Reviewerly/exHarmony huggingface dataset:
CFG2VEC: Hierarchical Graph Neural Network for Cross-Architectural Software Reverse Engineering

Mission-critical embedded software is critical to our society’s infrastructure but can be subject to new security vulnerabilities as technology advances. When security issues arise, Reverse Engineers (REs) use Software Reverse Engineering (SRE) tools to analyze vulnerable binaries. However, existing tools have limited support, and REs undergo a time-consuming, costly, and error-prone process that requires experience and expertise to understand the behaviors of software and vulnerabilities. To improve these tools, we propose cfg2vec, a Hierarchical Graph Neural Network (GNN) based approach. To represent binary, we propose a novel Graph-of-Graph (GoG) representation, combining the information of control-flow and function-call graphs. Our cfg2vec learns how to represent each binary function compiled from various CPU architectures, utilizing hierarchical GNN and the siamese network-based supervised learning architecture. We evaluate cfg2vec’s capability of predicting function names from stripped binaries. Our results show that cfg2vec outperforms the state-of-the-art by 24.54% in predicting function names and can even achieve 51.84% better given more training data. Additionally, cfg2vec consistently outperforms the state-of-the-art for all CPU architectures, while the baseline requires multiple training to achieve similar performance. More importantly, our results demonstrate that our cfg2vec could tackle binaries built from unseen CPU architectures, thus indicating that our approach can generalize the learned knowledge. Lastly, we demonstrate its practicability by implementing it as a Ghidra plugin used during resolving DARPA Assured MicroPatching (AMP) challenges.
Preference Adaptation: user satisfaction is all you need!

Decision making in self-adaptive systems often involves trade-offs between multiple quality attributes, with user preferences that indicate the relative importance and priorities among the attributes. However, eliciting such preferences accurately from users is a difficult task, as they may find it challenging to specify their preference in a precise, mathematical form. Instead, they may have an easier time expressing their displeasure when the system does not exhibit behaviors that satisfy their internal preferences. Furthermore, the user’s preference may change over time depending on the environmental context; thus, the system may be required to continuously adapt its behavior to satisfy this change in preference. However, existing self-adaptive frameworks do not explicitly consider dynamic human preference as one of the sources of uncertainty. In this paper, we propose a new adaptation framework that is specifically designed to support self-adaptation to user preference. Our framework takes a human-on-the-loop approach where the user is given an ability to intervene and indicate dissatisfaction and corrections with the current behavior of the system; in such a scenario, the system automatically updates the existing preference values so that the new, resulting behavior of the system is consistent with the user’s notion of satisfactory behavior. To perform this adaptation, we propose a novel similarity analysis to produce changes in the preference that are optimal with respect to the system utility. We illustrate our approach in a case study involving a delivery robot system. Our preliminary results indicate that our approach can effectively adapt its behavior to changing human preference.
Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

Small uncrewed aerial systems, sUAS, provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means.Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight.However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions.To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework, CICADA, for Controller Instability-preventing Configuration Aware Drone Adaptation.CICADA dynamically detects unstable drone behavior during flight and adapts to mitigate this threat.We have built a prototype of CICADA using a popular open source sUAS simulator and experimented with a large number of different configurations.Experimental results show that CICADA's adaptations reduce controller instability and enable the sUAS to recover from a significant number of poor configurations.In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes.
Fulfilling Industrial Needs for Consistency Among Engineering Artifacts

Maintaining the consistency of engineering artifacts is a challenge faced by several engineering companies. This is more evident when the engineering artifacts are created using different tools and have different formats. This is the context of a company that builds agricultural machines, where components are developed using a decentralized iterative process. In this study, we present an approach developed in collaboration with an industry partner to address the issues and requirements of a real engineering scenario. These issues include the manual execution of consistency checking, without guidelines that formalize the activity. Furthermore, the industry partner aims at a flexible solution that can be applied without disrupting the current development process significantly. The proposed approach applies consistency rules (CR) defined to automatically detect and provide inconsistency feedback to engineers in real-time. The approach presented in this work also allows the customization of the CRs, giving flexibility to how the consistency checking is applied. The feasibility of our approach is demonstrated in such an industrial scenario, with a discussion about how the issues were addressed and the limitations of the current solution. We also perform a scalability evaluation showing that the approach can be applied in large systems (up to 21,061 elements) in a reasonable amount of time, taking less than 0.25 milliseconds to apply a CR, in the worst cases.
Generalized Conversion Based Nonlinear Filtering Using Deterministic Sampling for Target Tracking

For nonlinear filtering, the linear minimum mean square error (LMMSE) estimation is popular. An LMMSE-based estimator using a measurement conversion can outperform the LMMSE estimator using the original measurement. However, to optimally obtain both the dimension and also the form of such a conversion is difficult, because this involves functional optimization. To solve this problem, this paper proposes a generalized conversion based filter (GCF) using deterministic sampling (DS). Being an LMMSE-based estimator using a general conversion of the measurement, the estimation performance of the GCF depends only on the conversion-related moments, which are calculated using DS. A constraint on the conversion is used to reduce possible evaluation errors of using a DS method to calculate those moments. The GCF optimizes the moments by obtaining both the optimal dimension and also the sample points of the conversion rather than the specific form of it. Then the final form of the GCF is analytically obtained. For tracking of multiple or maneuvering targets, the likelihood based on the proposed GCF is also derived, and it can be calculated using the obtained conversion sample also in an analytical form. Simulation results demonstrate the effectiveness of the GCF compared with some popular and recently-proposed nonlinear estimators, including the LMMSE estimator and existing conversion-based filters.
Exploring Interactive and Contrastive Relations for Nested Named Entity Recognition

Nested named entities (nested NEs) refer to the situation where one named entity is included or nested within another named entity, which cannot be recognized by the traditional sequence labeling methods. Recently, span-based methods have become the mainstream methods for nested Named Entity Recognition (nested NER). The fundamental concept behind this method is to enumerate nearly all potential spans as entity mentions and subsequently classify them. However, span-based methods independently classify spans without considering the semantic relations among them, which negatively impacts the span representation. To address the issue, we propose a novel deep learning architecture for nested NER that explores interactive and contrastive relations among spans. Specifically, we design a scale transformation mechanism that embeds geometric information into span representations, which enhances the model's ability to encode interactive relations between spans. Additionally, we introduce a supervised contrastive learning loss that pulls apart highly overlapping spans in the embedding space to encode the contrastive relations. Experiments show that our method achieves state-of-the-art or competitive performance on three publicly nested NER datasets, thus validating its effectiveness.
PAINTIQUE: Design and Development of Interaction Model of a Line Following Spray-Painting Robot

The monotonous, demanding, and potentially dangerous nature of wall painting makes it a prime candidate for automation. Car manufacturing industries have already implemented robot painting, but not the construction sector. A mobile robot with the ability to paint is desperately needed to paint the interior walls of houses. In this paper, the Line Following method is used to create an autonomous mobile robot with two distinct features: line-following and spray painting. It also shows the interaction with lines and walls by the robot. The plan is to minimize the need for humans to manually paint high-rise buildings. The robot is equipped with infrared (IR) sensors that help the robot to detect the path. The spray nozzle is moved by a servo motor as the wall is painted. The Arduino Uno board serves as the robot’s primary controller and powers the entire project. Finally, the robot’s performance is evaluated to ensure that it follows the line correctly and smoothly without drifting off path. Experiments are conducted on the design to find the optimum design, control, and programming for the robots’ best performance. Based on the outcomes, the robot was successful in completing the tasks assigned to it.
Safe Autonomous Decision-Making with vGOAL

Safety is one of the crucial features of autonomous systems. Safe decision-making is a critical and challenging task in developing such systems. To address this challenge, we proposed vGOAL, a GOAL-based specification language designed for ensuring safe autonomous decision-making. In this paper, we present an interpreter for vGOAL, serving as an agent-based decision-making component for autonomous systems. Our main contributions are the design and implementation of the vGOAL interpreter, which automatically and efficiently generates safe decisions in real-time, while simultaneously performing safety checking, error handling, and conflict resolution for competing requests. As the Robot Operating System is a popular framework for developing robotic application systems, we integrate the vGOAL interpreter with it via rosbridge. To demonstrate the effectiveness of vGOAL, we validated its performance using a real-world autonomous logistic system comprising three autonomous mobile robots.
A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems

For mobile robots to operate in an autonomous and safe manner they must be able to adequately perceive their environment despite challenging or unpredictable conditions in their sensory apparatus. Usually, this is addressed through ad-hoc, not easily generalizable Fault Detection and Diagnosis (FDD) approaches. In this work, we leverage Bayesian Networks (BNs) to propose a novel probabilistic inference architecture that provides generality, rigorous inferences and real-time performance for the detection, diagnosis and recovery of diverse and multiple sensory failures in robotic systems. Our proposal achieves all these goals by structuring a BN in a multidimensional setting that up to our knowledge deals coherently and rigorously for the first time with the following issues: modeling of complex interactions among the components of the system, including sensors, anomaly detection and recovery; representation of sensory information and other kinds of knowledge at different levels of cognitive abstraction; and management of the temporal evolution of sensory behavior. Real-time performance is achieved through the compilation of these BNs into feedforward neural networks. Our proposal has been implemented and tested for mobile robot navigation in environments with human presence, a complex task that involves diverse sensor anomalies. The results obtained from both simulated and real experiments prove that our architecture enhances the safety and robustness of robotic operation: among others, the minimum distance to pedestrians, the tracking time and the navigation time all improve statistically in the presence of anomalies, with a diversity of changes in medians ranging from ≃20% to ≃500%.
HCV: Practical Multi-Keyword Conjunctive Query with Little Result Pattern Leakage

Multi-keyword conjunctive query is the most common searchable encryption (SE) scheme and gives practical search capability. This paper focuses on constructing a conjunctive query scheme with high privacy and accurate query result. For efficiency, this paper introduces a novel counter vector (CV) data structure instead of inverted index and builds the CV index database with three-tuple keywords. We use two kinds of cryptography primitives to encrypt the CV index database, respectively. One is symmetric encryption for efficiency, which gives two schemes, CVX and ICV. Experiments show that both CVX and ICV have much higher efficiency than the existing schemes. The other is BGN homomorphic encryption for privacy and gives the scheme HCV. HCV reduces much RP information leakage and even achieves “ideal” leakage for 3-keyword query. Experiments show that the HCV scheme achieves high privacy while compromising not so much storage.
Privacy and Data Protection in ChatGPT and Other AI Chatbots

The evolution of artificial intelligence (AI) and machine learning (ML) has led to the development of sophisticated large language models (LLMs) that are used extensively in applications such as chatbots. This research investigates the critical issues of data protection and privacy enhancement in the context of LLM-based chatbots, with a focus on OpenAI's ChatGPT. It explores the dual challenges of safeguarding sensitive user information while ensuring the efficiency of machine learning models. It assesses existing privacy-enhancing technologies (PETs) and proposes innovative methods, such as differential privacy, federated learning, and data minimization techniques. The study also includes a survey of Chatbot users to measure their concerns related to data privacy with the use of these LLM-based applications. This study is meant to serve as a comprehensive guide for developers, policymakers, and researchers, contributing to the discourse on data protection in artificial intelligence.
A robust and secure user authentication scheme based on multifactor and multi‐gateway in <scp>IoT</scp> enabled sensor networks

Abstract The Internet of Things (IoT) enabled wireless sensor networks (WSNs) are used to collect data from various nodes in hostile environments, but it is essential to authenticate legitimate nodes to prevent malicious attacks. Traditional authentication schemes may be vulnerable to attack, necessitating the development of more secure authentication techniques. To address this problem, we propose a novel authentication technique for multi‐gateway IoT‐enabled WSNs that achieves effective and secure data communication. The proposed scheme is based on biological information, hash, and XOR operations, which reduce computational costs. Passwords, biometric information, and session keys are updated securely to maintain forward and backward secrecy during communication. We provide an informal security analysis of the proposed scheme and a formal security analysis of the session key using the real‐or‐random (ROR) model. We also use the AVISPA simulation toolkit to verify the proposed user authentication scheme. Experimental results demonstrate that our proposed scheme is more effective than previous multi‐factor multi‐gateway authentication protocols.
Three-dimensional freeform reflector design with a scattering surface

We introduce an approach to calculating three-dimensional freeform reflectors with a scattering surface. Our method is based on optimal transport and utilizes a Fredholm integral equation to express scattering. By solving this integral equation through a process analogous to deconvolution, we can recover a typical specular design problem. Consequently, we consider freeform reflector design with a scattering surface as a two-step process wherein the target distribution is first altered to account for scattering, and then the resulting specular problem is solved. We verify our approach using a custom raytracer that implements the surface scattering model we used to derive the Fredholm integral.
Training a robotic arm to estimate the weight of a suspended object

Empowered by machine learning (ML) and statistics, researchers are able to leverage a larger amount and variety of data to facilitate research processes. Moreover, computational tools can mine and translate insights from data for multiple purposes. Here, we present a new approach to estimating the weight of an object using robotic torque data. Our focus is on processing and analyzing raw torque data collected from the joints of a robotic arm that was originally intended for robotic movement and security. We demonstrate that by applying ML models using scikit-learn, it is possible to overcome the limitations of summary statistics and accurately predict the weight of an object held by the arm. Our work provides a practical example of how ML can be applied to data science problems beyond simple curve fitting, and we provide a workflow guideline to make this approach more accessible to researchers outside the ML field.
Principal weighted least square support vector machine: An online dimension-reduction tool for binary classification

As relevant technologies advance, streamed data are frequently encountered in various applications, and the need for scalable algorithms becomes urgent. In this article, we propose the principal weighted least square support vector machine (PWLSSVM) as a novel tool for SDR in binary classification where most SDR methods suffer since they assume continuous Y. We further show that the PWLSSVM can be employed for the online SDR for the streamed data. Namely, the PWLSSVM estimator can be directly updated from the new data without having old data. We explore the asymptotic properties of the PWLSSVM estimator and demonstrate its promising performance in terms of both estimation accuracy and computational efficiency for both simulated and real data.
An efficient chain code for access control in hyper ledger fabric healthcare system

The emergence of eHealth has been instrumental in enhancing healthcare systems, offering improved health services to diverse patients with the aid of cutting-edge technologies. The safe and effective collection, transmission, and accessibility of electronic health records (EHRs) using modern information and communication technologies (ICTs) is essential for realizing the goals of eHealth. However, the use of EHRs by healthcare practitioners creates the risk of data breaches and jeopardizes patient confidentiality. Fortunately, smart contracts and blockchain technology offer effective EHR access control techniques that can enable secure client identification, authentication, and authorization, enhancing the security of EHRs.So, this paper brings effective Blockchain-based solutions using Hyperledger fabric for providing privacy to EHRs. An effective deployment architecture and chain code were generated for design and access control. In this case, the utilization of Hyperledger Fabric as the Blockchain technology introduces a new consensus mechanism, enabling efficient scalability and safeguarding anonymity. By employing Hyperledger Fabric for framework development, the system achieves its objectives. Experimental results are carried out for analyzing the system performance and also computational tests.
A privilege-constrained sanitizable signature scheme for e-health systems

Electronic health record (EHR) sharing schemes are widely used in healthcare, medical, and research. However, privacy may be a concern for patients with EHRs. In this paper, a secure EHR sharing scheme with sanitizable signature is proposed to protect patients' privacy and enhance accountability. Our proposed scheme makes the following contributions: (1) doctors can specify patients to modify some fields before the expiration time; (2) patients can convert the original signature into a new and unlinkable signature for the modified record without interacting with the doctor; (3) the scheme satisfies traceability and can distinguish the generator of a given signature. In contrast to existing approaches, we introduce a new limited sanitizable signature scheme as the main ingredient, which allows the signer not only to decide which message blocks can be modified, but also to determine the maximum number of modifiable blocks and the expiration time for sanitization. Finally, the security analysis and experimental results show that the security and efficiency of our scheme can be approved.
Migration stress, risky Internet uses, and scam victimization: An empirical study among Chinese migrant workers

In China, Internet scams against migrant workers have grown increasingly prevalent. This study uses routine activity theory and uses and gratifications theory to investigate how migration stress affects susceptibility to risky internet use and scam-related outcomes among Chinese migrant workers. The present study utilized a survey of 543 migrant workers. Findings reveal that migration stress was a significant predictor of engaging in risky online leisure activities, risky online shopping, privacy disclosure, and a lack of protective measures. The failure to adopt protective behaviors and engagement in risky online leisure activities, online loaning, and privacy disclosure were also found to increase migrant workers’ exposure to scam information and victimization. Study results sheds light on the impact of migration stress on the online behavior of migrant workers in China and provides insights into protecting them from Internet scams. Our findings may inform the development of interventions aimed at preventing online scams against migrant workers and promoting safer Internet use.