Miracle-dz/UniFAR-FTUs huggingface dataset:
Security for Machine Learning-Based Systems: Attacks and Challenges During Training and Inference

The exponential increase in dependencies between the cyber and physical world leads to an enormous amount of data which must be efficiently processed and stored. Therefore, computing paradigms are evolving towards machine learning (ML)-based systems because of their ability to efficiently and accurately process the enormous amount of data. Although ML-based solutions address the efficient computing requirements of big data, they introduce security vulnerabilities into the systems, which cannot be addressed by traditional monitoring-based security measures. Therefore, this paper first presents a brief overview of various security threats in machine learning, their respective threat models and associated research challenges to develop robust security measures. To illustrate the security vulnerabilities of ML during training, inferencing and hardware implementation, we demonstrate some key security threats on ML using LeNet and VGGNet for MNIST and German Traffic Sign Recognition Benchmarks (GTSRB). Moreover, based on the security analysis of ML-training, we also propose an attack that has very less impact on the inference accuracy. Towards the end, we highlight the associated research challenges in developing security measures and provide a brief overview of the techniques used to mitigate such security threats.
Electroclinical Features of Generalized Paroxysmal Fast Activity in Typical Absence Seizures

Purpose: Generalized paroxysmal fast activity (GPFA) is a diffuse, paroxysmal, frontal predominant activity described in patients with generalized epilepsies. Studies specifically focusing on electroclinical features of typical absence seizures in children have not reported any GPFA-like features. We sought to identify GPFA in children with typical absence seizures, study its incidence, characteristic electroclinical features, and effect on their epilepsy. Methods: We performed a retrospective review of electroencephalograms of children with diagnosis of absence epilepsy. A total of 173 subjects were identified. In subjects with GPFA on their electroencephalograms, GPFA characteristics were collected (i.e., predominant location, duration, amplitude, frequency, provocation factors, and if GPFA was followed by spike-wave discharges). In GPFA-positive subjects, further data sets were collected examining their demographics, duration of epilepsy, and pharmacoresponsiveness to epilepsy. Results: Generalized paroxysmal fast activity was identified in 10 subjects (5.78%) with female to male ratio of 9:1. Median age of subjects was 17 years, and median duration of illness was 9.5 years. Mean maximum GPFA amplitude was 88.3 &mgr;V with posterior predominance in 9/10 subjects. Generalized paroxysmal fast activity frequency ranged between 11 and 20 Hz with duration of 1 to 4 seconds. Generalized paroxysmal fast activity was provoked with eye closure, hyperventilation, and photic stimulation. Antiseizure medications had no effect on GPFA, and epilepsy was well controlled in most subjects. Conclusions: Generalized paroxysmal fast activity is uncommon in children with typical absence seizures and has medium voltage, posterior predominance, and marked female preponderance. Generalized paroxysmal fast activity is seen during both pharmacoresponsive and drug-resistant epilepsy, and is not affected by antiseizure medications. It may serve as an independent marker of lifelong epilepsy.
Inferring User Intents from Motion in Hearing Healthcare

Sensors in our phones and wearables, leave digital traces of our activities. With active user participation, these devices serve as personal sensing devices, giving insights to human behavior, thoughts, intents and personalities. We discuss how acoustical environment data from hearing aids, coupled with motion and location data from smartphones, may provide new insights to physical and mental health. We outline an approach to model soundscape and context data to learn preferences for personalized hearing healthcare. Using Bayesian statistical inference we investigate how physical motion and acoustical features may interact to capture behavioral patterns. Finally, we discuss how such insights may offer a foundation for designing new types of participatory healthcare solutions, as preventive measures against cognitive decline, and physical health.
Jerney: A Peer-to-Peer Shared Public Transit on Fixed Routes

This paper introduces a public transit system called Jerney, which is a peer-to-peer shared ride on fixed routes. It takes the advantage of fixed route shared public transit (such as bus and rail systems) in order to keep the fare rate down compared to taxicabs, and provide a convenience of the peer-to-peer ride (such as Uber and Lyft) over the public transit such as bus and rail systems. Jerney system consists of three main components; passenger app, driver app, and dispatch system. Passenger books a shared ride via a mobile app by specifying origin and destination locations. The dispatch system then assigns a driver to the passenger and informs the passenger with pick-up and drop-off stations. Driver location can be monitored by the passenger once the ride has been booked. Jeryney system has been evaluated with real users in a user experience study from which there were positive feedbacks from the users, as they felt that the Jerney system was useful and easy to start using.
Histogram of oriented velocities for eye movement detection

Research in various fields including psychology, cognition, and medical science deal with eye tracking data to extract information about the intention and cognitive state of a subject. For the extraction of this information, the detection of eye movement types is an important task. Modern eye tracking data is noisy and most of the state-of-the-art algorithms are not developed for all types of eye movements since they are still under research. We propose a novel feature for eye movement detection, which is called histogram of oriented velocities. The construction of the feature is similar to the well known histogram of oriented gradients from computer vision. Since the detector is trained using machine learning, it can always be extended to new eye movement types. We evaluate our feature against the state-of-the-art on publicly available data. The evaluation includes different machine learning approaches such as support vector machines, regression trees, and k nearest neighbors. We evaluate our feature together with the machine learning approaches for different parameter sets. We provide a matlab script for the computation and evaluation as well as an integration in EyeTrace which can be downloaded at http://www.ti. uni-tuebingen.de/Eyetrace.1751. 0.html.
Analysis for the Influence of ABR Sensitivity on PTT-Based Cuff-Less Blood Pressure Estimation before and after Exercise

An accurate and continuous measurement of blood pressure (BP) is of great importance for the prognosis of some cardiovascular diseases in out-of-hospital settings. Pulse transit time (PTT) is a well-known cardiovascular parameter which is highly correlated with BP and has been widely applied in the estimation of continuous BP. However, due to the complexity of cardiovascular system, the accuracy of PTT-based BP estimation is still unsatisfactory. Recent studies indicate that, for the subjects before and after exercise, PTT can track the high-frequency BP oscillation (HF-BP) well, but is inadequate to follow the low-frequency BP variance (LF-BP). Unfortunately, the cause for this failure of PTT in LF-BP estimation is still unclear. Based on these previous researches, we investigated the cause behind this failure of PTT in LF-BP estimation. The heart rate- (HR-) related arterial baroreflex (ABR) model was introduced to analyze the failure of PTT in LF-BP estimation. Data from 42 healthy volunteers before and after exercise were collected to evaluate the correlation between the ABR sensitivity and the estimation error of PTT-based BP in LF and HF components. In the correlation plot, an obvious difference was observed between the LF and HF groups. The correlation coefficient r for the ABR sensitivity with the estimation error of systolic BP (SBP) and diastolic BP (DBP) in LF was 0.817 ± 0.038 and 0.757 ± 0.069, respectively. However, those correlation coefficient r for the ABR sensitivity with the estimation error of SBP and DBP in HF was only 0.403 ± 0.145 and 0.274 ± 0.154, respectively. These results indicated that there is an ABR-related complex LF autonomic regulation mechanism on BP, PTT, and HR, which influences the effect of PTT in LF-BP estimation.
Wi-Mind: Wireless Mental Effort Inference

From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should they only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this paper we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built and tested the system with 23 volunteers engaged in different tasks. Initial results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging.
Characterizing and Predicting Individual Traffic Usage of Mobile Application in Cellular Network

The proliferation of smart devices prompts the explosive usage of mobile applications, which increases network traffic load. Characterizing the application level traffic patterns from an individual perspective is valuable for operators and content providers to make technical and business strategies. In this paper, we identify several typical traffic patterns and predict per-user traffic demand utilizing application usage dataset in cellular network. Our primary contributions are twofold: First, we novelly designed a three-stage model combining factor analysis and machine learning to extract the traffic patterns of individuals. By detecting the latent temporal structure of their application usage, users in the network are grouped into six typical patterns. Second, we implement a Wavelet-ARMA based model to forecast per-user application level traffic demand. The evaluation on real-world dataset indicates the model improves the prediction accuracy by 7 to 8 times compared with the benchmark solutions.
A test of famine-induced developmental programming in utero

Abstract The ‘DOHaD’ literature argues that stressors encountered at age t ‘program’ individual health at age t+n, and that this programming appears strongest when t defines critical developmental periods including gestation. Accordingly, children of ill-nourished pregnant women suffer greater later life morbidity than do offspring of well-nourished mothers. The possibility that circumstances other than access to nutritious food drive both a mother’s diet and fetal development remains, however, a threat to the inference of programming in utero. Attempts to rule out this threat include tests of the hypothesis that birth cohorts in gestation during famines exhibit shorter life spans than other cohorts. The tests produce conflicting results attributed to confounding by autocorrelation, selective migration and introduction of modern medicine. We offer a test in which neither medicine nor migration nor autocorrelation could obscure the presumed effect. We apply time-series regression methods to the life span of Swedes born between 1751 and 1800 to test the hypothesis that cohorts exposed in utero to the Swedish Famine of 1773 lived shorter lives than expected from trends and other forms of autocorrelation. We use these 50 birth cohorts not only because they included those exposed to severe famine but also because they may well be the only human birth cohorts that completed life unaffected by selective migration and unaided by modern medicine and for which we know life span. We find that the cohort born in 1773 live 4.2 years longer than expected from trends over the last half of the 18th century.
Growth of an anaerobic sulfate‐reducing bacterium sustained by oxygen respiratory energy conservation after O2‐driven experimental evolution

Desulfovibrio species are representatives of microorganisms at the boundary between anaerobic and aerobic lifestyles, since they contain the enzymatic systems required for both sulfate and oxygen reduction. However, the latter has been shown to be solely a protective mechanism. By implementing the oxygen-driven experimental evolution of Desulfovibrio vulgaris Hildenborough, we have obtained strains that have evolved to grow with energy derived from oxidative phosphorylation linked to oxygen reduction. We show that a few mutations are sufficient for the emergence of this phenotype and reveal two routes of evolution primarily involving either inactivation or overexpression of the gene encoding heterodisulfide reductase. We propose that the oxygen respiration for energy conservation that sustains the growth of the O2 -evolved strains is associated with a rearrangement of metabolite fluxes, especially NAD+ /NADH, leading to an optimized O2 reduction. These evolved strains are the first sulfate-reducing bacteria that exhibit a demonstrated oxygen respiratory process that enables growth.
MHC '18: International Workshop on Mobile Human Contributions: Opportunities and Challenges

Ubicomp/HCI researchers are increasingly using smartphones to collect human-labelled data 'in the wild'. While this allows for the collection of a wide range of interesting data in authentic settings and surroundings, humans are notoriously inconsistent in the quality of their contributions. Improving the quality of data collected with mobile devices is a largely unexplored, but highly relevant field. The primary objective of this workshop is to share insights, ideas, and discoveries on the quality of mobile human contributions. The work presented in the International Workshop on Mobile Human Contributions (MHC '18) explores methods, tools, and novel approaches towards increasing the reliability of human data submissions with mobile devices.
Antibiotic-Resistant Bacteria in Greywater and Greywater-Irrigated Soils

This study represents the first systematic attempt to evaluate antibiotic-resistant bacteria (ARB) occurrence in treated greywater and the potential spread of these bacteria from the greywater to greywater-irrigated soil. Treated greywater from three recirculating vertical flow constructed wetlands, each located in a household in the central Negev Desert, Israel, was surveyed. The presence of antibiotic-resistant bacteria in raw and treated greywater was investigated with culture and molecular methods, as well as their presence in the corresponding treated-greywater-irrigated soils. Additionally, the effectiveness of chlorination to prevent the spread of ARB was tested. The total count of tetracycline-resistant bacteria significantly increased in the treated greywater, likely due to their concentration on the filter matrix of the treatment systems. Twenty-four strains of tetracycline-resistant bacteria were isolated and identified at the genus level by 16Sr RNA gene sequencing. All the tetracycline-resistant bacteria showed high resistance traits, and some of them presented multiple antibiotic resistances. Six tetracycline resistance genes (coding for efflux and ribosomal resistance mechanisms) and five β-lactamase genes were detected. In 14 of the isolated strains, the gene tet39, which is phylogenetically related to both environmental and clinical strains, was identified. All the tet39 resistant bacteria were positive to at least one of the β-lactamase genes tested. Chlorination was found to be an efficient method to reduce ARB in treated greywater. We concluded that disinfection of treated greywater may reduce the risks not only from the potential presence of pathogens but also from the presence of ARB and antibiotic resistance genes.
Ectopic adrenocorticotropic hormone syndrome associated with olfactory neuroblastoma: acquirement of adrenocorticotropic hormone expression during disease course as shown by serial immunohistochemistry examinations

Ectopic adrenocorticotropic hormone (ACTH) syndrome (EAS) is a condition of endogenous hypercortisolism sustained by an extrapituitary ACTH-secreting tumor. Olfactory neuroblastoma (ONB) is a rare malignant neoplasm of the sinonasal tract and is derived from the olfactory epithelium. Because the paranasal sinus is not a common site of EAS, the development of ONB in patients with EAS is rare. We herein report the first known case of ONB with acquirement of ACTH production during the clinical course as proven by immunohistochemistry. A 50-year-old man diagnosed with ONB was referred to our department in July 2015 because of hypokalemia, hyperglycemia, decreased eosinophil and granulocyte counts, and elevated serum levels of ACTH and cortisol. Although two previous ONB biopsy specimens (2011 and 2014) showed no ACTH immunoreactivity, a newly obtained specimen in August 2015 clearly showed ACTH immunoreactivity. This is the first case of ectopic ACTH syndrome associated with an ONB that acquired the ability to express ACTH during its clinical course as shown by serial immunohistochemical examinations.
Legible Normativity for AI Alignment: The Value of Silly Rules

It has become commonplace to assert that autonomous agents will have to be built to follow human rules of behavior--social norms and laws. But human laws and norms are complex and culturally varied systems; in many cases agents will have to learn the rules. This requires autonomous agents to have models of how human rule systems work so that they can make reliable predictions about rules. In this paper we contribute to the building of such models by analyzing an overlooked distinction between important rules and what we call silly rules -- rules with no discernible direct impact on welfare. We show that silly rules render a normative system both more robust and more adaptable in response to shocks to perceived stability. They make normativity more legible for humans, and can increase legibility for AI systems as well. For AI systems to integrate into human normative systems, we suggest, it may be important for them to have models that include representations of silly rules.
WiFit: A Bodyweight Exercise Monitoring System with Commodity Wi-Fi

Bodyweight exercises, such as push-up, sit-up, and squat, are effective forms of strength training to maintain good health. In order to improve people's exercise experience and provide feedback, lots of work has been done to monitor the bodyweight exercise by requiring people to wear special sensors on body. Different from traditional ways, a non-intrusive system WiFit is shown in this demo which uses surrounding Wi-Fi signals to monitor the bodyweight exercises without any attachment requirements. It not only could recognize the exercise type but also count the repetition number of exercise for diverse population even in different environments.
Associations between C-reactive protein levels and cognition during the first 6 months after acute psychosis

Abstract Objective Inverse relationships between the C-reactive protein (CRP) levels and cognitive performance in acute psychosis have been demonstrated. We aimed to investigate how the serum level and initial change of CRP in acutely admitted patients with psychosis was correlated with cognitive performance during a 6-months follow-up period. Methods The study is part of a pragmatic, randomised trial comparing four different second-generation antipsychotic drugs, and consists of 208 acute phase patients recruited at admittance for psychosis. This study reports data for all groups collectively, and does not compare treatment groups. Measurements of CRP and cognitive performance were conducted at baseline (T1) and after 4 weeks on average after inclusion (T2). Cognition was also assessed after 3 months (T3) and 6 months (T4) of follow-up. Results Global cognition improved during the follow-up period of 6 months, especially in the T1–T2 interval. The different cognitive subdomains showed different time-dependent profiles of improvement, with memory and attention improving significantly also in the later phases. Reduction of the CRP level during the initial follow-up interval (T1–T2) was associated with increased overall cognitive performance in the T2–T4 interval, but not in the T1–T2 interval. For the cognitive subdomains, we found an inverse association between change in CRP level and verbal abilities (T2–T4 interval), and attention (T2–T3 interval). Conclusion These findings indicate that initial changes in the serum level of CRP in the acute phase of psychosis may predict cognitive function in later phases of the disease.
WellComp 2018: First International Workshop on Computing for Well-Being

With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metric of computing, such as "efficiency" and "productivity", the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly paid attention in the recent years. In our first "WellComp" (Computing for Well-being) workshop, we intensively discuss about the contribution of ubiquitous computing towards users' well-being that covers both physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. Having strong international organization members in various ubicomp research domains, WellComp 2018 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to wellbeing and ubiquitous computing.
Resource Utilization with the Use of Seclusion and Restraint in a Dedicated Emergency Psychiatric Service

Objectives There is a dearth of data regarding the use of emergency interventions in dedicated emergency psychiatric service settings, and reliable data are needed. This article describes the frequency and duration of the use of seclusion and restraint for imminent or existing agitation, aggression, or violence in a dedicated emergency psychiatric service located within an academic university hospital and staffed by sufficient numbers of trained personnel. Methods We performed a retrospective chart review of 6 months’ visits to a dedicated emergency psychiatric service. Results Men outnumbered women with a 1.6 ratio of visits. Of 2843 subjects, 425 (14.6%) received emergent medication for anxiety (n = 90), substance withdrawal (n = 28), or agitation (n = 290). Physical interventions were used in 3.4%; 96 (3.3%) were secluded, and 9 (0.3%) were restrained. The average duration of seclusion was (mean ± standard deviation) 58.7 ± 37.4 minutes and for restraint 63.2 ± 23.4 minutes. Each episode of seclusion or restraint required approximately 3 hours of staff time. Conclusions The use of an intervention such as seclusion in >3% and restraint in 0.3% of patients represents the use of seclusion and restraint in a dedicated psychiatric emergency service with personnel trained to minimize the use of seclusion and restraint.