bluuebunny/crossref_metadata_2025 huggingface dataset:
Technical Design Space Analysis for Unobtrusive Driver Emotion Assessment Using Multi-Domain Context
Authors: Bethge David , Coelho Luis Falconeri , Kosch Thomas , Murugaboopathy Satiyabooshan , Zadow Ulrich von , Schmidt Albrecht , Grosse-Puppendahl Tobias
Published: January 2023

Driver emotions play a vital role in driving safety and performance. Consequently, regulating driver emotions through empathic interfaces have been investigated thoroughly. However, the prerequisite - driver emotion sensing - is a challenging endeavor: Body-worn physiological sensors are intrusive, while facial and speech recognition only capture overt emotions. In a user study (N=27), we investigate how emotions can be unobtrusively predicted by analyzing a rich set of contextual features captured by a smartphone, including road and traffic conditions, visual scene analysis, audio, weather information, and car speed. We derive a technical design space to inform practitioners and researchers about the most indicative sensing modalities, the corresponding impact on users' privacy, and the computational cost associated with processing this data. Our analysis shows that contextual emotion recognition is significantly more robust than facial recognition, leading to an overall improvement of 7% using a leave-one-participant-out cross-validation.
Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers
Authors: Liberis Edgar , Lane Nicholas D.
Published: January 2023

Wearable, embedded, and IoT devices are a centrepiece of many ubiquitous computing applications, such as fitness tracking, health monitoring, home security and voice assistants. By gathering user data through a variety of sensors and leveraging machine learning (ML), applications can adapt their behaviour: in other words, devices become "smart". Such devices are typically powered by microcontroller units (MCUs). As MCUs continue to improve, smart devices become capable of performing a non-trivial amount of sensing and data processing, including machine learning inference, which results in a greater degree of user data privacy and autonomy, compared to offloading the execution of ML models to another device.

Advanced predictive capabilities across many tasks make neural networks an attractive ML model for ubiquitous computing applications; however, on-device inference on MCUs remains extremely challenging. Orders of magnitude less storage, memory and computational ability, compared to what is typically required to execute neural networks, impose strict structural constraints on the network architecture and call for specialist model compression methodology. In this work, we present a differentiable structured pruning method for convolutional neural networks, which integrates a model's MCU-specific resource usage and parameter importance feedback to obtain highly compressed yet accurate models. Compared to related network pruning work, compressed models are more accurate due to better use of MCU resource budget, and compared to MCU specialist work, compressed models are produced faster. The user only needs to specify the amount of available computational resources and the pruning algorithm will automatically compress the network during training to satisfy them.

We evaluate our methodology using benchmark image and audio classification tasks and find that it (a) improves key resource usage of neural networks up to 80x; (b) has little to no overhead or even improves model training time; (c) produces compressed models with matching or improved resource usage up to 1.4x in less time compared to prior MCU-specific model compression methods.
PackquID
Authors: Shang Fei , Yang Panlong , Yan Yubo , Li Xiang-Yang
Published: January 2023

There are many scenarios where the liquid is occluded by other items (e.g. books in a packet), in which existing RF-based liquid identification methods are generally not suitable. Moreover, status methods are not applicable when the height of the liquid to be tested changes. This paper proposes PackquID, an RF-based in-packet liquid identification system, which can identify liquid without prior knowledge. In dealing with the obstruction of other items and the unknown container, we utilize a dual-antenna model and craft a relative frequency response factor, exploring the diversity of the permittivity in the frequency domain. In tackling the variable liquid height, we extend our model to 3D scope by analyzing the electric field distribution and solving the height effect via spatial-differential model. With 500 pages of printer paper obscured, PackquID can identify 9 common liquids, including Coca-Cola and Pepsi, with an accuracy of over 86% for 4 different packets (canvas bag, paper bag, backpack, and box) and 4 different containers. Nevertheless, PackquID can still identify liquids with an accuracy rate of over 87%, even when the liquid height changes from 4 cm to 12 cm.
TransFloor
Authors: Xie Zhiqing , Luo Haiyong , Zhang Xiaotian , Xiong Hao , Zhao Fang , Li Zhaohui , Ye Qi , Rong Bojie , Gao Jiuchong
Published: January 2023

Smart on-demand delivery services require accurate indoor localization to enhance the system-human synergy experience of couriers in complex multi-story malls and platform construction. Floor localization is an essential part of indoor positioning, which can provide floor/altitude data support for upper-level 3D indoor navigation services (e.g., delivery route planning) to improve delivery efficiency and optimize order dispatching strategies. We argue that due to label dependence and device dependence, the existing floor localization methods cannot be flexibly deployed on a large scale in numerous multi-story malls across the country, nor can they apply to all couriers/users on the platform. This paper proposes a novel self-evolving and user-transparent floor localization system named TransFloor, based on crowdsourcing delivery data (e.g., order status and sensors data) without additional label investment and specialized equipment constraints. TransFloor consists of an unsupervised barometer-based module--IOD-TKPD and an NLP-inspired Wi-Fi-based module--Wifi2Vec, and Self-Labeling is a perfect bridge between both to completely achieve label-free and device-independent floor positioning. In addition, TransFloor is designed as a lightweight plugin embedded into the platform without refactoring the existing architecture, and it has been deployed nationwide to adaptively launch real-time accurate 3D/floor positioning services for numerous crowdsourcing couriers. We evaluate TransFloor on real-world records from an instant delivery platform (involving 672,282 orders, 7,390 couriers, and 6,206 merchants in 388 malls during two months). It can achieve an average accuracy of 94.61% and demonstrate good robustness to device heterogeneity and adaptive durability, outperforming existing state-of-the-art methods. Crucially, it can effectively improve user satisfaction and reduce overdue delivery by providing accurate floor navigation information in complex multi-story malls. As a case study, the platform reduces erroneous order scheduling by 60% and overdue delivery by 2.7%, and increases delivery efficiency by reducing courier arrival time by 12.27 seconds accounting for 7.29%. We believe that the key ideas of TransFloor can be extended to other crowdsourcing scenarios for the public further.
PACT
Authors: Sangar Yaman , Biradavolu Yoganand , Pederson Kai , Ranganathan Vaishnavi , Krishnaswamy Bhuvana
Published: January 2023

The food and drug industry is facing the need to monitor the quality and safety of their products. This has made them turn to low-cost solutions that can enable smart sensing and tracking without adding much overhead. One such popular low-power solution is backscatter-based sensing and communication system. While it offers the promise of battery-less tags, it does so at the cost of a reduced communication range. In this work, we propose PACT - a scalable communication system that leverages the knowledge asymmetry in the network to improve the communication range of the tags. Borrowing from the backscatter principles, we design custom PACT Tags that are battery-less but use an active radio to extend the communication range beyond standard passive tags. They operate using the energy harvested from the PACT Source. A wide-band Reader is used to receive multiple Tag responses concurrently and upload them to a cloud server, enabling real-time monitoring and tracking at a longer range. We identify and address the challenges in the practical design of battery-less PACT Tags using an active radio and prototype them using off-the-shelf components. We show experimentally that our Tag consumes only 23μJ energy, which is harvested from an excitation Source that is up to 24 meters away from the Tag. We show that in outdoor deployments, the responses from an estimated 520 Tags can be received by a Reader concurrently while being 400 meters away from the Tags.
Earmonitor
Authors: Sun Xue , Xiong Jie , Feng Chao , Deng Wenwen , Wei Xudong , Fang Dingyi , Chen Xiaojiang
Published: January 2023

Earphones are emerging as the most popular wearable devices and there has been a growing trend in bringing intelligence to earphones. Previous efforts include adding extra sensors (e.g., accelerometer and gyroscope) or peripheral hardware to make earphones smart. These methods are usually complex in design and also incur additional cost. In this paper, we present Earmonitor, a low-cost system that uses the in-ear earphones to achieve sensing purposes. The basic idea behind Earmonitor is that each person's ear canal varies in size and shape. We therefore can extract the unique features from the ear canal-reflected signals to depict the personalized differences in ear canal geometry. Furthermore, we discover that the signal variations are also affected by the fine-grained physiological activities. We can therefore further detect the subtle heartbeat from the ear canal reflections. Experiments show that Earmonitor can achieve up to 96.4% Balanced Accuracy (BAC) and low False Acceptance Rate (FAR) for user identification on a large-scale data of 120 subjects. For heartbeat monitoring, without any training, we propose signal processing schemes to achieve high sensing accuracy even in the most challenging scenarios when the target is walking or running.
ThumbAir
Authors: Gil Hyunjae , Oakley Ian
Published: January 2023

Typing while wearing a standalone Head Mounted Display (HMD)---systems without external input devices or sensors to support text entry---is hard. To address this issue, prior work has used external trackers to monitor finger movements to support in-air typing on virtual keyboards. While performance has been promising, current systems are practically infeasible: finger movements may be visually occluded from inside-out HMD based tracking systems or, otherwise, awkward and uncomfortable to perform. To address these issues, this paper explores an alternative approach. Taking inspiration from the prevalence of thumb-typing on mobile phones, we describe four studies exploring, defining and validating the performance of ThumbAir, an in-air thumb-typing system implemented on a commercial HMD. The first study explores viable target locations, ultimately recommending eight targets sites. The second study collects performance data for taps on pairs of these targets to both inform the design of a target selection procedure and also support a computational design process to select a keyboard layout. The final two studies validate the selected keyboard layout in word repetition and phrase entry tasks, ultimately achieving final WPMs of 27.1 and 13.73. Qualitative data captured in the final study indicate that the discreet movements required to operate ThumbAir, in comparison to the larger scale finger and hand motions used in a baseline design from prior work, lead to reduced levels of perceived exertion and physical demand and are rated as acceptable for use in a wider range of social situations.
Generalization and Personalization of Mobile Sensing-Based Mood Inference Models
Authors: Meegahapola Lakmal , Droz William , Kun Peter , de Götzen Amalia , Nutakki Chaitanya , Diwakar Shyam , Correa Salvador Ruiz , Song Donglei , Xu Hao , Bidoglia Miriam , Gaskell George , Chagnaa Altangerel , Ganbold Amarsanaa , Zundui Tsolmon , Caprini Carlo , Miorandi Daniele , Hume Alethia , Zarza Jose Luis , Cernuzzi Luca , Bison Ivano , Britez Marcelo Rodas , Busso Matteo , Chenu-Abente Ronald , Günel Can , Giunchiglia Fausto , Schelenz Laura , Gatica-Perez Daniel
Published: January 2023

Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. In contrast, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78--0.98 for two-class (negative vs. positive valence) and 0.76--0.94 for three-class (negative vs. neutral vs. positive valence) inference. Further, with the country-agnostic approach, we show that models do not perform well compared to country-specific settings, even when models are partially personalized. We also show that continent-specific models outperform multi-country models in the case of Europe. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical similarity of countries might impact mood inference.
Affective Touch as Immediate and Passive Wearable Intervention
Authors: Zhao Yiran , Tao Yujie , Le Grace , Maki Rui , Adams Alexander , Lopes Pedro , Choudhury Tanzeem
Published: January 2023

We investigated affective touch as a new pathway to passively mitigate in-the-moment anxiety. While existing mobile interventions offer great promises for health and well-being, they typically focus on achieving long-term effects such as shifting behaviors. As such, most mobile interventions are not applicable to provide immediate help in acute conditions -- when a user experiences a high anxiety level during ongoing events (e.g., completing high-stake tasks or mitigating interpersonal conflicts). A few works have developed passive interventions that are effective in-the-moment by leveraging breathing regulations and biofeedback. In this paper, we drew on neuroscientific findings on affective touch, the slow stroking on hairy skin that can elicit innate pleasantness and evaluated affective touch as a mobile health intervention. To induce affective touch, we first engineered a wearable device that renders a soft stroking sensation on the user's forearm. Then, we conducted a between-group experiment, in which participants underwent high-stress situations with/without receiving affective touch and post-experiment interviews, with 24 participants. Our results showed that participants who received affective touch experienced lower state anxiety and the same physiological stress response level compared to the control group participants. We also found that affective touch facilitated emotion regulation by rendering pleasantness, providing emotional support, and shifting attention. Finally, we discussed the immediate effect of affective touch on anxiety and physiological stress, the benefits of affective touch as a passive intervention, and the implementation considerations to use affective touch in just-in-time systems.
I Spy You
Authors: Zhang Shijia , Liu Yilin , Gowda Mahanth
Published: January 2023

This paper presents iSpyU, a system that shows the feasibility of recognition of natural speech content played on a phone during conference calls (Skype, Zoom, etc) using a fusion of motion sensors such as accelerometer and gyroscope. While microphones require permissions from the user to be accessible by an app developer, the motion sensors are zero-permission sensors, thus accessible by a developer without alerting the user. This allows a malicious app to potentially eavesdrop on sensitive speech content played by the user's phone. In designing the attack, iSpyU tackles a number of technical challenges including: (i) Low sampling rate of motion sensors (500 Hz in comparison to 44 kHz for a microphone). (ii) Lack of availability of large-scale training datasets to train models for Automatic Speech Recognition (ASR) with motion sensors. iSpyU systematically addresses these challenges by a combination of techniques in synthetic training data generation, ASR modeling, and domain adaptation. Extensive measurement studies on modern smartphones show a word level accuracy of 53.3 - 59.9% over a dictionary of 2000-10000 words, and a character level accuracy of 70.0 - 74.8%. We believe such levels of accuracy poses a significant threat when viewed from a privacy perspective.
Robust Federated Learning for Ubiquitous Computing through Mitigation of Edge-Case Backdoor Attacks
Authors: Elhattab Fatima , Bouchenak Sara , Talbi Rania , Nitu Vlad
Published: January 2023

Federated Learning (FL) allows several data owners to train a joint model without sharing their training data. Such a paradigm is useful for better privacy in many ubiquitous computing systems. However, FL is vulnerable to poisoning attacks, where malicious participants attempt to inject a backdoor task in the model at training time, along with the main task that the model was initially trained for. Recent works show that FL is particularly vulnerable to edge-case backdoors introduced by data points with unusual out-of-distribution features. Such attacks are among the most difficult to counter, and today's FL defense mechanisms usually fail to tackle them. In this paper, we present ARMOR, a defense mechanism that leverages adversarial learning to uncover edge-case backdoors. In contrast to most of existing FL defenses, ARMOR does not require real data samples and is compatible with secure aggregation, thus, providing better FL privacy protection. ARMOR relies on GANs (Generative Adversarial Networks) to extract data features from model updates, and uses the generated samples to test the activation of potential edge-case backdoors in the model. Our experimental evaluations with three widely used datasets and neural networks show that ARMOR can tackle edge-case backdoors with 95% resilience against attacks, and without hurting model quality.
Understanding the Mechanism of Through-Wall Wireless Sensing
Authors: Zhang Hualei , Wang Zhu , Sun Zhuo , Song Wenchao , Ren Zhihui , Yu Zhiwen , Guo Bin
Published: January 2023

During the last few years, there is a growing interest on the usage of Wi-Fi signals for human activity detection. A large number of Wi-Fi based sensing systems have been developed, including respiration detection, gesture classification, identity recognition, etc. However, the usability and robustness of such systems are still limited, due to the complexity of practical environments. Various pioneering approaches have been proposed to solve this problem, among which the model-based approach is attracting more and more attention, due to the advantage that it does not require a huge dataset for model training. Existing models are usually developed for Line-of-Sight (LoS) scenarios, and can not be applied to facilitating the design of wireless sensing systems in Non-Line-of-Sight (NLoS) scenarios (e.g., through-wall sensing). To fill this gap, we propose a through-wall wireless sensing model, aiming to characterize the propagation laws and sensing mechanisms of Wi-Fi signals in through-wall scenarios. Specifically, based on the insight that Wi-Fi signals will be refracted while there is a wall between the transceivers, we develop a refraction-aware Fresnel model, and prove theoretically that the original Fresnel model can be seen as a special case of the proposed model. We find that the presence of a wall will change the distribution of Fresnel zones, which we called the "squeeze effect" of Fresnel zones. Moreover, our theoretical analysis indicates that the "squeeze effect" can help improve the sensing capability (i.e., spatial resolution) of Wi-Fi signals. To validate the proposed model, we implement a through-wall respiration sensing system with a pair of transceivers. Extensive experiments in typical through-wall environments show that the respiration detection error is lower than 0.5 bpm, while the subject's vertical distance to the connection line of the transceivers is less than 200 cm. To the best of our knowledge, this is the first theoretical model that reveals the Wi-Fi based wireless sensing mechanism in through-wall scenarios.
MilliPCD
Authors: Cai Pingping , Sur Sanjib
Published: January 2023

3D Point Cloud Data (PCD) has been used in many research and commercial applications widely, such as autonomous driving, robotics, and VR/AR. But existing PCD generation systems based on RGB-D and LiDARs require robust lighting and an unobstructed field of view of the target scenes. So, they may not work properly under challenging environmental conditions. Recently, millimeter-wave (mmWave) based imaging systems have raised considerable interest due to their ability to work in dark environments. But the resolution and quality of the PCD from these mmWave imaging systems are very poor. To improve the quality of PCD, we design and implement MilliPCD, a "beyond traditional vision" PCD generation system for handheld mmWave devices, by integrating traditional signal processing with advanced deep learning based algorithms. We evaluate MilliPCD with real mmWave reflected signals collected from large, diverse indoor environments, and the results show improvements in the quality w.r.t. the existing algorithms, both quantitatively and qualitatively.
Side-lobe Can Know More
Authors: Yang Qian , Wu Hengxin , Huang Qianyi , Zhang Jin , Chen Hao , Li Weichao , Tao Xiaofeng , Zhang Qian
Published: January 2023

Thanks to the wide bandwidth, large antenna array, and short wavelength, millimeter wave (mmWave) has superior performance in both communication and sensing. Thus, the integration of sensing and communication is a developing trend for the mmWave band. However, the directional transmission characteristics of the mmWave limits the sensing scope to a narrow sector. Existing works coordinate sensing and communication in a time-division manner, which takes advantage of the sector level sweep during the beam training interval for sensing and the data transmission interval for communication. Beam training is a low frequency (e.g., 10Hz) and low duty-cycle event, which makes it hard to track fast movement or perform continuous sensing. Such time-division designs imply that we need to strike a balance between sensing and communication, and it is hard to get the best of both worlds. In this paper, we try to solve this dilemma by exploiting side lobes for sensing. We design Sidense, where the main lobe of the transmitter is directed towards the receiver, while in the meantime, the side lobes can sense the ongoing activities in the surrounding. In this way, sensing and downlink communication work simultaneously and will not compete for hardware and radio resources. In order to compensate for the low antenna gain of side lobes, Sidense performs integration to boost the quality of sensing signals. Due to the uneven side-lobe energy, Sidense also designs a target separation scheme to tackle the mutual interference in multi-target scenarios. We implement Sidense with Sivers mmWave module. Results show that Sidense can achieve millimeter motion tracking accuracy at 6m. We also demonstrate a multi-person respiration monitoring application. As Sidense does not modify the communication procedure or the beamforming strategy, the downlink communication performance will not be sacrificed due to concurrent sensing. We believe that more fascinating applications can be implemented on this concurrent sensing and communication platform.
CAFI-AR
Authors: Tang Xiao , Li Ruihui , Fu Chi-Wing
Published: January 2023

Freehand interaction enhances user experience, allowing one to use bare hands to manipulate virtual objects in AR. Yet, it remains challenging to accurately and efficiently detect contacts between real hand and virtual object, due to the imprecise captured/estimated hand geometry. This paper presents CAFI-AR, a new approach for Contact-Aware Freehand Interaction with virtual AR objects, enabling us to automatically detect hand-object contacts in real-time with low latency. Specifically, we formulate a compact deep architecture to efficiently learn to predict hand action and contact moment from sequences of captured RGB images relative to the 3D virtual object. To train the architecture for detecting contacts on AR objects, we build a new dataset with 4,008 frame sequences, each with annotated hand-object interaction information. Further, we integrate CAFI-AR into our prototyping AR system and develop various interactive scenarios, demonstrating fine-grained contact-aware interactions on a rich variety of virtual AR objects, which cannot be achieved by existing AR interaction approaches. Lastly, we also evaluate CAFI-AR, quantitatively and qualitatively, through two user studies to demonstrate its effectiveness in terms of accurately detecting the hand-object contacts and promoting fluid freehand interactions
Privacy-Enhancing Technology and Everyday Augmented Reality
Authors: O'Hagan Joseph , Saeghe Pejman , Gugenheimer Jan , Medeiros Daniel , Marky Karola , Khamis Mohamed , McGill Mark
Published: January 2023

Fundamental to Augmented Reality (AR) headsets is their capacity to visually and aurally sense the world around them, necessary to drive the positional tracking that makes rendering 3D spatial content possible. This requisite sensing also opens the door for more advanced AR-driven activities, such as augmented perception, volumetric capture and biometric identification - activities with the potential to expose bystanders to significant privacy risks. Existing Privacy-Enhancing Technologies (PETs) often safeguard against these risks at a low level e.g., instituting camera access controls. However, we argue that such PETs are incompatible with the need for always-on sensing given AR headsets' intended everyday use. Through an online survey (N=102), we examine bystanders' awareness of, and concerns regarding, potentially privacy infringing AR activities; the extent to which bystanders' consent should be sought; and the level of granularity of information necessary to provide awareness of AR activities to bystanders. Our findings suggest that PETs should take into account the AR activity type, and relationship to bystanders, selectively facilitating awareness and consent. In this way, we can ensure bystanders feel their privacy is respected by everyday AR headsets, and avoid unnecessary rejection of these powerful devices by society.
CamRadar
Authors: Liu Ziwei , Lin Feng , Wang Chao , Shen Yijie , Ba Zhongjie , Lu Li , Xu Wenyao , Ren Kui
Published: January 2023

Hidden cameras in sensitive locations have become an increasing threat to personal privacy all over the world. Because the camera is small and camouflaged, it is difficult to detect the presence of the camera with naked eyes. Existing works on this subject have either only covered using wireless transmission to detect cameras, or using other methods which are cumbersome in practical use. In this paper, we introduce a new direction that leverages the unintentional electromagnetic (EM) emanations of the camera to detect it. We first find that the digital output of the camera's image sensor will be amplitude-modulated to the EM emanations of the camera's clock. Thus, changes in the scope of the camera will directly cause changes in the camera's EM emanations, which constitutes a unique characteristic for a hidden camera. Based on this, we propose a novel camera detection system named CamRadar, which can filter out potential camera EM emanations from numerous EM signals quickly and achieve accurate hidden camera detection. Benefitting from the camera's EM emanations, CamRadar will not be limited by the camera transmission types or the detection angle. Our extensive real-world experiments using CamRadar and 19 hidden cameras show that CamRadar achieves a fast detection (in 16.75s) with a detection rate of 93.23% as well as a low false positive rate of 3.95%.
StructureSense
Authors: Huang Xincheng , Miller Keylonnie L. , Sample Alanson P. , Banovic Nikola
Published: January 2023

Recent advancements in object-tracking technologies can turn mundane constructive assemblies into Tangible User Interfaces (TUI) media. Users rely on instructions or their own creativity to build both permanent and temporary structures out of such objects. However, most existing object-tracking technologies focus on tracking structures as monoliths, making it impossible to infer and track the user's assembly process and the resulting structures. Technologies that can track the assembly process often rely on specially fabricated assemblies, limiting the types of objects and structures they can track. Here, we present StructureSense, a tracking system based on passive UHF-RFID sensing that infers constructive assembly structures from object motion. We illustrated StructureSense in two use cases (as guided instructions and authoring tool) on two different constructive sets (wooden lamp and Jumbo Blocks), and evaluated system performance and usability. Our results showed the feasibility of using StructureSense to track mundane constructive assembly structures.
Phone-based Ambient Temperature Measurement with a New Confidence-based Truth Inference Model
Authors: Chen Dayin , Shi Xiaodan , Song Xuan , Chen Zhiheng , Zhang Haoran
Published: January 2023

Monitoring the indoor temperature can help saving of energy and improve the comfort level. Smartphone, as a ubiquitous device, can be an additional data source to provide the ambient temperature estimation. However, the estimation results sometimes can be unreliable due to the different phone using states. How to integrate multiple estimation results in one area to get a more accurate prediction result is still a challenge. In this work, we proposed one phone-based ambient temperature measurement system which contains two models. The first temperature prediction model takes easily accessible phone state features as inputs and outputs ambient temperature prediction with a confidence value. The second truth inference model takes multiple prediction results with confidences as inputs and outputs a referred final prediction result. Our temperature prediction model reaches 0.253°C with MAE in our testing set. We also proved by transfer learning our model can be used in other new type of phones. We evaluate the truth inference model in our testing dataset and it reaches 0.128°C, which outperforms the state-of-the-art truth inference algorithms. We believe this work can contribute to energy conservation and provide new ideas for crowdsourcing.
Not Only for Contact Tracing
Authors: Ayalon Oshrat , Li Sophie , Preneel Bart , Redmiles Elissa M.
Published: January 2023

Many countries developed and deployed contact tracing apps to reduce the spread of the COVID-19 coronavirus. Prior research explored people's intent to install these apps, which is necessary to ensure effectiveness. However, adopting contact tracing apps is not enough on its own, and much less is known about how people actually use these apps. Exploring app use can help us identify additional failures or risk points in the app life cycle. In this study, we conducted 13 semi-structured interviews with young adult users of Belgium's contact-tracing app, Coronalert. The interviews were conducted approximately a year after the onset of the COVID-19 pandemic. Our findings offer potential design directions for addressing issues identified in prior work - such as methods for maintaining long-term use and better integrating with the local health systems - and offer insight into existing design tensions such as the trade-off between maintaining users' privacy (by minimizing the personal data collected) and users' desire to have more information about an exposure incident. We distill from our results and the results of prior work a framework of people's decision points in contact-tracing app use that can serve to motivate careful design of future contact tracing technology.
Step-Indexed Logical Relations for Countable Nondeterminism and Probabilistic Choice
Authors: Aguirre Alejandro , Birkedal Lars
Published: January 2023

Developing denotational models for higher-order languages that combine
probabilistic and nondeterministic choice is known to be very
challenging. In this paper, we propose an alternative approach based
on operational techniques. We study a higher-order language combining
parametric polymorphism, recursive types, discrete probabilistic choice
and countable nondeterminism. We define probabilistic generalizations
of may- and must-termination as the optimal and pessimal probabilities
of termination. Then we define step-indexed logical relations and show
that they are sound and complete with respect to the induced contextual
preorders. For may-equivalence we use step-indexing over the natural
numbers whereas for must-equivalence we index over the countable
ordinals. We then show than the probabilities of may- and
must-termination coincide with the maximal and minimal probabilities of
termination under all schedulers. Finally we derive the equational
theory induced by contextual equivalence and show that it validates the
distributive combination of the algebraic theories for probabilistic
and nondeterministic choice.
Comparative Synthesis: Learning Near-Optimal Network Designs by Query
Authors: Wang Yanjun , Li Zixuan , Jiang Chuan , Qiu Xiaokang , Rao Sanjay
Published: January 2023

When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing critical traffic. The state of practice poses challenges since architects must precisely encode their intent into formal optimization models using abstract notions such as utility functions, and ad-hoc manually tuned knobs. In this paper, we present the first effort to synthesize optimal network designs with indeterminate objectives using an interactive program-synthesis-based approach. We make three contributions. First, we present comparative synthesis, an interactive synthesis framework which produces near-optimal programs (network designs) through two kinds of queries (Validate and Compare), without an objective explicitly given. Second, we develop the first learning algorithm for comparative synthesis in which a voting-guided learner picks the most informative query in each iteration. We present theoretical analysis of the convergence rate of the algorithm. Third, we implemented Net10Q, a system based on our approach, and demonstrate its effectiveness on four real-world network case studies using black-box oracles and simulation experiments, as well as a pilot user study comprising network researchers and practitioners. Both theoretical and experimental results show the promise of our approach.
On the Expressive Power of String Constraints
Authors: Day Joel D. , Ganesh Vijay , Grewal Nathan , Manea Florin
Published: January 2023

We investigate properties of strings which are expressible by canonical types of string constraints. Specifically, we consider a landscape of 20 logical theories, whose syntax is built around combinations of four common elements of string constraints: language membership (e.g. for regular languages), concatenation, equality between string terms, and equality between string-lengths. For a variable x and formula f from a given theory, we consider the set of values for which x may be substituted as part of a satisfying assignment, or in other words, the property f expresses through x. Since we consider string-based logics, this set is a formal language. We firstly consider the relative expressive power of different combinations of string constraints by comparing the classes of languages expressible in the corresponding theories, and are able to establish a mostly complete picture in this regard. Secondly, we consider the question of deciding whether the language or property expressed by a variable/formula in one theory can be expressed in another theory. We establish several negative results which are relevant to preprocessing and normalisation of string constraints in practice. Some of our results have strong connections to important open problems regarding word equations and the theory of string solving.
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
Authors: Lee Wonyeol , Rival Xavier , Yang Hongseok
Published: January 2023

We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pathwise gradient estimator, one of the most popular methods for posterior inference and model learning. Our improvement increases the scope of the estimator from differentiable models to non-differentiable ones without requiring manual intervention of the user; the improved estimator automatically identifies differentiable parts of a given probabilistic program using our static analysis, and applies the pathwise gradient estimator to the identified parts while using a more general but less efficient estimator, called score estimator, for the rest of the program. Our analysis has a surprisingly subtle soundness argument, partly due to the misbehaviours of some target smoothness properties when viewed from the perspective of program analysis designers. For instance, some smoothness properties, such as partial differentiability and partial continuity, are not preserved by function composition, and this makes it difficult to analyse sequential composition soundly without heavily sacrificing precision. We formulate five assumptions on a target smoothness property, prove the soundness of our analysis under those assumptions, and show that our leading examples satisfy these assumptions. We also show that by using information from our analysis instantiated for differentiability, our improved gradient estimator satisfies an important differentiability requirement and thus computes the correct estimate on average (i.e., returns an unbiased estimate) under a regularity condition. Our experiments with representative probabilistic programs in the Pyro language show that our static analysis is capable of identifying smooth parts of those programs accurately, and making our improved pathwise gradient estimator exploit all the opportunities for high performance in those programs.
An Algebra of Alignment for Relational Verification
Authors: Antonopoulos Timos , Koskinen Eric , Le Ton Chanh , Nagasamudram Ramana , Naumann David A. , Ngo Minh
Published: January 2023

Relational verification encompasses information flow security, regression verification, translation validation for compilers, and more. Effective alignment of the programs and computations to be related facilitates use of simpler relational invariants and relational procedure specs, which in turn enables automation and modular reasoning. Alignment has been explored in terms of trace pairs, deductive rules of relational Hoare logics (RHL), and several forms of product automata. This article shows how a simple extension of Kleene Algebra with Tests (KAT), called BiKAT, subsumes prior formulations, including alignment witnesses for forall-exists properties, which brings to light new RHL-style rules for such properties. Alignments can be discovered algorithmically or devised manually but, in either case, their adequacy with respect to the original programs must be proved; an explicit algebra enables constructive proof by equational reasoning. Furthermore our approach inherits algorithmic benefits from existing KAT-based techniques and tools, which are applicable to a range of semantic models.
DimSum: A Decentralized Approach to Multi-language Semantics and Verification
Authors: Sammler Michael , Spies Simon , Song Youngju , D'Osualdo Emanuele , Krebbers Robbert , Garg Deepak , Dreyer Derek
Published: January 2023

Prior work on multi-language program verification has achieved impressive results, including the compositional verification of complex compilers. But the existing approaches to this problem impose a variety of restrictions on the overall structure of multi-language programs (e.g. fixing the source language, fixing the set of involved languages, fixing the memory model, or fixing the semantics of interoperation). In this paper, we explore the problem of how to avoid such global restrictions.

Concretely, we present
DimSum
: a new,
decentralized
approach to multi-language semantics and verification, which we have implemented in the Coq proof assistant. Decentralization means that we can define and reason about languages independently from each other (as independent
modules
communicating via events), but also combine and translate between them when necessary (via a library of combinators).

We apply DimSum to a high-level imperative language Rec (with an abstract memory model and function calls), a low-level assembly language Asm (with a concrete memory model, arbitrary jumps, and syscalls), and a mathematical specification language Spec. We evaluate DimSum on two case studies: an Asm library extending Rec with support for pointer comparison, and a coroutine library for Rec written in Asm. In both cases, we show how DimSum allows the Asm libraries to be abstracted to Rec-level specifications, despite the behavior of the Asm libraries not being syntactically expressible in Rec itself. We also verify an optimizing multi-pass compiler from Rec to Asm, showing that it is compatible with these Asm libraries.
A General Noninterference Policy for Polynomial Time
Authors: Hainry Emmanuel , Péchoux Romain
Published: January 2023

We introduce a new noninterference policy to capture the class of functions computable in polynomial time on an object-oriented programming language. This policy makes a clear separation between the standard noninterference techniques for the control flow and the layering properties required to ensure that each “security” level preserves polynomial time soundness, and is thus very powerful as for the class of programs it can capture. This new characterization is a proper extension of existing tractable characterizations of polynomial time based on safe recursion. Despite the fact that this noninterference policy is Π
1
0
-complete, we show that it can be instantiated to some decidable and conservative instance using shape analysis techniques.
Admissible Types-to-PERs Relativization in Higher-Order Logic
Authors: Popescu Andrei , Traytel Dmitriy
Published: January 2023

Relativizing statements in Higher-Order Logic (HOL) from types to sets is useful for improving productivity when working with HOL-based interactive theorem provers such as HOL4, HOL Light and Isabelle/HOL. This paper provides the first comprehensive definition and study of types-to-sets relativization in HOL, done in the more general form of types-to-PERs (partial equivalence relations). We prove that, for a large practical fragment of HOL which includes container types such as datatypes and codatatypes, types-to-PERs relativization is admissible, in that the provability of the original, type-based statement implies the provability of its relativized, PER-based counterpart. Our results also imply the admissibility of a previously proposed axiomatic extension of HOL with local type definitions. We have implemented types-to-PERs relativization as an Isabelle tool that performs relativization of HOL theorems on demand.
Type-Preserving, Dependence-Aware Guide Generation for Sound, Effective Amortized Probabilistic Inference
Authors: Li Jianlin , Ven Leni , Shi Pengyuan , Zhang Yizhou
Published: January 2023

In probabilistic programming languages (PPLs), a critical step in optimization-based inference methods is constructing, for a given model program, a trainable guide program.
Soundness and effectiveness of inference rely on constructing good guides, but the expressive power of a universal PPL poses challenges.
This paper introduces an approach to automatically generating guides for deep amortized inference in a universal PPL.
Guides are generated using a type-directed translation per a novel behavioral type system.
Guide generation extracts and exploits independence structures using a syntactic approach to conditional independence, with a semantic account left to further work.
Despite the control-flow expressiveness allowed by the universal PPL, generated guides are guaranteed to satisfy a critical soundness condition and moreover, consistently improve training and inference over state-of-the-art baselines for a suite of benchmarks.
Choice Trees: Representing Nondeterministic, Recursive, and Impure Programs in Coq
Authors: Chappe Nicolas , He Paul , Henrio Ludovic , Zakowski Yannick , Zdancewic Steve
Published: January 2023

This paper introduces ctrees, a monad for modeling nondeterministic, recursive, and impure programs in Coq. Inspired by Xia et al.'s itrees, this novel data structure embeds computations into coinductive trees with three kind of nodes: external events, and two variants of nondeterministic branching. This apparent redundancy allows us to provide shallow embedding of denotational models with internal choice in the style of CCS, while recovering an inductive LTS view of the computation. ctrees inherit a vast collection of bisimulation and refinement tools, with respect to which we establish a rich equational theory.

We connect ctrees to the itree infrastructure by showing how a monad morphism embedding the former into the latter permits to use ctrees to implement nondeterministic effects. We demonstrate the utility of ctrees by using them to model concurrency semantics in two case studies: CCS and cooperative multithreading.
Elements of Quantitative Rewriting
Authors: Gavazzo Francesco , Di Florio Cecilia
Published: January 2023

We introduce a general theory of quantitative and metric rewriting systems, namely systems with a rewriting
relation enriched over quantales modelling abstract quantities. We develop theories of abstract and term-based
systems, refining cornerstone results of rewriting theory (such as Newman’s Lemma, Church-Rosser Theorem,
and critical pair-like lemmas) to a metric and quantitative setting. To avoid distance trivialisation and lack of
confluence issues, we introduce non-expansive, linear term rewriting systems, and then generalise the latter
to the novel class of graded term rewriting systems. These systems make quantitative rewriting modal and
context-sensitive, this way endowing rewriting with coeffectful behaviours.