Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards identifying IoT traffic anomalies on the home gateway: poster abstract

The number of IoT devices continues to grow despite the alarming rate of identification of security and privacy issues. There is widespread concern that development of IoT devices is performed without sufficient attention paid to security and privacy issues. Consequently, networks have a higher probability of incorporating vulnerable IoT devices that may be easy to compromise to launch cyber attacks. Inclusion of IoT devices paves the way for a new category of anomalies to be introduced to networks. Traditional anomaly detection techniques (e.g., semi-supervised and signature-based methods), however, are likely inefficient in detecting IoT-based anomalies. This is because these techniques require static signatures of known attacks, specialized hardware, or full packet inspection. They are also expensive, and may be inaccurate or unscalable. Vulnerable IoT devices can be used to perform destructive attacks or invade privacy. The ability to find anomalies in IoT traffic has the potential to assist with early detection and deployment of countermeasures to thwart such attacks. Thus, new techniques for detecting infected IoT devices are needed to mitigate the associated security and privacy risks. In this research, we investigate the possibility to identify IoT traffic using a combination of behavioural profile, predefined blocklist and device fingerprint. Such a system may be able to detect anomalous and/or malicious devices and/or traffic reliably and quickly. Initial results show that for our implementation of such a system, IoT traffic can be identified using device behaviour profile, fingerprint, and contacted destinations. This work takes the first step towards designing and evaluating iDetector, a framework that can detect anomalous behaviour within IoT networks. In our experiments, iDetector was able to correctly identify 80--90% of all captured traffic traversing a home gateway.
Spying with your robot vacuum cleaner: eavesdropping via lidar sensors

Eavesdropping on private conversations is one of the most common yet detrimental threats to privacy. A number of recent works have explored side-channels on smart devices for recording sounds without permission. This paper presents LidarPhone, a novel acoustic side-channel attack through the lidar sensors equipped in popular commodity robot vacuum cleaners. The core idea is to repurpose the lidar to a laser-based microphone that can sense sounds from subtle vibrations induced on nearby objects. LidarPhone carefully processes and extracts traces of sound signals from inherently noisy laser reflections to capture privacy sensitive information (such as speech emitted by a victim's computer speaker as the victim is engaged in a teleconferencing meeting; or known music clips from television shows emitted by a victim's TV set, potentially leaking the victim's political orientation or viewing preferences). We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and evaluate the feasibility of the attack through comprehensive real-world experiments. We use the prototype to collect both spoken digits and music played by a computer speaker and a TV soundbar, of more than 30k utterances totaling over 19 hours of recorded audio. LidarPhone achieves approximately 91% and 90% average accuracies of digit and music classifications, respectively.
RFWash: a weakly supervised tracking of hand hygiene technique

Each year, hundreds of thousands of people contract Healthcare Associated Infections (HAIs). Poor hand hygiene compliance among healthcare workers is thought to be the leading cause of HAIs and methods were developed to measure compliance. Surprisingly, human observation is still considered the gold standard for measuring compliance by World Health Organization (WHO). Moreover, no automated solutions exist for monitoring hand hygiene techniques, such as "how to hand rub" technique by WHO. In this paper, we introduce RFWash; the first radio-based device-free system for monitoring Hand Hygiene (HH) technique. On the technical level, HH gestures are performed back-to-back in a continuous sequence and pose a significant challenge to conventional two-stage gesture detection and recognition approaches. We propose a deep model that can be trained on unsegmented naturally-performed HH gesture sequences. RFWash evaluation demonstrates promising results for tracking HH gestures, achieving gesture error rate of < 8% when trained on 10-second segments, which reduces manual labelling overhead by ≈ 67% compared to fully supervised approach. The work is a step towards practical RF sensing that can reliably operate inside future healthcare facilities.
DISCOvering the heavy hitters with disaggregated sketches

We propose DISCO - a lightweight approach to flow monitoring in the data plane. The idea is to disaggregate the computation of a single (logically) centralized sketch into multiple small "sketch fragments" that are distributed across the flows' paths. This allows use less resources at switches without trading on telemetry capabilities.
Serum Adiponectin Levels Are Positively Associated With Diabetic Peripheral Neuropathy in Chinese Patients With Type 2 Diabetes

Objective To investigate the association between serum adiponectin levels and diabetic peripheral neuropathy (DPN) in Chinese type 2 diabetes (T2D) patients. Design and Methods Two hundred nineteen T2D patients aged 40–79 years were divided into two groups according to whether they had DPN. The systemic levels of five biomarkers were measured using a human adipokine multiplexed bead-based immunoassay. Diabetic peripheral neuropathy diagnostic criteria included both common DPN symptoms and neurological screening tests. Results Most features of DPN (n=98) and non-DPN patients (n=121) are similar, but the DPN patients were slightly older, had longer diabetes duration, higher hemoglobin (Hb) A1c, lower estimated glomerular filtration rates (eGFR), less exercise, and used lipid-lowering drugs more often. Serum adiponectin levels of DPN patients were higher than that of non-DPN patients (8.13 vs. 9.63 mg/ml, P = 0.004). Serum adiponectin levels were positively associated with DPN after adjusting for age, gender, body mass index, hypertension, HbA1c, alcohol intake, smoking status, physical activity, log-transformed low density lipoprotein cholesterol, lipid-lowering drug usage, eGFR, and diabetes duration {odds ratio (OR) 1.72 [95% confidence interval (CI) 1.02-2.89], P = 0.041}. The OR refers to a doubling in biomarkers. Conclusions Serum adiponectin levels were higher in DPN patients compared to non‑DPN patients in this Chinese T2D population. Serum adiponectin levels were positively associated with DPN presence, independent of multiple confounders.
BatComm: enabling inaudible acoustic communication with high-throughput for mobile devices

Acoustic communication is an increasingly popular alternative to existing short-range wireless communication technologies for mobile devices, such as NFC and QR codes. Unlike the current standards, there are no requirements for extra hardware, lighting conditions, or Internet connection. However, the audibility and limited throughput of existing studies hinder their deployment on a wide range of applications. In this paper, we aim to redesign acoustic communication mechanism to push the boundary of potential throughput while keeping the inaudibility. Specifically, we propose BatComm, a high-throughput and inaudible acoustic communication system for mobile devices capable of throughput rates 12X higher than contemporary state-of-the-art acoustic communication for mobile devices. We theoretically model the non-linearity of microphone and use orthogonal frequency division multiplexing (OFDM) to transmit data bits over multiple orthogonal channels with an ultrasound frequency carrier. We also design a series of techniques to mitigate interference caused by sources such as the signal's unbalanced frequency response, ambient noise, and unrelated residual signals created through OFDM, amplitude modulation (AM), and related processes. Extensive evaluations under multiple realistic settings demonstrate that our inaudible acoustic communication system can achieve over 47kbps within a 10cm communication range. We also show the possibility of increasing the communication range to room scale (i.e., around 2m) while maintaining high-throughput and inaudibility. Our findings offer a new direction for future inaudible acoustic communication techniques to pursue in emerging mobile and IoT applications.
Reorganizing queries with grouping

Language-integrated query has attracted much attention from researchers and engineers. It enables one to write a database query with high-level abstractions, which makes it possible to compose, iterate, and reuse queries. An important issue in language-integrated query is the N+1 query problem, and Cheney et al. proposed a program-transformation approach to solve it for a core language of Microsoft’s LINQ. In our previous work, we extended their language to grouping (GROUP BY in SQL) and aggregate functions, and showed that any term can be transformed to a single SQL query. It still has a problem in that the resulting queries may be unnecessarily large and inefficient. This paper solves the problem. Our key idea is re-organization of queries with nested control structures. While our previous work decomposes grouping into finer primitives before transformation, the new algorithm fuses nested control structures after transformation, while keeping the absence of nested data structures. Our algorithm also eliminates correlated subqueries as much as possible, to obtain better performance. We have conducted performance measurements, which shows that our new algorithm reduces the size of generated queries and improves the performance for several examples.
Stateless CPU-aware datacenter load-balancing

Today, datacenter operators deploy Load-balancers (LBs) to efficiently utilize server resources, but must over-provision server resources (by up to 30%) because of load imbalances and the desire to bound tail service latency. We posit one of the reasons for these imbalances is the lack of per-core load statistics in existing LBs. As a first step, we designed CrossRSS, a CPU core-aware LB that dynamically assigns incoming connections to the least loaded cores in the server pool. CrossRSS leverages knowledge of the dispatching by each server's Network Interface Card (NIC) to specific cores to reduce imbalances by more than an order of magnitude compared to existing LBs in a proof-of-concept datacenter environment, processing 12% more packets with the same number of cores.
An adaptive noise removal tool for IoT image processing under influence of weather conditions: poster abstract

As the foundation of intelligent algorithms and applications, data collection from the real world faces the problem that there is serious data degradation under various complex environments. As a typical situation, the visual degradation of images under different weather conditions only can be utilized after arduous image noise removal by application developers previously. To overcome the challenges, previous approaches cannot handle with comprehensive situations. In this paper, we will briefly describe an adaptive image noise removal tool, which can classify multiple weather conditions and enhance image quality with optimized algorithms. Further, we constructed a recognition application using YOLO-3, and validated the effect of our tool through recognition results of real-world images.
On-demand communication with the batteryless MiroCard: demo abstract

Over the last decade, energy harvesting has seen significant growth as different markets incorporate green and sustainable electrical energy production. Even though costs have fallen, few products in the Internet-of-Things marketplace have embraced solutions based on energy harvesting. This is partly due to a mismatch in both the power density and timeliness of energy production and consumption. Until recently, harvesting-based systems required a battery or supercapacitor to be functional. After years of research, advances in energy management techniques have enabled the design of fully batteryless sensing devices. This demo introduces the batteryless MiroCard, a novel smart-card powered by light. Its fast wake-up times and energy-efficient operation allow the MiroCard to emit BLE beacons even in low ambient light conditions.
Semantic Optimization of Conjunctive Queries

This work deals with the problem of semantic optimization of the central class of conjunctive queries (CQs). Since CQ evaluation is NP-complete, a long line of research has focussed on identifying fragments of CQs that can be efficiently evaluated. One of the most general restrictions corresponds to generalized hypetreewidth bounded by a fixed constant k ≥ 1; the associated fragment is denoted GHWk. A CQ is semantically in GHWk if it is equivalent to a CQ in GHWk. The problem of checking whether a CQ is semantically in GHWk has been studied in the constraint-free case, and it has been shown to be NP-complete. However, in case the database is subject to constraints such as tuple-generating dependencies (TGDs) that can express, e.g., inclusion dependencies, or equality-generating dependencies (EGDs) that capture, e.g., key dependencies, a CQ may turn out to be semantically in GHWk under the constraints, while not being semantically in GHWk without the constraints. This opens avenues to new query optimization techniques. In this article, we initiate and develop the theory of semantic optimization of CQs under constraints. More precisely, we study the following natural problem: Given a CQ and a set of constraints, is the query semantically in GHWk, for a fixed k ≥ 1, under the constraints, or, in other words, is the query equivalent to one that belongs to GHWk over all those databases that satisfy the constraints? We show that, contrary to what one might expect, decidability of CQ containment is a necessary but not a sufficient condition for the decidability of the problem in question. In particular, we show that checking whether a CQ is semantically in GHW1 is undecidable in the presence of full TGDs (i.e., Datalog rules) or EGDs. In view of the above negative results, we focus on the main classes of TGDs for which CQ containment is decidable and that do not capture the class of full TGDs, i.e., guarded, non-recursive, and sticky sets of TGDs, and show that the problem in question is decidable, while its complexity coincides with the complexity of CQ containment. We also consider key dependencies over unary and binary relations, and we show that the problem in question is decidable in elementary time. Furthermore, we investigate whether being semantically in GHWk alleviates the cost of query evaluation. Finally, in case a CQ is not semantically in GHWk, we discuss how it can be approximated via a CQ that falls in GHWk in an optimal way. Such approximations might help finding “quick” answers to the input query when exact evaluation is intractable.
Phenotyping of Adaptive Immune Responses in Inflammatory Diseases

Immunophenotyping on the molecular and cellular level is a central aspect for characterization of patients with inflammatory diseases, both to better understand disease etiopathogenesis and based on this to develop diagnostic and prognostic biomarkers which allow patient stratification and tailor-made treatment strategies. Technology-driven developments have considerably expanded the range of analysis tools. Especially the analysis of adaptive immune responses, often regarded as central though mostly poorly characterized disease drivers, is a major focus of personalized medicine. The identification of the disease-relevant antigens and characterization of corresponding antigen-specific lymphocytes in individual patients benefits significantly from recent developments in cytometry by sequencing and proteomics. The aim of this workshop was to identify the important developments for state-of-the-art immunophenotyping for clinical application and precision medicine. We focused here on recent key developments in analysis of antigen-specific lymphocytes, sequencing, and proteomics approaches, their relevance in precision medicine and the discussion of the major challenges and opportunities for the future.
MAScreen: Augmenting Speech with Visual Cues of Lip Motions, Facial Expressions, and Text Using a Wearable Display

Personal protective equipment, particularly face masks, have become increasingly common with the rise of global health issues, such as fine-dust storms and pandemics. Face masks, however, also degrade speech intelligibility by effectively occluding visual cues, such as lip motions and facial expressions. In this paper, we propose MAScreen, a wearable LED display in the shape of a mask, which is capable of sensing lip motion and speech and provides real-time visual feedback of the mouth behind the mask.
Annotating executable DSLs with energy estimation formulas

Reducing the energy consumption of a complex, especially cyber-physical, system is a cross-cutting concern through the system layers, and typically requires long feedback loops between experts in several engineering disciplines. Having an immediate automatic estimation of the global system consumption at design-time would significantly accelerate this process, but cross-layer tools are missing in several domains. Executable domain-specific modeling languages (xDSLs) can be used to design several layers of the system under development in an integrated view. By including the behavioral specification for software and physical components of the system, they are an effective source artifact for cross-layer energy estimation. In this paper we propose EEL, a language for annotating xDSL primitives with energy-related properties, i.e. how their execution would contribute to the energy consumption on a specific runtime platform. Given an xDSL, energy specialists create EEL models of that xDSL for each considered runtime platform. The models are used at design time, to predict the energy consumption of the real systems. This avoids the need of energetic analysis by deployment and measurement on all runtime platforms, that is slow and expensive. We augment an existing language workbench for xDSLs with an editor for EEL models and a component that computes energy-consumption estimations during model editing. The evaluation shows that EEL can be used to represent estimation models from literature, and provide useful predictions.
Advancing Computerized Cognitive Training for MCI and Alzheimer's Disease in a Pandemic and Post-pandemic World

Worldwide, some 40 million adults have Alzheimer’s disease (AD) (1) and several hundred million may be at elevated risk for AD by virtue of mild cognitive impairment (MCI) and/or silent buildup of cortical AD pathology. There are no pharmacological treatments with more than minimal efficacy for mild AD, and prevention strategies are not established. The COVID-19 pandemic has transformed mobile health applications and telemedicine from nice to have tools into essential healthcare infrastructure (2–13). We anticipate that this need will be particularly great for the elderly who, due to their greater risk for infection, may avoid medical facilities or be required to self-isolate. These are also the very groups at highest risk for cognitive decline. Further, emerging data suggests COVID-19may itself be linked with longer-term neurological consequences, including cognitive decline (5). Definitive data on the utility of cognitive/mental wellness tools during the pandemic awaits the results of ongoing clinical trials (6–10), but there is accumulating preliminary evidence (11). For example, during the COVID-19 pandemic, chatbots employed by hospitals and government agencies fielded millions of queries from concerned patients (3). Digital tools also were deployed to provide psychological self-help to people isolated at home or in retirement centers and nursing homes (2–4). A survey of 1,000 adults done in March 2020 (12) found that 82% were concerned about leaving their home, 78% are avoiding doctor visits unrelated to COVID and 80% would prefer to receive a remote virtual health consultation if given the opportunity. A recent survey of elderlyMCI subjects during the pandemic (13) demonstrated potential for cognitive stimulation via assistive technology but also found that those living alone had the greatest negative mental effects.
Probing Neurovisceral Integration via Functional Near-Infrared Spectroscopy and Heart Rate Variability

The neurovisceral integration model (NVM) proposes that an organism’s ability to flexibly adapt to its environment is related to biological flexibility within the central autonomic network (CAN). One important aspect of this flexibility is behavioral inhibition (Thayer and Friedman, 2002). During a behavioral inhibition task, the CAN, which comprises a series of feedback loops, must be able to integrate information and react to these inputs flexibly to facilitate optimal performance. The functioning of the CAN is shown to be associated with respiratory sinus arrhythmia (RSA), as the vagus nerve is part of this feedback system. Although the NVM has been examined through neural imaging and RSA, only a few studies have examined these measures simultaneously during the neuroimaging procedure. Furthermore, these studies were done at rest or used tasks that were not targeted at processes associated with the NVM, such as behavioral inhibition and cognitive flexibility. For this reason, the present study assessed RSA and neural activation in the pre-frontal cortex simultaneously while participants completed a behavior inhibition task. RSA and functional near-infrared spectroscopy were collected in 38 adults, and resting levels of pre-frontal activation were negatively related to RSA, but pre-frontal activation during the behavior inhibition task was not. The negative relationship between RSA and oxygenated hemoglobin is consistent with previous functional magnetic resonance imaging work examining the NVM at baseline and should be further studied. Additional research investigating how this relationship may change based on task demands or environmental contexts would help clarify the applicability of the model.
The Impact of COVID-19 Pandemic on the Castile and Leon Addiction Treatment Network: A Real-Word Experience

Background: Patients suffering from addiction are a vulnerable group in the midst of COVID-19, so their healthcare is considered essential. In this paper, the measures and responses of the Drug Addiction Assistance Network of Castile and Leon (DAACYL) in Spain during the first 6 weeks of the COVID-19 pandemic are explained. The aim is that this experience could be useful in places where this problem will continue and could help future interventions. Methods: A telephone survey was carried out as the main methodology, to collect information for the subsequent organization and repercussion on professionals and patients. This was carried out by the heads of the 18 DAACYL units. Among the interventions applied, the following stand out: implantation of telemedicine techniques, restriction of daily methadone dispensing, suspension of urine controls and initiation of care programs for the homeless. Results: As a result of these interventions, the professionals observed that patients are less demanding and mostly stable, with a low percentage of relapses. An increase in the consumption of alcohol and benzodiazepines have been reported as more common among people who relapse. Furthermore, the prevalence of COVID-19 infection in the sample is minimal; therefore, different hypotheses should be considered as an explanation (infra-diagnosis, immune system used to aggression, possible anti-inflammatory effect of some psychotropic drugs and a greater perception of danger against infection than the general population). Conclusions: The rapid adaptation and successful implementation of DAACYL have had satisfactory results. On the other hand, the prevention of the possible increase in the development of behavioral addictions and the use of homemade drugs should be considered.