In rehabilitation applications, encouraging patients to actively participate in training is essential for effective recovery. However, personalized control design in robot-assisted therapy remains challenging due to variations in patients' motor capabilities. To address this issue, this paper proposes an assist-as-needed (AAN) control framework that integrates a hybrid fuzzy-transformer neural network (HFTN) with a fuzzy echo state network (FESN)-based variable impedance controller to ensure personalized support and active engagement. The HFTN integrates fuzzy logic with transformer architectures in parallel paths, establishing a novel neuro-fuzzy musculoskeletal (MSK) model that maps surface electromyography (sEMG) signals to joint torque through combined uncertainty and temporal modeling for enhanced real-time estimation. The variable impedance controller constructs the stiffness and damping matrices of the robotic system through the FESN and develops an adaptive update law for the FESN output weights, effectively addressing instability issues in variable stiffness control. Furthermore, driven by physiologically estimated joint torques from the HFTN, the adaption of the FESN reservoir states enables real-time modulation of stiffness and damping, facilitating transitions between human-dominated and robot-dominated modes. This realizes the AAN concept, ensuring personalized and responsive assistance. Various experiments on an upper limb rehabilitation robot were conducted to validate the effectiveness of both the neuro-fuzzy MSK model and the AAN controller in delivering optimal assistance while promoting active user participation.

Active suspended backpacks represent a promising solution to mitigate the impact of inertial forces on individuals engaged in load carriage. However, identifying effective control objectives aimed at enhancing human carrying capacity remains a significant challenge. In this study, we introduce a novel approach by integrating a limb-like structure-type (LLS) bioinspired vibration isolator, modeled using Lagrangian mechanics, into an active load-transfer suspended backpack to primarily alleviate human shoulder pressure, thereby constructing a humanrobot interaction control framework for the system. Drawing from a double-mass coupled oscillator model, this approach formulates a vertical dynamics model for the human-backpack system, systematically exploring the principles of both static load transfer and dynamic load reduction on the human shoulder. Subsequently, a series elastic actuators-based controller with prescribed performance is proposed to simultaneously achieve trajectory tracking and ensure load motion within the limited range. Theoretically, we validate the input-output stability of the LLS model and guarantee the ultimate uniform boundedness of the closed-loop system. Simulation and experimental trials conducted across different terrain scenarios validate the effectiveness of the proposed method, highlighting reductions of 18.68% in metabolic rate during level ground walking, 9.58% in a staircase scenario and 12.35% in a complex terrain, involving uphill, downstairs, and flat ground walking.

Recent incidents with autonomous vehicles highlight the need for rigorous testing to ensure safety and robustness. Constructing test scenarios for autonomous driving systems (ADSs), however, is labor-intensive. We propose TARGET, an end-to-end framework that automatically generates test scenarios from traffic rules. To address complexity, we leverage a Large Language Model (LLM) to extract knowledge from traffic rules. To mitigate hallucinations caused by large context during input processing, we introduce a domain-specific language (DSL) designed to be syntactically simple and compositional. This design allows the LLM to learn and generate test scenarios in a modular manner while enabling syntactic and semantic validation for each component. Based on these validated representations, TARGET synthesizes executable scripts to render scenarios in simulation. Evaluated seven ADSs with 284 scenarios derived from 54 traffic rules, TARGET uncovered 610 rule violations, collisions, and other issues. For each violation, TARGET generates scenario recordings and detailed logs, aiding root cause analysis. Two identified issues were confirmed by ADS developers: one linked to an existing bug report and the other to limited ADS functionality.

Federated learning (FL) is a distributed framework for machine learning that enables collaborative training of a shared model across data silos while preserving data privacy. However, the FL aggregation server faces a challenge in waiting for a large volume of model parameters from selected nodes before generating a global model, which leads to inefficient communication and aggregation. Although transmitting only the signs of stochastic gradient descent (SignSGD) reduces the transmission load, it decreases model accuracy, and the time waiting for local model collection remains substantial. Moreover, the security of FL is severely compromised by prevalent poisoning, backdoor, and DDoS attacks, causing ineffective and inaccurate model training. To overcome these challenges, this paper proposes aBlockchain-basedAsynchronousSignSGD (BASS) architecture for efficient and secure federated learning. By integrating a blockchain-based semi-asynchronous aggregation scheme with sign-based gradient compression, BASS considerably improves communication and aggregation efficiency, while providing resistance against attacks. Besides, a novel node-summarized sign aggregation algorithm is developed for the blockchain leaders to ensure the convergence and accuracy of the global model. An open-source prototype is developed, on top of which extensive experiments are conducted. The results validate the superiority of BASS in terms of efficiency, model accuracy, and security.

Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. FL is effective when dealing with independent and identically distributed (iid) datasets, but struggles with non-iid datasets. Various personalized approaches have been proposed, but such approaches fail to handle underlying shifts in data distribution, such as data distribution skew commonly observed in real-world scenarios (e.g., driver behavior in smart transportation systems changing across time and location). Additionally, trust concerns among unacquainted devices and security concerns with the centralized aggregator pose additional challenges. To address these challenges, this paper presents a dynamically optimized personal deep learning scheme based on blockchain and federated learning. Specifically, the innovative smart contract implemented in the blockchain allows distributed edge devices to reach a consensus on the optimal weights of personalized models. Experimental evaluations using multiple models and real-world datasets demonstrate that the proposed scheme achieves higher accuracy and faster convergence compared to traditional federated and personalized learning approaches.

Modern software systems are complex, and they heavily rely on external libraries developed by different teams and organizations. Such systems suffer from higher instability due to incompatibility issues caused by library upgrades. In this article, we address the problem by investigating the impact of a library upgrade on the behaviors of its clients. We developed CompCheck , an automated upgrade compatibility checking framework that generates incompatibility-revealing tests based on previous examples. CompCheck first establishes an offline knowledge base of incompatibility issues by mining from open source projects and their upgrades. It then discovers incompatibilities for a specific client project, by searching for similar library usages in the knowledge base and generating tests to reveal the problems. We evaluated CompCheck on 202 call sites of 37 open source projects and the results show that CompCheck successfully revealed incompatibility issues on 76 call sites, 72.7% and 94.9% more than two existing techniques, confirming CompCheck ’s applicability and effectiveness.

Program repair is an integral part of every software system's life-cycle but can be extremely challenging. To date, various automated program repair (APR) techniques have been proposed to reduce manual debugging efforts. However, given a real-world buggy program, a typical APR technique can generate a large number of patches, each of which needs to be validated against the original test suite, incurring extremely high computation costs. Although existing APR techniques have already leveraged various static and/or dynamic information to find the desired patches faster, they are still rather costly. In this work, we propose SeAPR (Self-Boosted Automated Program Repair), the first general-purpose technique to leverage the earlier patch execution information during APR to directly boost existing APR techniques themselves on-the-fly. Our basic intuition is that patches similar to earlier high-quality/low-quality patches should be promoted/degraded to speed up the detection of the desired patches. The experimental study on 13 state-of-the-art APR tools demonstrates that, overall, SeAPR can substantially reduce the number of patch executions with negligible overhead. Our study also investigates the impact of various configurations on SeAPR. Lastly, our study demonstrates that SeAPR can even leverage the historical patch execution information from other APR tools for the same buggy program to further boost the current APR tool.

The dynamic load attached to the load gravity imposes an excessive burden to human shoulders during load carriage, resulting in possible muscle injuries and additional physical exertion. This paper proposes an active suspension backpack, capable of transferring partial load from human shoulders to pelvis and alleviating the dynamic load through separated panels and motor actuation, to reduce pressure on human shoulders and improve walking metabolic efficiency. Based on the human body motion in the vertical direction, the dynamical model of the human-backpack system with shoulder interaction force measured by a soft ballonet with an embedded air pressure sensor is introduced, and an impedance controller has been implemented to maintain a relatively small and constant pressure on the shoulder. In an experimental case study, we presents preliminary results of three healthy subjects performing a treadmill walking with a 20kg load in ACTIVE configuration where the shoulder pressure shows a decrease by 30% along with a reduction of the metabolic energy consumption by 16.4%, compared with the load LOCKED case.

When developing autonomous driving systems (ADS), developers often need to replay previously collected driving recordings to check the correctness of newly introduced changes to the system. However, simply replaying the entire recording is not necessary given the high redundancy of driving scenes in a recording (e.g., keeping the same lane for 10 minutes on a highway). In this pa- per, we propose a novel test reduction and prioritization approach for multi-module ADS. First, our approach automatically encodes frames in a driving recording to feature vectors based on a driving scene schema. Then, the given recording is sliced into segments based on the similarity of consecutive vectors. Lengthy segments are truncated to reduce the length of a recording and redundant segments with the same vector are removed. The remaining seg- ments are prioritized based on both the coverage and the rarity of driving scenes. We implemented this approach on an industry- level, multi-module ADS called Apollo and evaluated it on three road maps in various regression settings. The results show that our approach significantly reduced the original recordings by over 34% while keeping comparable test effectiveness, identifying almost all injected faults. Furthermore, our test prioritization method achieves about 22% to 39% and 41% to 53% improvements over three baselines in terms of both the average percentage of faults detected (APFD) and TOP-K.

Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional software systems, autonomous driving systems (ADSs) often use deep neural networks in tandem with logic-based modules. This new paradigm poses unique challenges for software testing. Despite the recent development of new ADS testing techniques, it is not clear to what extent those techniques have addressed the needs of ADS practitioners. To fill this gap, we present the first comprehensive study to identify the current practices and needs of ADS testing. We conducted semi-structured interviews with developers from 10 autonomous driving companies and surveyed 100 developers who have worked on autonomous driving systems. A systematic analysis of the interview and survey data revealed 7 common practices and 4 emerging needs of autonomous driving testing. Through a comprehensive literature review, we developed a taxonomy of existing ADS testing techniques and analyzed the gap between ADS research and practitioners’ needs. Finally, we proposed several future directions for SE researchers, such as developing test reduction techniques to accelerate simulation-based ADS testing.

Manual debugging is notoriously tedious and time-consuming. Therefore, various automated fault localization techniques have been proposed to help with manual debugging. Among the existing fault localization techniques, spectrum-based fault localization (SBFL) is one of the most widely studied techniques due to being lightweight. The focus of the existing SBFL techniques is to consider how to differentiate program entities (i.e., one dimension in program spectra); indeed, this focus is aligned with the ultimate goal of finding the faulty lines of code. Our key insight is to enhance the existing SBFL techniques by additionally considering how to differentiate tests (i.e., the other dimension in program spectra), which, to the best of our knowledge, has not been studied in prior work. We present our basic approach, PRFL, a lightweight technique that boosts SBFL by differentiating tests using PageRank algorithm. Specifically, given the original program spectrum information, PRFL uses PageRank to recompute the spectrum by considering the contributions of different tests. Next, traditional SBFL techniques are applied on the recomputed spectrum to achieve more effective fault localization. On top of PRFL, we explore PRFL+ and PRFLMA, two variants which extend PRFL by optimizing its components and integrating Method-level Aggregation technique, respectively. Though being simple and lightweight, PRFL has been demonstrated to outperform state-of-the-art SBFL techniques significantly (e.g., ranking 39.2% / 82.3% more real/artificial faults at Top-1 compared with the most effective traditional SBFL technique) with low overhead (e.g., around 6 minutes average extra overhead on real faults) on 395 real faults from 6 Defects4J projects and 96925 artificial (i.e., mutation) faults from 240 GitHub projects. To further validate PRFL's effectiveness, we compare PRFL with multiple recent proposed fault localization techniques (e.g., Multric, Metallaxis and MBFL-hybrid-avg), and the experimental results show that PRFL outperforms them as well. Furthermore, we study the performance of PRFLMA, and the experimental results present it can locate 137 real faults (73.4% / 24.5% more compared with the most effective SBFL/PRFL technique) and 35058 artificial faults (159.6% / 28.1% more than SBFL/PRFL technique) at Top-1. At last, we study the generalizability of PRFL on another benchmark, Bugs.jar, and the result shows PRFL can help locate around 30 percent more faults at Top 1.

Deep learning has shown great success at interpreting unstructured data such as object recognition in images. Symbolic/logical-reasoning techniques have shown great success in interpreting structured data such as table extraction in webpages, custom text files, spreadsheets. The tables in PDF documents are often generated from such structured sources (text-based Word/LATEX documents, spreadsheets, webpages) but end up being unstructured. We thus explore novel combinations of deep learning and symbolic reasoning techniques to build an effective solution for PDF table extraction. We evaluate effectiveness without granting partial credit for matching part of a table (which may cause silent errors in downstream data processing). Our method achieves a 0.725 F1 score (vs. 0.339 for the state-of-the-art) on detecting correct table bounds-a much stricter metric than the common one of detecting characters within tables-in a well known public benchmark (ICDAR 2013) and a 0.404 F1 score (vs. 0.144 for the state-of-the-art) on our private benchmark with more widely varied table structures.

The Internet of Multimedia Things (IoMT) has become the backbone of innumerable multimedia applications in various fields. The wide application of IoMT not only makes our life convenient but also brings challenges to service discovery. Service discovery aims to leverage location information and trust evidence scattered in a variety of multimedia applications to find trusted IoMT devices that can provide specific service in target areas. However, the eavesdropping and tampering to these sensitive IoMT data during the trust propagation process invalidate the service discovery process. To address these challenges, we propose Secure Service Discovery (SSD) for IoMT using cross-blockchain-enabled fog computing. To resist the tampering and eavesdropping during the trust propagation process, a scalable cross-blockchain structure consisting of multiple parallel blockchains is first proposed based on fog, in which different parallel blockchains can be orchestrated to propagate encrypted location information and trust evidence of different applications. Moreover, to enable a cross-blockchain structure to leverage encrypted location information and trust evidence to find trusted IoMT devices in preset areas, a novel privacy-preserving range query is proposed to query and aggregate trust evidence. Security analysis and simulations are carried out to demonstrate the effectiveness and security of the proposed SSD.

Constraint-solving is an expensive phase for scenario finding tools. It has been widely observed that there is no single "dominant" SAT solver that always wins in every case; instead, the performance of different solvers varies by cases. Some SAT solvers perform particularly well for certain tasks while other solvers perform well for other tasks. In this paper, we propose an approach that uses machine learning techniques to automatically select a SAT solver for one of the widely used scenario finding tools, i.e. Alloy Analyzer, based on the features extracted from a given model. The goal is to choose the best SAT solver for a given model to minimize the expensive constraint solving time. We extract features from three different levels, i.e. the Alloy source code level, the Kodkod formula level and the boolean formula level. The experimental results show that our portfolio approach outperforms the best SAT solver by 30% as well as the baseline approach by 128% where users randomly select a solver for any given model.

Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with serious safety and security concerns. This paper describes DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. DeepCheck implements novel techniques for lightweight symbolic analysis of DNNs and applies them to address two challenging problems in DNN analysis: 1) identification of important input features and 2) leveraging those features to create adversarial inputs. Experimental results with an MNIST image classification network and a sentiment network for textual data show that DeepCheck promises to be a valuable tool for DNN analysis.

Effective program repair techniques, which modify faulty programs to fix them with respect to given test suites, can substantially reduce the cost of manual debugging. A common repair approach is to iteratively first generate candidate programs with possible bug fixes and then validate them against the given tests until a candidate that passes all the tests is found. While this approach is conceptually simple, due to the potentially high number of candidates that need to first be generated and then be compiled and tested, existing repair techniques that embody this approach have relatively low effectiveness, especially for faults at a fine granularity.

While Deep Neural Networks (DNNs) have established the fundamentals of image-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To address the safety issues in autonomous driving systems, a recent set of testing techniques have been designed to automatically generate artificial driving scenes to enrich test suite, e.g., generating new input images transformed from the original ones. However, these techniques are insufficient due to two limitations: first, many such synthetic images often lack diversity of driving scenes, and hence compromise the resulting efficacy and reliability. Second, for machine-learning-based systems, a mismatch between training and application domain can dramatically degrade system accuracy, such that it is necessary to validate inputs for improving system robustness.

Manually locating and removing bugs in faulty program is often tedious and error-prone. A common automated program repair approach called generate-and-validate (G&V) iteratively creates candidate fixes, compiles them, and runs these candidates against the given tests. This approach can be costly due to a large number of re-compilations and re-executions of the program. To tackle this limitation, recent work introduced the SketchFix approach that tightly integrates the generation and validation phases, and utilizes runtime behaviors to substantially prune a large amount of repair candidates. This tool paper describes our Java implementation of SketchFix, which is an open-source library that we released on Github. Our experimental evaluation using Defects4J benchmark shows that SketchFix can significantly reduce the number of re-compilations and re-executions compared to other approaches and work particularly well in repairing expression manipulation at the AST node-level granularity.The demo video is at: https://youtu.be/AO-YCH8vGzQ.

Among the approaches that investigate the similarity between web services, hardly any concentrates on the impacts from contexts. In this paper we introduce service2vec which is an approach to represent web services as service embeddings based on a recent popular deep learning technique word2vec. Our approach composes and combines web services to be a document that is trained by the modeling technique of word2vec. As a result, each web service in the document is vectorized. By taking the advantage of word2vec, the resulting service embeddings of service2vec can be used to illustrate the contextual relations between web services. The experimental results suggest that service2vec can deliver contextual similarity between web services.

Manual debugging is notoriously tedious and time consuming. Therefore, various automated fault localization techniques have been proposed to help with manual debugging. Among the existing fault localization techniques, spectrum-based fault localization (SBFL) is one of the most widely studied techniques due to being lightweight. A focus of existing SBFL techniques is to consider how to differentiate program source code entities (i.e., one dimension in program spectra); indeed, this focus is aligned with the ultimate goal of finding the faulty lines of code. Our key insight is to enhance existing SBFL techniques by additionally considering how to differentiate tests (i.e., the other dimension in program spectra), which, to the best of our knowledge, has not been studied in prior work.