Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial-HD: Hyperdimensional Computing Adversarial Attack Design for Secure Industrial Internet of Things

Industrial Internet of Things (IIoT) is a collaboration of sensors, networking equipment, and devices to collect data from industrial operations. IIoT systems possess numerous security vulnerabilities due to inter-connectivity and limited computational power. Machine learning based intrusion detection system (IDS) is one possible security approach that continuously monitors network data and detects cyberattacks in an automated manner. Hyper-dimensional (HD) computing is a brain-inspired ML method that is sufficiently accurate while being extremely robust, fast, and energy-efficient. Based on these characteristics, HD can be a favorable ML-based IDS solution for IIoT systems. However, its prediction performance is impacted by small perturbations in the input data. To fully evaluate the vulnerabilities of HD, we propose an effective HD-oriented adversarial attack design. We first select the most diverse set of attacks to minimize overhead, and eliminate adversarial redundancy. Then, we perform a real-time attack selection which finds out the most effective attack. Our experiments on a realistic IIoT intrusion data set show the effectiveness of our attack design. Compared to the most effective single attack, our design strategy can improve attack success rate by up to 36%, and F1 score by up to 61%.
Mixed Integer Linear Programming Approach for Control Synthesis with Weighted Signal Temporal Logic

This work presents an optimization-based control synthesis approach for an extension of Signal Temporal Logic (STL) called weighted Signal Temporal Logic (wSTL). wSTL was proposed to accommodate user preferences for importance and priorities over concurrent and sequential tasks as well as satisfaction times denoted by weights over the logical and temporal operators, respectively. We propose a Mixed Integer Linear Programming (MILP) based approach for synthesis with wSTL specifications. These specifications have the same qualitative semantics as STL but differ in their quantitative semantics, which is recursively modulated with weights. Additionally, we extend the formal definition of wSTL to include the semantics for until and release temporal operators and present an efficient encoding for these operators in the MILP formulation. As opposed to the original implementation of wSTL, where the arithmetic-geometric mean robustness was used with gradient-based methods prone to local optima, our encoding allows the use of a weighted version of traditional robustness and efficient global MILP solvers. We demonstrate the operational performance of the proposed formulation using multiple case studies, showcasing the distinct functionalities over Boolean and temporal operators. Moreover, we elaborate on multiple case studies for synthesizing controllers for an agent navigating a non-convex environment under different constraints highlighting the difference in synthesized control plans for STL and wSTL. Finally, we compare the time and complexity performance of encodings for STL and wSTL.
Probiotic potential of lactic acid bacteria isolated from colostrum of 3 different mammals

ABSTRACT This study aimed to investigate the in vitro properties of lactic acid bacteria isolated from colostrum samples of goat, sheep and cow. Four isolates of lactic acid bacteria identified through 16S rDNA sequencing were screened for probiotic potential including their enzymatic activities (hemolytic, DNase activity, API ZYM profiling and tolerance to the gastrointestinal conditions), their antimicrobial activities, their adhesion properties through biofilm formation capacity, auto-aggregation and co-aggregation with pathogens bacteria and hydrophobicity. They were also tested for their antibiotic susceptibility and antioxidant power. All the strains (ADG1b Lactobacillus gasseri, ZPana Lactiplantibacillus plantarum, S2ana Enterococcus hirae and ZGana Lactobacillus gasseri) demonstrated probiotic properties and they could resist different gastrointestinal conditions. They displayed good antimicrobial activities. Furthermore, they had high antioxidant power, although strain dependent. The results of this study showed that L. gasseri (ADG1b) is the most promising probiotic.
Addressing the paradox of tolerance in liberal democracies: why do France and Germany respond differently to right-wing radicalism?

ABSTRACT This article presents a comparative analysis of how European democracies address the paradox of tolerance. States have formulated various responses to political radicalism, in particular right-wing radicalism. Scholars have explained these variations by the differences in their authoritarian pasts. However, a wide array of policy actors with different perceptions and interests are involved in the regulation of right-wing radicalism. Their impact on implemented policy measures needs to be assessed to better understand what drives the politics of democratic defense in liberal democracies. Relying on grey literature, administrative reports and semi-structured interviews conducted between 2013 and 2017 in France and Germany among a range of policy actors responding to right-wing radicalism, I identify two key factors to explain the regulation of right-wing radicalism. This article demonstrates that the historical framing of right-wing radicalism and the national specificities in policymaking need to be explored if we are to understand the regulation dynamics of right-wing radicalism. By contrast, the radical right’s structure is of secondary importance in explaining how liberal democracies address the paradox of tolerance. These results advocate for the systematic use of policy tools in analyzing the politics of democratic defense.
Understanding Teachers’ Uncertainty in Encounters with Pupils with Experiences of Domestic Violence

ABSTRACT Political, legal, and educational documents present expectations for teachers who encounter pupils with experiences of domestic violence. Teachers say they lack competence and confidence and experience uncertainty about their role in this area. This study sheds light on teachers’ experiences of uncertainty in their encounters with these pupils. Data was collected in group conversations with ten teachers and coded thematically. Hoekstra & Korthagen and Lauvås & Handal concepts of trusted colleague/peer mentoring together with Bandura’s self-efficacy theory were used as a framework. Three challenges generating teachers’ experiences of uncertainty: marginalized opportunities, “crumbled” support channels and being out of the information trail. When concerned about a pupil, teachers rarely sought information online, tending instead to seek support from a trusted colleague or a supervisor. They mentioned the need to talk to somebody who knew the pupil and the need for external personal guidance from professionals of an independent state/municipal service.
A Framework for Biodiversity Image Analysis using Machine Learning and Crowdsourcing Knowledge

This paper proposes a data-driven methodology framework to enrich existing biodiversity data by collecting crowd-sourced knowledge contributed by citizens in the form of photographs. The framework aims to provide the design of an easy-to-use web interface tool that clusters biodiversity images from multiple data sources. Through its design it will provide the users the ability to identify species by uploading photos to this tool.
Ensuring Software Quality through Videos in Requirements Engineering

Complex software systems are drafted, specified, and implemented in cooperation of all stakeholders. A shared understanding of requirements is important in all phases. However, requirements engineers, customers, developers, testers, and other stakeholders do not have a shared understanding of requirements due to insufficient communication. To solve this problem, this paper proposes to use videos to facilitate stakeholder involvement in requirements engineering. The basic idea is to use vision videos and test videos in requirements engineering with the help of testing techniques. A vision video visualises a future software system with a motivation and an envisioned solution. Requirement engineers use vision videos to elicit testable requirements from stakeholders and then validate the elicited requirements. A test video shows test executions of a software under development. Developers use test videos for (1) clarification of misunderstood requirements among development team and (2) eliciting new requirements and collecting feedback from stakeholders. Experiments and case studies are planned to check the effectivity or efficiency of these methods. The testability of a software is shaped at the beginning through defining testable requirements. During development, these requirements are updated through stakeholders’ feedback. Hence, using video in requirements engineering ensures software quality.
Strategies for creating knowledge graphs to depict a multi-perspective Queer communities representation

This paper introduces an experimental study for building knowledge graphs about queer communities with different perspectives. The paper describes a set of pipelines implementing several knowledge graphs construction strategies that lead to a complementary understanding of the queer communities. We implemented a library with the knowledge graph construction pipelines and conducted experiments to evaluate execution cost against the comprehensiveness of the content. The library includes visualization tools for observing the knowledge graphs concerning several granularities. Finally, we use storytelling to provide a first interpretation of our results regarding the profiling of queer communities.
This paper is included in the Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation.

Datacenters waste signiﬁcant compute and memory resources today because they lack resource fungibility : the ability to reassign resources quickly and without disruption. We propose logical processes , a new abstraction that splits the classic UNIX process into units of state called proclets . Proclets can be migrated quickly within datacenter racks, to provide fungibility and adapt to the memory and compute resource needs of the moment. We prototype logical processes in Nu, and use it to build three different applications: a social network application, a MapReduce system, and a scalable key-value store. We evaluate Nu with 32 servers. Our evaluation shows that Nu achieves high efﬁciency and fungibility: it migrates proclets in ≈ 100 µs ; under intense resource pressure, migration causes small disruptions to tail latency—the 99.9 th percentile remains below or around 1ms—for a duration of 0.54–2.1s, or a modest disruption to throughput (<6%) for a duration of 24–37ms, depending on the application.
HALP: Heuristic Aided Learned Preference Eviction Policy for YouTube Content Delivery Network

Video streaming services are among the largest web applications in production, and a large source of downstream internet trafﬁc. A large-scale video streaming service at Google, YouTube, leverages a Content Delivery Network (CDN) to serve its users. A key consideration in providing a seam-less service is cache efﬁciency. In this work, we demonstrate machine learning techniques to improve the efﬁciency of YouTube’s CDN DRAM cache. While many recently proposed learning-based caching algorithms show promising results, we identify and address three challenges blocking deployment of such techniques in a large-scale production environment: computation overhead for learning, robust byte miss ratio improvement, and measuring impact under production noise. We propose a novel caching algorithm, HALP, which achieves low CPU overhead and robust byte miss ratio improvement by augmenting a heuristic policy with machine learning. We also propose a production measurement method, impact distribution analysis, that can accurately measure the impact distribution of a new caching algorithm deployment in a noisy production environment. HALP has been running in YouTube CDN production as a DRAM level eviction algorithm since early 2022 and has reliably reduced the byte miss during peak by an average of 9.1% while expending a modest CPU overhead of 1.8%.
Ethanol’s interaction with BK channel α subunit residue K361 does not mediate behavioral responses to alcohol in mice

Large conductance potassium (BK) channels are among the most sensitive molecular targets of ethanol and genetic variations in the channel-forming α subunit have been nominally associated with alcohol use disorders. However, whether the direct action of ethanol at BK α influences the motivation to drink alcohol remains to be determined. In the present study, we sought to investigate the behavioral relevance of this molecular interaction by introducing in the mouse genome a point mutation known to render BK channels insensitive to ethanol while preserving their physiological function. The BK α K361N substitution prevented ethanol from reducing spike threshold in medial habenula neurons. However, it did not alter acute responses to ethanol in vivo, including ataxia, sedation, hypothermia, analgesia, and conditioned place preference. Furthermore, the mutation did not have reproducible effects on alcohol consumption in limited, continuous, or intermittent access home cage two-bottle choice paradigms conducted in both males and females. Notably, in contrast to previous observations made in mice missing BK channel auxiliary β subunits, the BK α K361N substitution had no significant impact on ethanol intake escalation induced by chronic intermittent alcohol vapor inhalation. It also did not affect the metabolic and locomotor consequences of chronic alcohol exposure. Altogether, these data suggest that the direct interaction of ethanol with BK α does not mediate the alcohol-related phenotypes examined here in mice.
This paper is included in the Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation.

On average, every 10 days a child dies from in-vehicle heat-stroke. The life-threatening situation calls for an automatic Child Presence Detection (CPD) solution to prevent these tragedies. In this paper, we present V E C ARE , the ﬁrst CPD system that leverages existing in-car audio without any hardware changes. To achieve so, we explore the fundamental properties of acoustic reﬂection signals and develop a novel paradigm of statistical acoustic sensing , which allows to detect motion, track breathing, and estimate speed in a uniﬁed model. Based on this, we build an accurate and robust CPD system by introducing a set of techniques that overcome multiple challenges concerning sound interference and sensing coverage. We implement V E C ARE using commodity speakers and a single microphone and conduct experiments with infant simulators and adults, as well as 15 young children for the real-world in-car study. The results demonstrate that V E C ARE achieves an average detection rate of 98.8% with a false alarm rate of 2.1% for 15 children in various cars, boosting the coverage by over 2.3 × compared to state-of-the-art and achieving whole-car detection with no blind spot.
Mining Data Wrangling Workflows for Patterns, Reuse and Optimisation Opportunities

Data wrangling (DW) is the complex process associated with preparation of raw data for analysis. It is typically performed as a craft in an ad hoc manner and its level of complexity and success is highly dependent on the data analysis task at hand, quality of input data, and skill set of the data analyst. This makes the reuse of DW pipelines difficult, forcing data analysts to often devise a new pipeline for each combination of data and analysis tasks, a process that is not only complex but also expensive, consuming between 50 to 80% of even the most experienced data analyst’s time. In this paper, we investigate a number of DW pipelines in the form of workflows to find commonalities or patterns in the way DW is performed in practice, considering a multitude of data analysis tasks and data sets, devised by data analysts with varying levels of experience. We present our investigation as a methodology that, from selection of workflow sources to workflow mining techniques, describes how we dealt with the challenges of finding patterns in the way people prepare data for analysis, given the general lack of guidelines for best practices and standards. The obtained results provide insights into the most commonly used DW operations, solution patterns, redundancies and, not only optimisation opportunities, but also opportunities for reuse of experience and best practices in data engineering. We believe that the obtained insights can be useful in facilitating the construction of DW solutions to inexperienced data analysts via the reuse of patterns and best practices in DW.
RF-Bouncer: A Programmable Dual-band Metasurface for Sub-6 Wireless Networks

Offloading the beamforming task from the endpoints to the metasurface installed in the propagation environment has attracted significant attention. Currently, most of the metasurface-based beamforming solutions are designed and optimized for operation on a single ISM band (either 2.4 GHz or 5 GHz). In this paper, we propose RF-Bouncer, a compact, low-cost, simple-structure programmable dual-band metasur-face that supports concurrent beamforming on two Sub-6 ISM bands. By configuring the states of the meta-atoms, the metasurface is able to simultaneously steer the incident signals from two bands towards their desired departure angles. We fabricate the metasurface and validate its performance via extensive experiments. Experimental results demonstrate that RF-Bouncer achieves 15.4 dB average signal strength improvement and a 2 . 49 × throughput improvement even with a relatively small 16 × 16 array of meta-atoms.
Impairments in hippocampal oscillations accompany the loss of LTP induced by GIRK activity blockade

Learning and memory occurrence requires of hippocampal long-term synaptic plasticity and a precise neural activity orchestrated by brain network oscillations, both processes reciprocally influencing each other. As G-protein-gated inwardly rectifying potassium (GIRK) channels rule synaptic plasticity that supports hippocampal-dependent memory, here we assessed their unknown role in hippocampal oscillatory activity in relation to synaptic plasticity induction. In alert male mice, pharmacological GIRK modulation did not alter neural oscillations before long-term potentiation (LTP) induction. However, after an LTP generating protocol, both gain- and loss-of basal GIRK activity transformed LTP into long-term depression, but only specific suppression of constitutive GIRK activity caused a disruption of network synchronization (δ, α, γ bands), even leading to long-lasting ripples and fast ripples pathological oscillations. Together, our data showed that constitutive GIRK activity plays a key role in the tuning mechanism of hippocampal oscillatory activity during long-term synaptic plasticity processes that underlies hippocampal-dependent cognitive functions.
Atomistic ensemble of active SHP2 phosphatase

The heterogeneous structural ensemble of the constitutively active SHP2^E76K mutant has been determined by means molecular dynamics simulations in agreement with experimental data from small-angle X-ray scattering. SHP2 phosphatase plays an important role in regulating several intracellular signaling pathways. Pathogenic mutations of SHP2 cause developmental disorders and are linked to hematological malignancies and cancer. SHP2 comprises two tandemly-arranged SH2 domains, a catalytic PTP domain, and a disordered C-terminal tail. Under physiological, non-stimulating conditions, the catalytic site of PTP is occluded by the N-SH2 domain, so that the basal activity of SHP2 is low. Whereas the autoinhibited structure of SHP2 has been known for two decades, its active, open structure still represents a conundrum. Since the oncogenic mutant SHP2^E76K almost completely populates the active, open state, this mutant has been extensively studied as a model for activated SHP2. By molecular dynamics simulations and accurate explicit-solvent SAXS curve predictions, we present the heterogeneous atomistic ensemble of constitutively active SHP2^E76K in solution, encompassing a set of conformational arrangements and radii of gyration in agreement with experimental SAXS data.
Age-related changes in the neural processing of semantics, within and beyond the core semantic network

Ageing is associated with increases in functional activation, which have been interpreted either as compensatory responses to the higher task demands older people experience, or as neural dedifferentiation. Ageing is also characterised by a shift to greater reliance on prior knowledge and less on executive function, whose underlying neural mechanism is poorly understood. This pre-registered fMRI study investigated these questions within the domain of semantic cognition. To disentangle the compensation and dedifferentiation theories, we extracted activation signal in core verbal semantic regions, for young and older participants during semantic tasks. Verbal semantic processing relies heavily on left inferior frontal gyrus (IFG) but older people frequently show additional right IFG activation. We found that right IFG exhibited a similar linear activation-demand relationship as left IFG across age groups and semantic tasks, indicating that age-related over-recruitment of this region may be compensatory in nature. To answer the second question, we examined network-level activity and connectivity changes in semantic and non-semantic tasks. Older people showed more engagement of the default mode network (DMN) and less of the executive multiple demand network (MDN) aligning with their greater reserves of prior knowledge and declined executive control. In contrast, activation was age-invariant in regions contributing specifically to executive control of semantic processing. Older adults also showed a degraded ability to modulate MDN activation as a function of demand in the non-semantic task, but not in the semantic tasks. These findings provide a new perspective on the neural basis of semantic cognition in later life, and suggest that preservation of activation in specialised semantic networks may support preserved performance in this critical domain.