Miracle-dz/UniFAR-FTUs huggingface dataset:
Poster Abstract: A Radar Based User Discrimination System for Medication Adherence Monitoring

Medication non-adherence is a major healthcare challenge globally, with over half of patients with chronic conditions in developed countries failing to follow their prescribed medication regimen. This can lead to poor disease outcomes, increased hospital visits, and a significant financial burden on healthcare systems [1]. These issues have driven a recent wave of research, including the development of smart adherence products [6] that can be incorporated into a patient’s daily life to monitor medication adherence. In this work, we present a radar-based system for user identification while taking medication, which extends our recent work [5]. we conducted preliminary experiments examining semi-medication-taking activities executed by 6 subjects. Our system achieved 80% accuracy in identifying who has taken the medication in a group of 3 subjects.
Demo Abstract: Leveraging Side-Channels to Turn Processors into Low Overhead Radios

Traditionally, side channels have been exploited to uncover sensitive information such as cryptographic keys from computing devices. An attacker can extract private information from a device’s processor and memory by using electromagnetic (EM) radiation while code and data are being executed. Rather than seeing side-channels and electromagnetic radiation as vulnerabilities, we consider them as potential wireless communication channels for resource-constrained devices. The main advantage of this approach is completely eliminating the need for a conventional radio and antenna, which offers energy savings, simplicity, and flexibility for resource-constrained devices. This demo is based on our upcoming IPSN’23 paper which presents the details of this radio-less side-channel-based communication framework. In this demo abstract, we briefly explain the concept, its implementation, advantages, and limitations, and then describe our setup and plan for the live demo for this framework. Our demo shows how a radio-less MCU (Arduino UNO) could communicate with a nearby receiver (a cheap UHF TV antenna connected to a software-defined radio) by generating side-channel EM radiations while running specialized software. Our demo confirms the feasibility of this method in a realistic setting and shows how our framework can enable low overhead connectivity for resource-constrained devices.
Unikernel Linux (UKL)

This paper presents Unikernel Linux (UKL), a path toward integrating unikernel optimization techniques in Linux, a general purpose operating system. UKL adds a configuration option to Linux allowing for a single, optimized process to link with the kernel directly, and run at supervisor privilege. This UKL process does not require application source code modification, only a re-link with our, slightly modified, Linux kernel and glibc. Unmodified applications show modest performance gains out of the box, and developers can further optimize applications for more significant gains (e.g. 26% throughput improvement for Redis). UKL retains support for co-running multiple user level processes capable of communicating with the UKL process using standard IPC. UKL preserves Linux's battle-tested codebase, community, and ecosystem of tools, applications, and hardware support. UKL runs both on bare-metal and virtual servers and supports multi-core execution. The changes to the Linux kernel are modest (1250 LOC).
The Impact of Pair Programming Based on Project-Based Learning on Computational Thinking of Junior High School Students

Computational thinking as a key in K-12 education has gotten more and more attention. Researchers and teachers are focusing more on tools and teaching methods that could edify computational thinking. Nowadays, developing students’ computational thinking through graphical programming has become a norm. In addition, project-based learning has been proven effective in cultivating students’ computational thinking. The purpose of this study is to find out the effectiveness of the paired programming teaching method, which is based on project-based learning in junior high school students’ computational thinking, and to provide new opportunities and conditions for the development of computational thinking in junior high school students. This research could expand the view of starting project-based graphical programming courses, and provide a reference for the designing, developing, and teaching of junior high school computational thinking courses.
Analysis of Students’ Learning Activities Based on the ICAP Framework

To improve students’ academic performance, it is important to explore the frequency and differences of students’ learning activities in Science and Chinese classes. Based on the ICAP framework, this study designed learning activities coding scheme to analyze students’ learning activities in two classes video recordings (science and Chinese classes). Then χ2-test and social network analysis are conducted. The results revealed that the frequency of learning activities is: active > constructive > passive > instructive. The number of learning activities in Science class is more than that of Chinese class. Students tend to operate, summarize, discuss and communicate actively, but they are less likely to elaborate and defend for their own views. More importantly, Students’ learning activities have significant difference in Science and Chinese classes. We should pay more attention to design different learning activities in different classes.
Nephele: Extending Virtualization Environments for Cloning Unikernel-based VMs

Unikernels gained an increasing interest in the recent years because they provide efficient resource allocation and high performance for cloud services by bundling the application with a minimal set of OS services in a guest VM. Although a unikernel is by design small and lightweight, fleets of unikernels based on the same image are not necessarily more efficient than containers because the latter can rely upon OS primitives for sharing memory. Futhermore, porting POSIX applications on top of unikernels brings a new challenge: what does fork() mean in the world of unikernels where there is memory isolation within a VM? Lacking fork() support significantly reduces the applicability of unikernels in popular cloud applications. In this paper we address these shortcomings and show that cloning unikernels makes way to further improvements and enables full functionality of popular cloud applications, such as NGINX and Redis. Our solution, Nephele, extends the Xen virtualization platform and provides autoscaling capabilities to unikernel based VMs. Nephele provides 8x faster instantiation times and can run 3x more active unikernel VMs on the same hardware compared to booting separate unikernels.
Link Quality Modeling for LoRa Networks in Orchards

LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential for efficiently deploying a LoRa network in orchards, e.g., determining gateway coverage and sensor placement. Although some propagation models have been studied for LoRa networks, they are not suitable for orchard environments, because they do not consider the shadowing effect on wireless propagation caused by the ground and tree canopies. This paper presents FLog, a propagation model for LoRa signals in orchard environments. FLog leverages a unique feature of orchards, i.e., all trees have similar shapes and are planted regularly in space. We develop a 3D model of the orchards. Once we have the location of a sensor and a gateway, we know the mediums that the wireless signal traverse. Based on this knowledge, we generate the First Fresnel Zone (FFZ) between the sender and the receiver. The intrinsic path loss exponents (PLE) of all mediums can be combined into a classic Log-Normal Shadowing model in the FFZ. Extensive experiments in almond orchards show that FLog reduces the link quality estimation error by 42.7% and improves gateway coverage estimation accuracy by 70.3%, compared with a widely-used propagation model.
OFence: Pairing Barriers to Find Concurrency Bugs in the Linux Kernel

Knowing which functions may execute concurrently is key to finding concurrency-related bugs. Existing tools infer the possibility of concurrency using dynamic analysis or by pairing functions that use the same locks. Code that relies on more relaxed concurrency controls is, by and large, out of the reach of existing concurrency-related bug-tracking tools. In this paper, we propose a new heuristic to automatically infer the possibility of concurrency in lockless code that relies on memory barriers (memory fences) for correctness, a task made complex by the fact that barriers do not have a unique identifier and do not have a clearly delimited scope. To infer the possibility of concurrency between barriers, we propose a novel heuristic based on matching objects accessed before and after barriers. Our approach is based on the observation that barriers work in pairs: if a write memory barrier orders writes to a set of objects, then there should be a read barrier that orders reads to the same set of objects. This pairing strategy allows us to infer which barriers are meant to run concurrently and, in turn, check the code surrounding the barriers for concurrency-related bugs. As a example of a type of concurrency bug, we focus on bugs related to the incorrect placement of reads or writes relative to barriers. When we detect incorrect read or write placements in the code, we automatically produce a patch to fix them. We evaluate our heuristic on the Linux kernel. Our analysis runs in 8 minutes. We fixed 12 incorrect ordering constraints that could have resulted in hard-to-debug data corruption or kernel crashes. The patches have been merged in the mainline kernel. None of the bugs could have been found using existing static analysis heuristics.
Network On or Off? Instant Global Binary Decisions over UWB with Flick

In many low-power wireless systems, a condition occurring at some nodes (e.g., an anomalous sensor sample, an aperiodic packet to transmit, a new joining node) determines whether the entire network should be awake (e.g., to react to the anomaly, deliver the packet, update the node group) or enter sleep. State-of-the-art protocols exploit periodic network-wide floods based on concurrent transmissions (e.g., via Glossy) to establish the global decision quickly, reliably, and efficiently. Still, time is of the essence: the faster the network agrees, the faster it either reacts or enters sleep. Flick achieves this global decision with order-of-magnitude latency improvements and 5-nines reliability by detecting and disseminating a binary on/off vote via the preamble of a packet instead of its full content. The actual realization of this simple idea entails several techniques on the ultra-wideband (UWB) radios we use. We evaluate Flick over a 78-node network in the Cloves testbed showing that, e.g., a single node can globally flick the switch to on across a 10-hop diameter in < 500&#U+03BC;s , i.e., roughly the time for a Glossy packet to go across a single hop, and with 4.4 × less energy. We demonstrate the potential of Flick by integrating it into staple protocols and evaluating the performance improvements it enables.
DyTIS: A Dynamic Dataset Targeted Index Structure Simultaneously Efficient for Search, Insert, and Scan

Many datasets in real life are complex and dynamic, that is, their key densities are varied over the whole key space and their key distributions change over time. It is challenging for an index structure to efficiently support all key operations for data management, in particular, search, insert, and scan, for such dynamic datasets. In this paper, we present DyTIS (Dynamic dataset Targeted Index Structure), an index that targets dynamic datasets. DyTIS, though based on the structure of Extendible hashing, leverages the CDF of the key distribution of a dataset, and learns and adjusts its structure as the dataset grows. The key novelty behind DyTIS is to group keys by the natural key order and maintain keys in sorted order in each bucket to support scan operations within a hash index. We also define what we refer to as a dynamic dataset and propose a means to quantify its dynamic characteristics. Our experimental results show that DyTIS provides higher performance than the state-of-the-art learned index for the dynamic datasets considered.
PhD Forum Abstract: Integrating Prior Knowledge and Machine Learning Techniques for Efficient AIoT Sensing

Recent advances in machine learning have inspired the development of deep neural network (DNN)-based smart sensing applications for the Artificial Internet of Things (AIoT). However, the effectiveness of DNNs relies on the availability of large, labeled data to uncover useful feature representations. The widespread use of DNN models in computer vision (CV), natural language processing (NLP), and voice sensing can be attributed to the massively available labeled training datasets. Despite the abundance of IoT sensing data, the human-uninterpretable property of AIoT data makes it difficult to construct labeled datasets for DNN model training. Additionally, variations in sensor hardware or DNN models’ deployment environments introduce domain shifts, making generalized machine learning algorithms even more difficult to develop. The scarcity of labeled training data and run-time domain shifts are two main challenges in developing effective machine learning algorithms for AIoT sensing. The goal of my research is to address the above challenges for AIoT sensing applications. Two main research methodologies are involved. The first is to leverage the latest state-of-the-art machine learning techniques to develop effective models for smart sensing. The second approach involves integrating known prior knowledge into machine learning algorithms to develop more accurate and reliable DNN models for AIoT sensing applications.
Construction automatique et continue de prévisions

Résumé. — La procédure de Box et Jenkins est une des meilleures méthodes de construction de prévisions pour le court terme. Elle présente toutefois un inconvénient important : elle n'est pas automatique. Cet article décrit le programme CAPRI qui construit automatiquement et rapidement un modèle, génère des prévisions précises et met à jour le modèle si de nouvelles données sont disponibles. Mots clés : Automatisation; Box et Jenkins; Prévisions. Abstract. — The Box-Jenkins procedure is one of the best methods for building short-term forecasts. However, it has an important disadvantage: it isn't automatic. This paper describes the program CAPRI which constructs automatically and quickly one model, générâtes accurate forecasts and updates the model ifnew data are available.
Trustworthiness Management Based on Blockchain in the Internet of Things

In the smart Internet of Things (IoT), if the identity of the message source is not evaluated, malicious nodes will attack other nodes by publishing malicious information or requests. This paper proposes a trust management mechanism for IoT devices based on blockchain. The information of each node is stored through the blockchain, and the Internet of Things equipment receiving communication messages grants different permissions by querying the reputation level of relevant nodes through the blockchain. The reputation level of nodes is calculated by historical behavior. Due to the limited storage capacity of some IoT devices, this paper proposes a special node, namely the cluster head, for storage and trust management. At the same time, the security of the proposed trust mechanism is evaluated, and the results show that the management mechanism can protect the secure communication of IoT devices more effective. CCS CONCEPTS • Security and privacy • Security services • Access control
Coping strategies among middle-aged and older cancer survivors in Japan: A qualitative study

We investigated the coping strategies used in everyday life by middle-aged and older cancer survivors in Japan. Sixteen Japanese volunteers participated in one-to-one semi-structured interviews. The interviews focused on the respondents’ descriptions of how they felt and what they did after having been diagnosed with cancer. Although having been diagnosed with cancer, many survivors reported having made identifiable positive adjustments in lifestyle and ways of thinking, such as turning to family and friends for psychosocial support, as well as visiting shrines to find spiritual peace. The implications of these findings are discussed in relation to social work practice.
Book Review: Evidence–based Initiatives for Organizational Change and Development

Evidence-Based Initiatives for Organizational Change and Development provides an insightful toolbox of case studies for academicians and practitioners alike. Compiling case studies elucidating the application of the foundational tenets of Organizational Change and Development (OCD), the authors provide a perspective related to implementation within different organizations and markets. The authors, Robert G. Hamlin, Andrea D. Ellinger, and Jenni Jones, are recognized and accomplished researchers in OCD, contributing to peer-reviewed academic journals regarding OCD and afﬁliated topics. Furthermore, each author possesses real-world practitioner experience in various industries, instrumental knowledge when combining theoretical academic perspectives and evidence-based research.
Rediversification Following Ecotype Isolation Reveals Hidden Adaptive Potential

Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about if communities can regenerate ecological diversity following species removal or extinction, and how the rediversified communities would compare to the original ones. Here we show that simple two-ecotype communities from the E. coli Long Term Evolution Experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different compared to the original community in ways relevant to the mechanism of ecotype coexistence, for example in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, but with unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.
A multiscale theory for spreading and migration of adhesion-reinforced mesenchymal cells

We present a chemomechanical whole-cell theory for the spreading and migration dynamics of mesenchymal cells that can actively reinforce their adhesion to an underlying viscoelastic substrate as a function of its stiffness. Our multiscale model couples the adhesion reinforcement effect at the subcellular scale with the nonlinear mechanics of the nucleus-cytoskeletal network complex at the cellular scale to explain the concurrent monotonic area-stiffness and non-monotonic speed-stiffness relationships observed in experiments: We consider that large cell spreading on stiff substrates flattens the nucleus, increasing the viscous drag force on it. The resulting force balance dictates a reduction in the migration speed on stiff substrates. We also reproduce the experimental influence of the substrate viscosity on the cell spreading area and migration speed by elucidating how the viscosity may either maintain adhesion reinforcement or prevent it depending on the substrate stiffness. Additionally, our model captures the experimental directed migration behavior of the adhesion-reinforced cells along a stiffness gradient, known as durotaxis, as well as up or down a viscosity gradient (viscotaxis or anti-viscotaxis), the cell moving towards an optimal viscosity in either case. Overall, our theory explains the intertwined mechanics of the cell spreading, migration speed and direction in the presence of the molecular adhesion reinforcement mechanism.
Dynamics of a Lipid Vesicle across a Microfluidic Constriction: How does the Fluidity of the Encapsulation and its Micro-Environment Matter?

Constriction in the flow passage in the physiological circulatory system is central to the occurrence of several diseased conditions such as thrombosis and is also pivotal towards the understanding of several regulatory processes in the human microvasculature. It is, therefore, imperative to advance a mechanistic insight on the dynamics of the transiting cellular encapsulations in a physiologically-mimicking micro-confinement, with particular focus on deciphering the role of its mechano-physical properties. Here we bring out a quantitative depiction on the role of the membrane fluidity and the initial deflation (shape deviation from sphericity) of a lipid vesicle during its morphological transition from stretching to tumbling via rolling as it migrates across a microfluidic constriction. Based on our experimental observations as well as theoretical insights, we construct a regime map to elucidate the range of the key dimensionless parameters orchestrating the dynamic transition. Our results further bring out the role of the initial position of the lipid vesicle on its subsequent stretching dynamics, exhibiting characteristic nonlinearities and non-monotonic trends. In addition, our observations on the vesicle’s stretching dynamics emerge from mapping selectively with the viscosity contrast between the encapsulated and the suspending fluid medium, offering potential physiologically relevant cues on the impact of the aging of a cellular moiety on its deformability as it transits through a constricted path. Such mechanistic insights may potentially enable establishing quantitative correlations between the dynamical transition of a cellular encapsulation and its mechano-physical properties, which may in turn, have decisive implications in various states of health and disease while circulating across microvascular fluidic pathways. Impact Statement This study brings out a quantitative mechanistic insight into the dynamics of migrating lipid vesicles as they migrate through a constricted microfluidic passage. Having a direct similitude with the movement of red blood cells in human microvascular pathways, the resulting mapping between the initial shape and bending properties of the vesicle membrane with three distinct morphological transitions (stretching, rolling, and tumbling) provides cues for understanding the healthy and diseased states of the cells based on their morpho-dynamic features and establish exclusive connectivity of the same with the cell membrane as well as the cytoplasm properties, a paradigm that is currently non-existant. This, in turn, may lead to a novel mechanistic approach of label-free disease detection based on cellular imaging, for which the current understanding is mostly empirical rather than fundamental.