All critical systems must evolve to meet the needs of a growing and diversifying user base. But supporting that evolution is challenging at increasing scale: Maintainers must find a way to ensure that each change does only what is intended, and will not inadvertently change behavior for existing users. This paper presents how we addressed this challenge for the Amazon Web Services (AWS) authorization engine, invoked 1 billion times per second, by using formal verification. Over a period of four years, we built a new authorization engine, one that behaves functionally the same as its predecessor, using the verification-aware programming language Dafny. We can now confidently deploy enhancements and optimizations while maintaining the highest assurance of both correctness and backward compatibility. We deployed the new engine in 2024 without incident and customers immediately enjoyed a threefold performance improvement. The methodology we followed to build this new engine was not an off-the-shelf application of an existing verification tool, and this paper presents several key insights: 1) Rather than prove correct the existing engine, written in Java, we found it more effective to write a new engine in Dafny, a language built for verification from the ground up, and then compile the result to Java. 2) To ensure performance, debuggability, and to gain trust from stakeholders, we needed to generate readable, idiomatic Java code, essentially a transliteration of the source Dafny. 3) To ensure that the specification matches the system's actual behavior, we performed extensive differential and shadow testing throughout the development process, ultimately comparing against 1015production samples prior to deployment. Our approach demonstrates how formal verification can be effectively applied to evolve critical legacy software at scale.

Data processing frameworks like Apache Spark and Flink provide built-in support for user-defined aggregation functions (UDAFs), enabling the integration of domain-specific logic. However, for these frameworks to support efficient UDAF execution, the function needs to satisfy a homomorphism property, which ensures that partial results from independent computations can be merged correctly Motivated by this problem, this paper introduces a novel homomorphism calculus that can both verify and refute whether a UDAF is a dataframe homomorphism. If so, our calculus also enables the construction of a corresponding merge operator which can be used for incremental computation and parallel execution. We have implemented an algorithm based on our proposed calculus and evaluate it on real-world UDAFs, demonstrating that our approach significantly outperforms two leading synthesizers.

In this work, we describe a new software model-checking algorithm called GPS. GPS treats the task of model checking a program as a directed search of the program states, guided by a compositional, summary-based static analysis. The summaries produced by static analysis are used both to prune away infeasible paths and to drive test generation to reach new, unexplored program states. GPS can find both proofs of safety and counter-examples to safety (i.e., inputs that trigger bugs), and features a novel two-layered search strategy that renders it particularly efficient at finding bugs in programs featuring long, input-dependent error paths. To make GPS refutationally complete (in the sense that it will find an error if one exists, if it is allotted enough time), we introduce an instrumentation technique and show that it helps GPS achieve refutation-completeness without sacrificing overall performance. We benchmarked GPS on a diverse suite of benchmarks including programs from the Software Verification Competition (SV-COMP), from prior literature, as well as synthetic programs based on examples in this paper. We found that our implementation of GPS outperforms state-of-the-art software model checkers (including the top performers in SV-COMP ReachSafety-Loops category), both in terms of the number of benchmarks solved and in terms of running time.

This paper presents an automated reasoning technique for checking equivalence between graph database queries written in Cypher and relational queries in SQL. To formalize a suitable notion of equivalence in this setting, we introduce the concept of database transformers , which transform database instances between graph and relational models. We then propose a novel verification methodology that checks equivalence modulo a given transformer by reducing the original problem to verifying equivalence between a pair of SQL queries. This reduction is achieved by embedding a subset of Cypher into SQL through syntax-directed translation, allowing us to leverage existing research on automated reasoning for SQL while obviating the need for reasoning simultaneously over two different data models. We have implemented our approach in a tool called Graphiti and used it to check equivalence between graph and relational queries. Our experiments demonstrate that Graphiti is useful both for verification and refutation and that it can uncover subtle bugs, including those found in Cypher tutorials and academic papers.

Data reliability and algorithm robustness are both important for wearable devices. To validate the accuracy of a recently published research vehicle, WeBe band, we conducted a concurrent heart rate (HR) and galvanic skin response (GSR) validity study. WeBe band, Empatica E4 and MindWare, which is currently considered the gold standard for collecting these measures, are compared concurrently. Fifty healthy adult partic-ipants volunteered (female n=29, 49 in 18–25 age range, 1 in 26–30 age range; [mean (SD)]: height = 167.6 (8.9) cm, mass = 150.1 (33.1) lbs). Participants wore the WeBe band and the Empatica band on opposite wrists (alternating device placement between participants) and the MindWare electrodes were placed on the on chest, back, and palms. Each participant completed a study session (a total 51 minutes) that included sitting, standing, normal paced walking and faster paced walking. Data was processed and validity was measured though: mean absolute percent error (MAPE), Bland-Altman limits of aggreement (LOA) and concordance coefficient (rc). Results showed that WeBe band is valid under all conditions.

This paper presents a novel Electrodermal Activ-ity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on enhancing the accuracy and efficiency of EDA measurements, a reliable indicator of stress. Traditional EDA monitoring solutions often grapple with trade-offs between sensor placement, cost, and power consumption, leading to compromised data accuracy. Our innovative design incorporates an adaptive gain mechanism, catering to the broad dynamic range and high-resolution needs of EDA data analysis. The performance of our system was extensively tested through simulations and a custom Printed Circuit Board (PCB), achieving an error rate below 1 % and maintaining power consumption at a mere$\mathbf{700}\mu \mathbf{A}$under a 3.$7\mathbf{V}$power supply. This research contributes significantly to the field of wearable health technology, offering a robust and efficient solution for long-term stress monitoring.

With the evolution of wireless communication ser-vices, the requirement for reliability and latency is more and more strict, especially in the field of personal consumption area and the Industrial Internet of Things (IIoT), such as autonomous vehicles, remote healthcare, smart factory, augmented reality (AR)/virtual reality (VR), and so on. Moreover, the requirement for reliability and latency is also diverse for different types of communication services. Under the requirement of the enhancement of such hyper reliability and low latency communications (HRLLC) and the fact of diver user equipment capabilities, this paper proposes a collaborative ranging aware UE aggregation transmission mechanism for future IoT networks. Under the proposed mechanism, a ranging aware scheduling (RAS) based commu-nication procedure is designed for reliability enhancement and latency reduction, wherein some UEs or particular higher-end devices play as cooperation nodes (CNs) for packet duplication through multiple paths simultaneously. Considering each CN's system signaling overhead and signal processing capability with a restriction of latency requirement, the scheduling algorithm RAS is performed at each CN. According to the presented simulation results, the research work in this paper can provide insight into the distributed cooperation system for future IoT networks.

Compiling high-level programs to high-speed packet-processing pipelines is a challenging combinatorial optimization problem. The compiler must configure the pipeline’s resources to match the semantics of the program’s high-level specification, while packing all of the program’s computation into the pipeline’s limited resources. State of the art approaches tackle individual aspects of this problem. Yet, they miss opportunities to produce globally high-quality outcomes within reasonable compilation times. We develop a framework to decompose the compilation problem for such pipelines into three phases—making extensive use of solver engines (e.g., ILP, SMT, and program synthesis) to simplify the development of these phases. Transformation rewrites programs to use more abundant pipeline resources, avoiding scarce ones. Synthesis breaks complex transactional code into configurations of pipelined compute units. Allocation maps the program’s compute and memory to the pipeline’s hardware resources. We prototype these ideas in a compiler, CaT, which targets (1) the Tofino programmable switch pipeline and (2) Menshen, a cycle-accurate simulator of a Verilog description of the RMT pipeline. CaT can handle programs that existing compilers cannot currently run on pipelines and generates code faster than existing compilers, where the generated code uses fewer pipeline resources.

In the realm of physiological-based affective computing, significant progress has been witnessed in machine learning over the last two decades. Nevertheless, the lack of consistency in measurement tools and data organization across diverse datasets poses a challenge when integrating new datasets for algorithm testing, research, and result comparison across multiple datasets. Despite the expansion of artificial intelligence-driven affective computing, a notable gap remains in the form of a comprehensive toolkit tailored for both machine learning researchers and psychologists who are new to the field of machine learning. In response to these challenges, we introduce a Python toolkit designed to fulfill two key roles: establishing a standardized benchmark for affective computing datasets and offering an all-encompassing toolkit for machine learning in physiological signal based affective computing. This toolkit encompasses vital components essential to the machine learning process, encompassing tasks like dataset integration and interpretation, signal preprocessing, feature derivation, post-processing, classification models, and evaluation metrics. Our proposed toolkit is designed for working with seven publicly available datasets, embracing five different modalities and incorporating twenty diverse machine learning models spanning from conventional options like the support vector machine (SVM) to cutting-edge deep learning models. To the best of our knowledge, the proposed toolkit stands as the pioneering initiative for creating a standardized dataset benchmarking system and a comprehensive solution tailored for machine learning applications in affective computing. The open-source codebase for the proposed toolkit is accessible via https://github.com/rjfang/pyAffeCT.

In modern society, people are exposed to various stressors and negative emotions daily and they may cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, this paper delves into the potential of modern wearable technologies as a tool for real-time health monitoring. The advent of ubiquitous sensing has ushered in an era where physiological and behavioral measurements can be continuously recorded in daily life. One significant physiological marker is the Galvanic Skin Response (GSR), which exhibits noteworthy changes under different emotional states. We propose a machine learning-based emotion recognition framework. It includes a preprocessing stage that eliminates noise and extracts 87 features from the GSR data. To account for individual differences in physiological responses, we also introduce a novel normalization procedure per subject. Finally, a subset of dominant and discriminative features enhances the proposed framework’s performance. We conducted experiments on two datasets, the wearable stress and affect detection dataset (WESAD) for stress detection, and the multimodal MAHNOB-HCI dataset for emotion recognition. The results show that the Leave-One-Out method is capable of detecting stress with 97.03% accuracy. Moreover, the proposed method classifies arousal and valence with an accuracy of 82.20% and 82.57%, respectively.

Physiological signal-based pattern recognition has progressed significantly, such as automated pain assessment and stress detection. Public datasets provide a research platform to conduct machine learning studies. However, models trained from public datasets easily overfit that specific dataset and do not apply to unseen data collected in real-life scenarios. This paper proposes to use the transfer learning-based domain generalization technique to generalize the models to solve this issue. Data from different training domains are generalized, i.e., the dissimilarity is minimized by the proposed approach such that the model trained is generalized. We proved that the generalized model is more adaptive to new unseen data. Experiments have been done on the BioVid heat pain dataset and WESAD stress dataset, and results showed that our proposed methods significantly improve the model performance on new unseen data.

Stress detection is an emerging field. WESAD is a commonly used public dataset for automated stress detection. It contains physiological signals including ECG, EDA, EMG, ACC, BVP, EDA, and skin temperature. The time window approach is used to extract features from time-series physiological signals. We find in previous studies that a 60-second time window with a 0.25-second window shift is widely used, but such window settings may cause redundancy and over-fitting. Thus, we propose to use (1) new window settings and (2) normalization per subject to tackle this problem. The experiment results show that our proposed methods significantly increase the classification performance.

Stress is an inevitable part of our lives in modern society since in many situations people are exposed to various stressors daily. According to studies, long-term stress can cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, stress detection is one of the crucial areas of study to maintain a healthy life. Recently, by developing commercial wearable technologies, real-time and continuous data collection for personal stress monitoring becomes more feasible. Under stress conditions, there are notable changes in physiological signals such as heart rate, respiration, perspiration, and eye pupil dilation. Previous studies have shown that Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), can identify stress. EDA measures changes in perspiration by detecting the changes in the electrical conductivity of the skin. This paper focuses on stress detection using only EDA wearable sensors and applied machine learning techniques. First, 87 different features are extracted from EDA signals. Then, the data are normalized per subject because of differences in individuals’ physiological responses. Finally, five dominant features in stress detection are selected. We used a publicly available dataset, namely, the wearable stress and affect detection dataset (WESAD) in this study. The results show that the One-Leave-Out method is capable of detecting stress with 97.03% accuracy.