Miracle-dz/UniFAR-FTUs huggingface dataset:
OASIS: An Intrusion Detection System Embedded in Bluetooth Low Energy Controllers

Bluetooth Low Energy has established itself as one of the central protocols of the Internet of Things. Its many features (mobility, low energy consumption) make it an attractive protocol for smart devices. However, numerous critical vulnerabilities affecting BLE have been made public in recent years, some of which are linked to the protocol’s design itself. The impossibility of correcting these vulnerabilities without affecting the specification requires the development of effective intrusion detection systems, enabling the detection and prevention of these threats. Unfortunately, the protocol relies on peer-to-peer communications and introduces many complex and dynamic mechanisms (e.g., channel hopping), making monitoring complex, costly and limited. Existing intrusion detection approaches lack flexibility, are limited in scope and introduce high deployment costs. In this paper, we explore a novel approach consisting in embedding an intrusion detection system directly within BLE controllers. This strategic position tackles these challenges by enabling a more advanced analysis and instrumentation of the protocol and opens the way to new defensive applications. We propose OASIS, a framework for injecting detection heuristics into controllers’ firmwares in a generic way without affecting the normal operation of the protocol stack. It can be deployed in various contexts during the life cycle of a device, from the chip manufacturer to a software developer making use of proprietary components, or even in a full black box context by a security analyst to harden a commercial product. We describe its modular architecture and present its implementation within five of the most popular BLE chips from three different manufacturers, deployed in billions of devices and embedding heterogeneous protocol stacks. We present five modules for critical low-level protocol attack detection. We show that OASIS has a low impact on the controller performance (power, timing, memory) and evaluate its usage in a real-world setting.
Simulating interacting multiple natural-hazard events for lifecycle consequence analysis

: Among different types of natural-hazard interactions (simply multi-hazard interactions hereinafter), some occur through the nature of the hazards themselves, regardless of the presence of any physical assets: they are often called “Level I” (or occurrence) interactions. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., severe wind and flooding; liquefaction and landslides triggered by an earthquake), thus creating a dependency between the parameters characterising such hazard events. They differ from “Level II” (or consequence) interactions, which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impact due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard Life Cycle Analysis (LCA) aims to quantify the consequences (e.g., repair costs, downtime, and casualty rates) expected throughout a system’s service life, accounting for both Level I and Level II interactions. Nevertheless, the available literature generally considers these interactions mainly defining relevant taxonomies, often qualitatively, without providing a computational framework to simulate a sequence of hazard events in terms of their occurrence times and features and resulting consequences. This paper aims to partly fill this gap by identifying modelling approaches associated with different Level I interactions. It describes a simulation-based approach for generating multi-hazard scenarios (i.e., a sequence of hazard events and associated features through the system’s life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method outputs potential sequences of events throughout a system’s
Assessment of post-traumatic stress disorders in women victims of intimate partner violence: a mixed methods comparison at initial care in coordinated and uncoordinated care facilities in France

Background: Intimate Partner Violence (IPV) has serious consequences on the mental health of victims-survivors such as Post-Traumatic Stress Disorder (PTSD). « La Maison des Femmes » (MdF, Women’s Home) is a French original medical and social structure speci�cally dedicated to provide multidisciplinary care for victims-survivors of IPV. The main objective of this study was to examine the prevalence of PTSD in victims-survivors of IPV consulting at the MdF or in two Municipal Health Centers (MHCs) located in the same area of the Paris conurbation. Methods: We conducted an observational survey from July 2020 to June 2021 in the MdF and in two MHCs. All women aged 18 years and over having suffered from IPV were eligible. PTSD diagnosis was assessed using the PTSD self-report checklist of symptoms de�ned in the DSM-5 (PCL-5). We solicited data on the level of self-rated global health, the substances use, and the possibility of reaching women by phone in the 6 months following the inclusion visit. We also conducted qualitative interviews with a sub-sample of the women, asking for victims-survivors’ perceptions of the effect of the care provided at MdF and perceptions of their speci�c needs. Results: A total of 67 women (mean age: 34 years old [Standard Deviation=9.7]) responded to our questionnaire (40 in the MdF, 12 in the MHC-1 and 15 in the MHC-2). PTSD diagnosis was retained for 40 women (59.7%) (PCL-5 score ≥ 33). The prevalence of PTSD was quite similar between the three groups. Around 30% of participants (n=23) self-rated their global health as bad or very bad, less than 30% (n=18) of women were regular smokers. Six months after inclusion, a half of participants (52.2%) had been reached by phone. Analysis of the nine qualitative interviews clari�ed victims-survivors’ perceptions of the MdF’s speci�c care: social networking, multidisciplinary approach, specialized listening, healthcare facilities, evasion and “feeling at home”.
Household Livelihood Strategies and Socio-Economic Conditions Influencing Watershed Degradation in Kaiti Sub-watershed, Makueni County, Kenya

Aim: The aim of the study was to find out how livelihood strategies and socio-economic conditions influence watershed degradation in Kaiti sub-watershed in Makueni County, Kenya, and their effects on the environment. The study examined the livelihood strategies and options of the people as well as the socio-economic conditions contributing to watershed degradation, investigated the land use methods practiced and how they affect the sub-watershed. Study Design: The study used a descriptive survey research approach to obtain data on socioeconomic characteristics of the study sites as well as historical trends of land use. Place and Duration of Study: South Eastern Kenya University, Kitui County, Kenya; between June to August 2015. Original Research Article Kieti et al.; JSRR, 12(2): 1-13, 2016; Article no.JSRR. 28412 2 Methodology: The study used multiple methods such as household surveys, observations, Focus Group Discussant interviews (FGDs), key informant and experts’ interviews, drawn from sampling of households systematically along vertical and horizontal transect lines. Structured and semistructured questionnaires were used to collect data from 101 community and key informants. The data collected was analysed using Statistical Package for Social Sciences (SPSS) and Microsoft Excel 2010. Results: The study established that low food production as reported by the farmers (78%) and reduced income and livelihood (75%) were consequences of watershed degradation in the study area. Landlessness at 39% (S.E=0.311 z= 1.311 sig.0.190), illegal encroachment at 18% (S.E=0.555 z= -0.604 sig.0.546), and laxity in law enforcement at 27% (S.E=0.481 z=0.227 sig. 0.821) were other factors mentioned by the farmers as contributing to watershed degradation. Conclusion: The study concluded that these conditions predisposed farmers to adopt inappropriate farming methods and unsustainable livelihood strategies which compromised the watershed’s environmental integrity. The study sought to make recommendation for efficient watershed management.
Reverse Engineering of Multiplexed CAN Frames

—The Controller Area Network (CAN) is widely deployed as the de facto global standard for the communication between Electronic Control Units (ECUs) in the automotive sector. Despite being unencrypted, the data transmitted over CAN is encoded according to the Original Equipment Man-ufacturers (OEMs) speciﬁcations, and their formats are kept secret from the general public. Thus, the only way to obtain accurate vehicle information from the CAN bus is through reverse engineering. Aftermarket companies and academic researchers have focused on automating the CAN reverse-engineering process to improve its speed and scalability. However, the manufacturers have recently started multiplexing the CAN frames primarily for platform upgrades, rendering state-of-the-art (SOTA) reverse engineering ineffective. To overcome this new barrier, we present CAN Multiplexed Frames Translator ( CAN-MXT ), the ﬁrst tool for the identiﬁcation of new-generation multiplexed CAN frames. We also introduce CAN Multiplexed Frames Generator ( CAN-MXG ), a tool for the parsing of standard CAN trafﬁc into multiplexed trafﬁc, facilitating research and app development on CAN multiplexing.
Brain Network Recon ﬁ guration and Perceptual Decoupling During an Absorptive State of Consciousness

Trance is an absorptive state of consciousness characterized by narrowed awareness of external surroundings and has long been used — for example, by shamans — to gain insight. Shamans across cultures often induce trance by listening to rhythmic drumming. Using functional magnetic resonance imaging (fMRI), we examined the brain-network con ﬁ guration associated with trance. Experienced shamanic practitioners ( n = 15) listened to rhythmic drumming, and either entered a trance state or remained in a nontrance state during 8-min scans. We analyzed changes in network connectivity. Trance was associated with higher eigenvector centrality (i
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects

In order to improve the detection accuracy for objects at different scales, most of the recent works utilize the pyramidal feature hierarchy of the ConvNets from bottom to top. Nevertheless, the weak semantic information makes the bottom layers poor in detection, especially for small objects. Furthermore, most of the fine details are lost on the top layers. In this paper, we design a Multi-scale Deconvolutional Single Shot Detector for small objects (MDSSD for short). To obtain the feature maps with enriched representation power, we add the high-level layers with semantic information to the low-level layers via deconvolution Fusion Block. It is noteworthy that multiple high-level layers with different scales are upsampled simultaneously in our framework. We implement the skip connections to form more descriptive feature maps and predictions are made on these new fusion layers. Our proposed framework achieves 78.6% mAP on PASCAL VOC2007 test and 26.8% mAP on MS COCO test-dev2015 at 38.5 FPS with only 300*300 input.
Event Nugget Detection, Classification and Coreference Resolution using Deep Neural Networks and eXtreme Grandient Boosting

For the shared task of event nugget detection at TAC 2015 we trained a deep feed forward network achieving an ofﬁcial F 1 -score of 65 . 31% for plain annotations, 55 . 56% for event mention type and 49 . 16% for the realis value. For the task of Event Coreference Resolution we prototyped a simple baseline using Gradient Boosted Decision Trees achieving an over-all average CoNLL score of 70 . 02% . Our code is publicly available on GitHub 1 .
A Tutorial on IEEE 802.11ax High Efﬁciency WLANs

—While celebrating the 21st year since the very ﬁrst IEEE 802.11 “legacy” 2 Mbit/s wireless Local Area Network standard, the latest Wi-Fi newborn is today reaching the ﬁnish line, topping the remarkable speed of 10 Gbit/s. IEEE 802.11ax was launched in May 2014 with the goal of enhancing throughput-per-area in high-density scenarios. The ﬁrst 802.11ax draft versions, namely D1.0 and D2.0, were released at the end of 2016 and 2017. Focusing on a more mature version D3.0, in this tutorial paper, we help the reader to smoothly enter into the several major 802.11ax breakthroughs, including a brand new OFDMA-based random access approach as well as novel spatial frequency reuse techniques. In addition, this tutorial will highlight selected signiﬁcant improvements (including PHY enhancements, MU-MIMO extensions, power saving advances, and so on) which make this standard a very signiﬁcant step forward with respect to its predecessor 802.11ac.
The Aquila Digital Community The Aquila Digital Community

Studying Deese-Roediger-McDermott (DRM) lists using a distinctive encoding task can reduce the DRM false memory illusion. Reductions for both distinctively encoded lists and non-distinctively encoded lists in a within-group design have been ascribed to use of a distinctiveness heuristic by which participants monitor their memories at test for distinctive-task details. Alternatively, participants might simply set a more conservative response criterion, which would be exceeded by distinctive list items more often than all other test items, including the critical nonstudied items. To evaluate these alternatives, we compared a within group who studied 5 lists by reading, 5 by anagram generation, and 5 by imagery, relative to a control group who studied all 15 lists by reading. Generation and imagery improved recognition accuracy by impairing relational encoding, but the within group did not show greater memory monitoring at test relative to the read control group. Critically, the within group’s pattern of list-based source judgments provided new evidence that participants successfully monitored for distinctive-task details at test. Thus, source judgments revealed evidence of qualitative, recollection-based monitoring in the within group, to which our quantitative signal-detection measure of monitoring was blind.
Multi-Modal User Modeling for Task Guidance: A Dataset for Real-Time Assistance with Stress and Interruption Dynamics

A BSTRACT Integrating artificial intelligence (AI) with wearable technology and the power of augmented reality (AR) holds great potential for enabling real-time task assistance. Through wearable sensors and cameras, it is possible to monitor both the state of the physical world and the biometric status of a human operator, which can allow the AI to offer adaptive assistance tailored to match the user’s needs and optimize aid. Developing AI models for such purposes requires training data that not only describes users’ cognitive state during tasks but also accounts for their responses to the interactive system during the task. We introduce the Multi-Modal User Modeling for Task Guidance (MUMTG) to support the development of AI models for such purposes. The dataset is created through human-subjects studies with users performing search and assembly procedures with help from virtual instructions provided by either a head-worn AR headset or a monitor screen. The data-collection study uses a game-like scenario to guide participants through six guided tasks that vary in difficulty. Withineach group, we manipulated task duration and induced several stress triggers to increase the task cognitive demand for three tasks. The dataset includes physiological data such as electrodermal activity, temperature, heart rate, pupil dilation, and gaze. We also collected subjective self-report ratings regarding task workload and emotional responses after each task. We offer this rich dataset as a valuable resource to facilitate the development of user models for task guidance in highly demanding contexts.
Investigating the Efficiency of Drinking Water Treatment Sludge and Iron-Based Additives in Anaerobic Digestion of Dairy Manure: A Kinetic Modeling Study

In the quest for enhanced anaerobic digestion (AD) performance and stability, iron-based additives as micro-nutrients and drinking water treatment sludge (DWTS) emerge as key players. This study investigates the kinetics of methane production during AD of dairy manure, incorporating varying concentrations of Fe and Fe 3 O 4 (10, 20
Perceiving head movements in news readings: Evidence from web-based auditory vs. audio-visual prominence ratings

Previous research has shown that visual information can be integrated in the perception of prominence, but the available evidence stems mostly from controlled experimental settings, often making use of synthetic stimuli. The present study provides evidence from spontaneously produced head gestures that occurred in Swedish television news readings. Materials were rated for word prominence by 85 adult volunteers in a between-subjects design (audio-visual vs. audio-only ratings) using a crowd-sourcing approach. Accented words accompanied by a head movement were perceived as more prominent than accented words lacking a head movement. Crucially, the difference in perceived prominence level between words with and without a head gesture was found to be larger in an audio-visual rating condition compared to an audio-only condition. The results suggest that visual prominence signals are integrated in speech processing even in a relatively uncontrolled, naturalistic setting, such as watching the news.
ScholarWorks@UARK ScholarWorks@UARK

Training a system of artificial neural networks on digital images is a big challenge. Often times digital images contain a large amount of information and values for artificial neural networks to understand. In this work, the inference model is proposed in order to absolve this problem. The inference model is composed of a parameterized autoencoder that endures the loss of information caused by the rescaling of images and transition model that predicts the effect of an action on the observation. To test the inference model, the images of a moving robotic arm were given as the data set. The inference model successfully reconstructed the observation using small, rescaled images and even anticipated the observation based on its intuition using its transition model. Capabilities of the inference model implies that the model extracted the essential features of the digital images.
Open-ocean deep convection explored in the Mediterranean

Open-ocean deep convection is a littleunderstood process occurring in winter in remote areas under hostile observation conditions, for example, in the Labrador and Greenland Seas and near the Antarctic continent. Deep convection is a crucial link in the “Great Ocean Conveyor Belt” [Broecker, 1991], transforming poleward flowing warm surface waters through atmosphere-oceaninteraction into cold equatorward flowing water masses. Understanding its physics, interannual variations, and role in the global thermohaline circulation is an important objective of climate change research. In convection regions, drastic changes in water mass properties and distribution occur on scales of 10–100 km. These changes occur quickly and are difficult to observe with conventional oceanographic techniques. Apart from observing the development of the deep-mixed patch of homogeneous water itself, processes of interest are convective plumes on scales <1 km and vertical velocities of several cm s−1 [Schott et al., 1994] that quickly mix water masses vertically, and instability processes at the rim of the convection region that expedite horizontal exchanges of convected and background water masses [e.g., Gascard, 1978].
NBER WORKING PAPER SERIES THE MACROECONOMY AND THE YIELD CURVE: A DYNAMIC LATENT FACTOR APPROACH

We thank the Editor (Giampiero Gallo) and two referees for insightful guidance. We thank the Guggenheim Foundation, the National Science Foundation, and the Wharton Financial Institutions Center for research support. Finally, we thank Andrew Ang, Pierluigi Balduzzi, Todd Clark, Ron Gallant, Ken Nyholm, Monika Piazzesi, Mike Wickens, Scott Weiner, and discussants and participants at many seminars and conferences, for helpful comments. The views expressed in this paper do not necessarily reflect those of the Federal Reserve Bank of San Francisco. The views expressed herein are those of the author(s) and not necessarily those of the National Bureau of Economic Research.
The Footwear and Footwear Modifications for Reducing Biomechanical Risk Factor, External Knee Adduction Moment for Medial Knee Osteoarthritis Progression: A Systematic Review and Meta-Analysis

Objective The purpose of this systematic study is to provide clear, patient-focused, recent, evidence-based, and consensus recommendations for footwear and footwear modifications with effects that are globally relevant in OA knee. The identification and quantitative rating of studies estimating 1st and 2nd KAM by wearing different types of footwear and insoles in patients with OA knee. Method: Five databases were searched. A full search of 258 articles was found. To be included in this study, the population should be with OA of any grade without any ambulatory aids, male and female were included with age group above 54 years. For the intervention, all the types of shoes and any kind of modification in the shoes were included. The Primary outcome of interest relating to the biomechanical risk of disease progression was the 1st and 2nd Knee Adduction Moment. Eligible studies were pooled using meta-analysis. Result: Twenty-three studies were included with a total population of 841. Variable stiffness shoe (Mean Difference MD: -0.27; 95% CI: -0.34, -0.21) and Moleca (Mean Difference MD: -0.25; 95% CI: -0.56, 0.05) (Mean Difference MD: -0.25; 95% CI: -0.56, 0.05) have a comparably large statistically significant reduction in KAM with low heterogeneity (Ch2 = 1.49, I2 = 0%). The quality of all the studies is moderate (modified Downs and Black quality checklist) and low to moderate risk of bias (QUADAS 2). Conclusion: Biomechanical parameters related to the medial knee load, including first peak EKAM and second peak EKAM, were reduced with the use of footwears and footwear modification, apart from the mobility shoes in first peak KAM and MBT in second peak KAM in comparison with barefoot. VSS and Moleca show significant changes in KAM. Future studies need to consider in terms of height of arch in LWI, duration of footwear usage, material, and rigidity of insole, consider the grades of OA knee for baseline for disease specific recommendation. However, based on our study, the footwear and its modifications show an immediate reduction in EKAM. VSS and Moleca have greater effect in reducing EKAM. Key words: Footwear, OA knee, Degenerative knee, Biomechanical knee, Knee adduction moment, Joint loading, Kinematic knee joint