289 research outputs found
Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing
Soft robots offer significant advantages in adaptability, safety, and
dexterity compared to conventional rigid-body robots. However, it is
challenging to equip soft robots with accurate proprioception and tactile
sensing due to their high flexibility and elasticity. In this work, we describe
the development of a vision-based proprioceptive and tactile sensor for soft
robots called GelFlex, which is inspired by previous GelSight sensing
techniques. More specifically, we develop a novel exoskeleton-covered soft
finger with embedded cameras and deep learning methods that enable
high-resolution proprioceptive sensing and rich tactile sensing. To do so, we
design features along the axial direction of the finger, which enable
high-resolution proprioceptive sensing, and incorporate a reflective ink
coating on the surface of the finger to enable rich tactile sensing. We design
a highly underactuated exoskeleton with a tendon-driven mechanism to actuate
the finger. Finally, we assemble 2 of the fingers together to form a robotic
gripper and successfully perform a bar stock classification task, which
requires both shape and tactile information. We train neural networks for
proprioception and shape (box versus cylinder) classification using data from
the embedded sensors. The proprioception CNN had over 99\% accuracy on our
testing set (all six joint angles were within 1 degree of error) and had an
average accumulative distance error of 0.77 mm during live testing, which is
better than human finger proprioception. These proposed techniques offer soft
robots the high-level ability to simultaneously perceive their proprioceptive
state and peripheral environment, providing potential solutions for soft robots
to solve everyday manipulation tasks. We believe the methods developed in this
work can be widely applied to different designs and applications.Comment: Accepted to ICRA202
Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture
In this paper, we propose a highly parameter-efficient approach to scaling
pre-trained language models (PLMs) to a deeper model depth. Unlike prior work
that shares all parameters or uses extra blocks, we design a more capable
parameter-sharing architecture based on matrix product operator (MPO). MPO
decomposition can reorganize and factorize the information of a parameter
matrix into two parts: the major part that contains the major information
(central tensor) and the supplementary part that only has a small proportion of
parameters (auxiliary tensors). Based on such a decomposition, our architecture
shares the central tensor across all layers for reducing the model size and
meanwhile keeps layer-specific auxiliary tensors (also using adapters) for
enhancing the adaptation flexibility. To improve the model training, we further
propose a stable initialization algorithm tailored for the MPO-based
architecture. Extensive experiments have demonstrated the effectiveness of our
proposed model in reducing the model size and achieving highly competitive
performance.Comment: 14 pages, 4 figures, 6 table
Variation of Korotkoff stethoscope sounds during blood pressure measurement: Analysis using a convolutional neural network
Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heart beats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as âKorotkoffâ. A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10-fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analysed separately, allowing consistency to be evaluated between groups. Next, within-subject variation of the Korotkoff sounds analysed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analysed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P < 0.001). Results reached stability for SBP (97.8%, at 6th beats below SBP) and DBP (98.1%, at 6th beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at high cuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P < 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject
Static Semantics Reconstruction for Enhancing JavaScript-WebAssembly Multilingual Malware Detection
The emergence of WebAssembly allows attackers to hide the malicious
functionalities of JavaScript malware in cross-language interoperations, termed
JavaScript-WebAssembly multilingual malware (JWMM). However, existing
anti-virus solutions based on static program analysis are still limited to
monolingual code. As a result, their detection effectiveness decreases
significantly against JWMM. The detection of JWMM is challenging due to the
complex interoperations and semantic diversity between JavaScript and
WebAssembly. To bridge this gap, we present JWBinder, the first technique aimed
at enhancing the static detection of JWMM. JWBinder performs a
language-specific data-flow analysis to capture the cross-language
interoperations and then characterizes the functionalities of JWMM through a
unified high-level structure called Inter-language Program Dependency Graph.
The extensive evaluation on one of the most representative real-world
anti-virus platforms, VirusTotal, shows that \system effectively enhances
anti-virus systems from various vendors and increases the overall successful
detection rate against JWMM from 49.1\% to 86.2\%. Additionally, we assess the
side effects and runtime overhead of JWBinder, corroborating its practical
viability in real-world applications.Comment: Accepted to ESORICS 202
Controllable nonlinear propagation of partially incoherent Airy beams
The self-accelerating beams such as the Airy beam show great potentials in
many applications including optical manipulation, imaging and communication.
However, their superior features during linear propagation could be easily
corrupted by optical nonlinearity or spatial incoherence individually. Here we
investigate how the interaction of spatial incoherence and nonlinear
propagation affect the beam quality of Airy beam, and find that the two
destroying factors can in fact balance each other. Our results show that the
influence of coherence and nonlinearity on the propagation of PIABs can be
formulated as two exponential functions that have factors of opposite signs.
With appropriate spatial coherence length, the PIABs not only resist the
corruption of beam profile caused by self-focusing nonlinearity, but also
exhibits less anomalous diffraction caused by the self-defocusing nonlinearity.
Our work provides deep insight into how to maintain the beam quality of
self-accelerating Airy beams by exploiting the interaction between partially
incoherence and optical nonlinearity. Our results may bring about new
possibilities for optimizing partially incoherent structured field and
developing related applications such as optical communication, incoherent
imaging and optical manipulations.Comment: 11pages,6 figure
Healthcare professionalsâ and patientsâ assessments of listed mobile health apps in China: a qualitative study
BackgroundIn recent years, mobile health (mHealth) has gradually developed in China, and intelligent medicine has become an important research topic. However, there are still significant problems in mHealth applications (apps). Although healthcare professionals and patients are the main users, few studies have focused on their perceptions of the quality of mHealth apps.ObjectiveThis study aimed to (1) understand the respective perceptions of healthcare professionals and patients regarding mHealth apps, (2) assess what barriers exist that influence the user experience, and (3) explore how to improve the quality of mHealth apps and the development of the mHealth market in China. The study aims to promote the standardization of mHealth apps and provide effective information for the improvement and development of mHealth apps in the future.MethodsSemistructured interviews with 9 patients and 14 healthcare professionals were conducted from January 2022 to April 2022 in the Affiliated Hospital of Xuzhou Medical University. The participants used mHealth apps for more than 3âmonths, including the âGood Moodâ and âPeace and Safe Doctorsâ apps and apps developed by the hospital that were popular in China. Interview transcripts were analysed using thematic analysis.ResultsThe following five themes were extracted: different concerns, hidden medical dangers, distance and insecurity, barriers for older people, and having positive perceptions of mHealth apps. Healthcare professionals prioritized simplicity in regard to mHealth apps, whereas patients rated effectiveness as the most crucial factor. The study also revealed several problems with mHealth apps, including insufficient information about physician qualifications, inaccurate medical content, nonstandard treatment processes, and unclear accountability, which led to a sense of distance and insecurity among participants. Older individuals faced additional obstacles when using mHealth apps. Despite these issues, the participants remained optimistic about the future of mHealth app development.ConclusionThe utilization, advantages, and obstacles of mHealth applications for healthcare professionals and patients were explored through semistructured interviews. Despite the promising prospects for mHealth apps in China, numerous issues still need to be addressed. Enhancing the safety monitoring system and developing user-friendly mHealth apps for older adult patients are essential steps to bridge the gap between healthcare providers and patients
How ChatGPT is Solving Vulnerability Management Problem
Recently, ChatGPT has attracted great attention from the code analysis
domain. Prior works show that ChatGPT has the capabilities of processing
foundational code analysis tasks, such as abstract syntax tree generation,
which indicates the potential of using ChatGPT to comprehend code syntax and
static behaviors. However, it is unclear whether ChatGPT can complete more
complicated real-world vulnerability management tasks, such as the prediction
of security relevance and patch correctness, which require an all-encompassing
understanding of various aspects, including code syntax, program semantics, and
related manual comments.
In this paper, we explore ChatGPT's capabilities on 6 tasks involving the
complete vulnerability management process with a large-scale dataset containing
78,445 samples. For each task, we compare ChatGPT against SOTA approaches,
investigate the impact of different prompts, and explore the difficulties. The
results suggest promising potential in leveraging ChatGPT to assist
vulnerability management. One notable example is ChatGPT's proficiency in tasks
like generating titles for software bug reports. Furthermore, our findings
reveal the difficulties encountered by ChatGPT and shed light on promising
future directions. For instance, directly providing random demonstration
examples in the prompt cannot consistently guarantee good performance in
vulnerability management. By contrast, leveraging ChatGPT in a self-heuristic
way -- extracting expertise from demonstration examples itself and integrating
the extracted expertise in the prompt is a promising research direction.
Besides, ChatGPT may misunderstand and misuse the information in the prompt.
Consequently, effectively guiding ChatGPT to focus on helpful information
rather than the irrelevant content is still an open problem
Recommended from our members
Oscillation-specific nodal alterations in early to middle stages Parkinsons disease.
Background: Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinsons disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods: Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027âHz; slow-4: 0.027-0.073âHz; slow-3: 0.073-0.198âHz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results: Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions: Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages
- âŠ