Engineering (PSE) discipline integrated with the Verification, Validation, and Testing (VV&T) discipline. METHOD: During one semester, divided into five multidisciplinary teams, PSE and VV&T students developed five real web systems experiencing software engineering practices (elicitation, analysis and design, coding and testing, deployment) and entrepreneurship practices (elaboration and pitch presentation to investors). At the end of the semester, we surveyed to collect students’ opinions about their level of learning, satisfaction with project development, knowledge related to integrating complementary content, and the experience acquired with the discipline. RESULTS: The results show evidence of the effectiveness of applying the PBL methodology in the learning and experience acquired by the students of the PSE and VV&T disciplines. The results went beyond the classroom’s limits, directly impacting the students’ careers. CONCLUSION: Developing real projects with real clients, deadlines, and constraints, proved to be a fundamental element in the development of technical knowledge (technical skills) and behavioral skills (soft skills), increasing the student’s confidence level and enabling them to act in software engineering disciplines.
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning. The evolution of DRM architecture has been driven by changes in data forms. However, the development of AI-generated content (AIGC) technology, such as ChatGPT and Stable Diffusion, has given black and shadow industries powerful tools to personalize data and generate realistic images and conversations for fraudulent activities. This poses a challenge for DRM systems to control risks from the source of data generation and to respond quickly to the fast-changing risk environment. This paper aims to provide a technical analysis of the challenges and opportunities of AIGC from upstream, midstream, and downstream paths of black/shadow industries and suggest future directions for improving existing risk control systems. The paper will explore the new shadow techniques triggered by generative AI technology and provide insights for building the next-generation DRM system.
Multi-language Reverse Dictionary Model Based on Improved mBERT

A reverse dictionary generates a ranked list of vocabulary words that correspond to the definition of a given input description. Although reverse dictionary has widely practical values, little research has been done, particularly on multilingual reverse dictionary. To address this gap and enhance the accuracy of reverse dictionary across different languages, this paper proposes a multilingual reverse dictionary model based on mBERT. It optimizes the original model with features such as part-of-speech of words. The effectiveness of this improved model has been validated on both English and Chinese datasets. Experimental results illustrate that our model outperforms the baseline models in most metrics.
Online Federated Learning for Air-Ground Edge Intelligence

Traditional federated learning (FL) usually relies on static ground base stations (BSs) for model aggregation. Unmanned aerial vehicles (UAVs), due to their flexible 3D deployment, can effectively complement ground BSs, enabling the establishment of air-ground integrated FL to boost ubiquitous edge intelligence. However, for online FL under such an air-ground setting, resource-limited users need to determine the resource allocation and sample selection to enhance training performance and achieve high energy efficiency. This paper studies how to minimize the tradeoff between energy consumption and training performance of air-gound online FL, and proposes an efficient algorithm to solve the aforementioned problem. Simulation results validate the effectiveness of our proposed algorithm in terms of both training effect and energy consumption.
Augmenting Distributed AI Training with Loss-tolerant Transmission

Parameter server (PS) communication architecture in distributed machine learning (DML) systems is utilized to enhance the speed of model training in data centers (DCs) and edge nodes. However, it faces severe long-tail latency caused by many-to-one "incast" traffic patterns and suffers from non-congestion packet loss, negatively impacting training throughput. To address this challenge, we design the Loss-tolerant Transmission Protocol (LTP), which permits partial loss of gradients during synchronization to avoid unneeded retransmission and contributes to faster synchronization per iteration. Moreover, the preliminary evaluation shows that LTP outperforms other schemes on both communication latency and training accuracy.
Causal Inspired Trustworthy Machine Learning

In causality-based trustworthy machine learning, finding mechanisms from data-driven correlation analysis to causal inference and constructing a machine learning framework from correlation-driven to causality-driven are two significant challenges. To address these challenges, we propose a series of innovations, including data-driven causal inference mechanisms, causality-inspired interpretable and stable learning frameworks, causality-based generalizable graph neural network learning frameworks, and other fundamental theories and key technologies. To further support the development of the field, we make the corresponding codes and resources public in the open-source community, including the big data causal inference framework based on instrumental variables (https://github.com/causal-machine-learning-lab/mliv) and the large-scale graph neural network computing and edge-cloud collaborative learning platform (https://github.com/luoxi-model/luoxi_models).
Revealing and Analyzing the Visual Scams of Cryptocurrency Wallets

Cryptocurrencies have attracted extensive attention from malicious actors. Numerous studies have reported various cyber attacks and scams targeting this domain. This paper takes the first step to characterize the visual scams occurring within cryptocurrency wallets. Scammers exploit deceptive visual features – specifically, the omission of detailed information on the wallet’s interface such as wallet addresses, cryptocurrency tokens, and smart contract names, to confuse or mislead users. This could potentially lead users to carry out unintended transactions. By analyzing 169,680,580 transactions between December 2022 and May 2023, we identified 5,515,896 instances of fake token scams, 15,807 instances of function name scams, and 89,681,248 instances of zero transfer scams. Our analysis reveals that over 240,000 victims have been affected by these visual scam attacks, resulting in losses exceeding $43 million. These substantial figures emphasize the severity of these deceptive tactics and underline the urgent need for effective protective measures.
How to Sell Crowdsensed Data to Share-Averse Data Consumers: A Reinforcement Learning Approach

Big data trading has attracted extensive attention from both academia and industry. We propose a prior-free data trading approach to share multiple data copies among share-averse data consumers.
mmEcho: A mmWave-based Acoustic Eavesdropping Method

Acoustic eavesdropping on private or confidential spaces poses a significant privacy threat. While soundproof rooms can mitigate these risks to some extent, they are unable to counter sophisticated eavesdropping, which has gained attention in recent research. This paper presents mmEcho, an acoustic eavesdropping system that employs millimeter-wave radio signals to accurately measure micrometer-level vibrations induced by sound waves. Compared to prior approaches, our method achieves high accuracy in eavesdropping without requiring prior knowledge about the victim. We evaluate the performance of mmEcho under extensive real-world settings and scenarios. Our results show that mmEcho can accurately reconstruct audio from sources.
Chaotic moving video quality enhancement based on deep in-loop filtering

The Joint Video Experts Team (JVET) has announced the latest generation of the Versatile Video Coding(VVC, H.266) standard. The in-loop filter in VVC inherits the De-Blocking Filter (DBF) and Sample Adaptive Offset (SAO) of High Efficiency Video Coding (HEVC, H.265), and adds the Adaptive Loop Filter (ALF) to minimize the error between the original sample and the decoded sample. However, for chaotic moving video encoding with low bitrates, serious blocking artifacts still remain after in-loop filtering due to the severe quantization distortion of texture details. To tackle this problem, this paper proposes a Convolutional Neural Network(CNN) based VVC in-loop filter for chaotic moving video encoding with low bitrates. First, a blur-aware attention network is designed to perceive the blurring effect and to restore texture details. Then, a deep in-loop filtering method is proposed based on the blur-aware network to replace the VVC in-loop filter. Finally, experimental results show that the proposed method could averagely save 8.3% of bit consumption at similar subjective quality. Meanwhile, under close bit rate consumption, the proposed method could reconstruct more texture information, thereby significantly reducing the blocking artifacts and improving the visual quality.
Accelerating Incremental Graph Processing by Layering Graph

By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. For this, we propose Layph, a two-layered graph framework. The upper layer is a skeleton of the graph which is much smaller than the original graph, and the lower layer has some disjoint subgraphs. Layph limits costly global iterative computations on the original graph to the small graph skeleton and a few subgraphs updated with the input graph changes. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems in response time.
An Informative Path Planning for Multi-granularity Collaborative Search via Multiple UAVs

Unmanned aerial vehicles (UAVs) have the advantage of high mobility under harsh environments, and they can change their flight altitude to collect information, which is an informative path planning (IPP) problem. In this paper, we study the multi-granularity collaborative search issue via a swarm of UAVs in marine environment. To capture different probability of objects of interest (OOI) occupancy and the motion of OOI, we first use digital pheromone and the binary classifier of UAVs for sensing OOI based on the depth perception model of cameras. Then, we use multi-agent deep deterministic policy gradient (MADDPG) approach to learn the collaborative policy of UAVs in order to implement cooperative multi-granularity search for sea surface targets.
GeoImageCut: A Toolkit for Image Cut from Geoscience Literature

The field of geoscience has experienced a transformative shift towards data-driven scientific discovery in the era of big data science. However, the diverse and variable nature of images obtained from scientific literature presents significant challenges for researchers in terms of data collection. To address this issue, we introduce GeoImageCut, a publicly available toolkit designed to process and extract multiple objects from individual images sourced from geoscience literature. Developed as an essential component of the Deep-Time Digital Earth (DDE) program, GeoImageCut has provided invaluable support to forty geoscience research teams within the program, successfully processing over 10,000 documents. A demonstration of GeoImageCut is available through our Web User Interface (UI) at: https://deepshovel.acemap.cn
APter: Privacy Enhancement in Deep Learning Services following Principle of Least Privilege

The data abuse concern has risen along with the widespread development of Deep Learning Services (DLS). Mobile users specifically worry about data privacy being compromised. Mitigating this new concern is demanding because it requires excellent balancing between data privacy protection and highly-usable service. Unfortunately, existing works do not meet this unique requirement. In this work, we propose the data privacy protection mechanism called APter. APter is a user-side DLS-input converter, and its outputs, although still good for inference, can hardly be reconstructed and labeled for new model training inferred privacy information. At the core of APter is a lightweight converter to minimize private information according to the DLS input. Moreover, adapting APter does not have to change the existing provider backend and DLS models. We conduct comprehensive experiments with our APter prototype on mobile devices and demonstrate that APter can substantially raise the bar of data abuse difficulty with little impact on the service quality and overhead.
MoEnlight: Energy-efficient and self-adaptive Low-light Video Stream Enhancement on Mobile Devices

Camera-equipped devices and deep learning advancements have driven the development of intelligent mobile video apps. These apps require on-device processing of video streams for real-time, high-quality services while addressing privacy and robustness. However, their performance is limited by low-light conditions and small-aperture cameras in mobile platforms. Existing low-light video enhancement solutions are unsuitable due to complex models and lack of energy efficiency. We introduce MoEnlight, an energy-conscious system for enhancing low-light video on mobile devices. MoEnlight achieves real-time enhancement with competitive quality, adapting to dynamic energy budgets. Our experiments demonstrate MoEnlight’s superiority over state-of-the-art solutions for enhancing low-light videos.
Double Auction Mechanism Design in Federated Learning

In FL, participants cooperatively train a global model with their local data. The participants, however, may be heterogeneous in terms of data distribution. In such cases, FL might produce a biased global model that is not optimal for each participant. Moreover, participants may be reluctant to share their models due to privacy concerns and the incurred computation and communication costs. In this paper, we design a double auction mechanism to 1. recommend proper models for each participant’s model aggregation to ease distribution heterogeneity and improve his/her personal model performance (PMP) on the local data, 2. to make most model buyer-seller pair succeed and ensure truthfulness.
FedAdapter: Efficient Federated Learning for Mobile NLP

Fine-tuning pre-trained models for downstream tasks often requires private data, for which federated learning is the de-facto approach (i.e., FedNLP). However, FedNLP is prohibitively slow due to the large model sizes and the resultant high network/computation cost. Towards practical FedNLP, we identify as the key building blocks adapters, small bottleneck modules inserted at a variety of model layers. To automate adapter configuration, we propose FedAdapter 1, a framework that enhances the existing FedNLP with progressive training and sideline trial. Extensive experiments show that FedAdapter can reduce FedNLP’s model convergence delay to no more than several hours.
An Agile Management Model for Distributed Software Development Teams

Agile project management models are notorious in current software development. However, distributed teams pose management challenges, such as the need for effective communication and collaboration tools and the difficulty of monitoring progress and work quality. This paper presents the Agile Management Model for Distributed Software Development Teams (AgiTeD), a Scrum-inspired framework grounded on transparency, inspection, adaptation, and quality. AgiTeD defines four roles, six events within the development cycle, and seven artifacts. We applied the proposed model to a distributed team and surveyed 23 developers working remotely on eight different projects of an organization. The developers highlighted some positive facets of AgiTeD for better supporting the work of distributed software development teams, emphasizing the Agile Leader role to ensure the full implementation of the model, the proposed cycle and its events, and the Repository and Communication Rules artifact to organize the teamwork.
MApp-IDEA: Monitoring App for Issue Detection and Prioritization

Opinion mining for app reviews uses machine learning-based methods to analyze people’s comments on app stores, aiming to support software maintenance and evolution. The challenge of manually analyzing a large amount of textual data can be solved through automatic opinion mining. We present MApp-IDEA tool to detect and classify emerging issues from user feedback in a risk matrix with prioritization levels and monitor evolution over time. The tool includes automatic app review tracking and an analytical data exploration instrument that allows engineers to browse the risk matrix, time series, heat map, issue tree, alerts, and notifications. Additionally, our tool has a performance analysis module, where it was possible to verify that in 6 million processed reviews of 50 popular apps, MApp-IDEA detected approximately 240,000 issues, where the peaks of the time series of issues are related to release dates of app versions. The tool is available on Github 1 and there is a presentation about the tool in Video 2 3.
Toward a Method for Safety and Security Requirements Alignment in Critical IoT Systems

The complexity of critical IoT systems demands the joint treatment of safety and security requirements from the early system life cycle stages. Dealing with multiple (and sometimes conflicting) relationships between such requirements has been a great research need. We present a preliminary version of a method for the alignment of safety and security requirements for critical IoT systems based on the System Theoretic Process Analysis (STPA) technique. A simple use case of our method shows how those requirements are handled during the conception and elicitation phases. When fully defined, our method will help analysts handle safety and security as first-class concerns from conception to specification of critical IoT systems, including requirements dependencies and conflicts.
Nonconvex Regularization with Multi-Weighted Strategy for Real Color Image Denoising

Most existing image denoising methods commonly assume that the image is contaminated by additive white Gaussian noise (AWGN). However, real-world color image noise exhibits more complicated distribution properties, making it challenging to develop an accurate model. Consequently, denoising methods designed for AWGN often fail to achieve satisfactory performance on real-world images. In this paper, we present a novel multi-channel optimization model for real-world color images denoising within the multi-weighted Schatten <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> -norm minimization. Our proposed model utilizes the weighted Schatten <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>p</mi> </math> -norm as the regularization term, while the data fidelity term employs two weight matrices to balance the noise level across channels and regions. Besides, it helps to preserve as much detail as possible in the recovered image while removing noise. Although our proposed model is nonconvex and has no analytical solution, an accurate and efficient optimization algorithm is established based on the alternating direction method of multipliers (ADMMs) framework. Finally, we demonstrate the superior performance of our proposed method over existing state-of-the-art models on three real image datasets.