Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Management for Mixed-Criticality Systems on Multi-core Platforms with Focus on Communication

The System-on-Chip revolution has not impacted the field of safety-critical systems as widely as the rest of the electronics market. However, it is nowadays acknowledged that sharing resources between applications of different criticality is a key leverage to reduce costs and improve performance. Certification of systems hosting such mixed-criticality application sets requires sufficient isolation between criticality levels, i.e. a low-critical task must not cause a fault in a high-critical task, especially a temporal fault, which is likely when several co-running tasks compete for a shared resource. This digest describes implementation schemes which can be included, at both hardware and software levels, in mixed-critical systems to enforce such isolation.
The Efficacy of Frontline Near‐Peer Teaching in a Modern Medical Curriculum

Within medical education a reduction in curriculum time for subjects, such as anatomy puts pressure on educators to ensure the same learning outcomes are conveyed in less time. This has the potential to impact negatively on student experience. Near‐peer teaching (NPT) is often praised as an effective revision tool, but its use as a frontline teaching resource remains unreported. The study explores the potential for NPT to promote delivery of learning outcomes and maximize student experience within a neuroanatomy module for second year medical students. The study occurred in three educational settings, (1) frontline NPT of cranial nerves, (2) revision session NPT of cranial nerves, and (3) NPT alongside faculty staff in laboratory‐based neuroanatomy practical exercises. For the first and second components, knowledge was measured using a pre‐ and post‐session test and student perception was ascertained with a questionnaire. For the third component, student perception was assessed with an end‐of‐module survey. The results show that overall, NPT was well received by learners. A significant knowledge gain was seen between the pre‐ and post‐session test of the frontline NPT session. The study presents evidence in favor of using NPTs to supplement the delivery of learning outcomes in a time and resource constrained curriculum. In particular, for the effective delivery of frontline material. Anat Sci Educ 0: 1–9. © 2018 American Association of Anatomists.
Learning Battles in ViZDoom via Deep Reinforcement Learning

First-person shooter (FPS) video games play an important role in game artificial intelligence (AI). In this paper, we present an effective deep reinforcement learning (DRL) method to learn battles in ViZDoom. Our approach utilizes the actor-critic with Kronecker-factored trust region (ACKTR), a sample-efficient and computationally inexpensive DRL method. We train our ACKTR agents in two battle scenarios, and compare with the advantage actor-critic (A2C) baseline agent. The experimental results demonstrate that DRL methods successfully teach agents to battle in these scenarios. In addition, the ACKTR agents significantly outperform the A2C agents in terms of all the metrics by a significant margin.
Karaoke: Distributed Private Messaging Immune to Passive Traffic Analysis

Karaoke is a system for low-latency metadata-private communication. Karaoke provides di ff erential privacy guarantees, and scales better with the number of users than prior such systems (Vuvuzela and Stadium). Karaoke achieves high performance by addressing two challenges faced by prior systems. The first is that di ff erential privacy requires continuously adding noise messages, which leads to high overheads. Karaoke avoids this using optimistic indistinguishability : in the common case, Karaoke reveals no information to the adversary, and Karaoke clients can detect precisely when information may be revealed (thus requiring less noise). The second challenge lies in generating su ffi cient noise in a distributed system where some nodes may be malicious. Prior work either required each server to generate enough noise on its own, or used expensive verifiable shu ffl es to prevent any message loss. Karaoke achieves high performance using e ffi cient noise verification , generating noise across many servers and using Bloom filters to e ffi ciently check if any noise messages have been discarded. These techniques allow our prototype of Karaoke to achieve a latency of 6.8 seconds for 2M users. Overall, Karaoke’s latency is 5 × to 10 × better than Vuvuzela and Stadium.
An Event-Based Cochlear Filter Temporal Encoding Scheme for Speech Signals

Spiking Neural Network (SNN), the third generation of neural networks, has been shown to perform well in pattern recognition tasks involving temporal information, such as speech recognition and motion detection. However, most neural networks, including the SNN, for speech recognition rely on short-time frequency analysis, such as the mel-frequency cepstral coefficients (MFCC), for low-level feature extraction. MFCC feature extraction works by analyzing a window of time signal in multiple frequency bands one window at a time, in a synchronous fashion. This is in contrast to the event-based principle of SNN, whereby electrical impulses are emitted and processed in an asynchronous fashion. Just as speech signals arrive at the human's cochlear filterbank concurrently, but spikes encoding the power in each frequency band are emitted asynchronously, we propose an event-based cochlear filter encoding scheme, whereby the power in each frequency band is directly extracted in the time domain and spikes encoded using the latency code are emitted asynchronously to represent the power of each frequency band. This replaces the traditional MFCC frontend used in most speech recognition models, and makes possible an end-to- end event-based SNN implementation for a speech recognition task. The proposed event-based neural encoding is not only biologically plausible, but also outperforms the MFCC as an encoding frontend for an SNN classifier in a speech recognition task, in terms of higher classification accuracy and lower latency. Such an end-to-end SNN model could be implemented on a neuromorphic chip to fully realize the advantages of event-based processing.
Off-Policy Integral Reinforcement Learning for Semi-Global Constrained Output Regulation of Continuous-Time Linear Systems

This paper presents a data-driven method based on off-policy integral reinforcement learning to solve the semi-global output regulation of continuous-time linear systems with input saturation. A family of state feedback laws for the input constrained output regulation problem is designed based on solving an algebraic Riccati equation. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from online implementation is efficiently utilized to design the controller. Therefore, the controller design in this paper is data-driven. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and stabilize a given linear system with all its poles inside or on the imaginary axis. Finally, a simulation example is conducted to show the validity of the presented approach to solve the semi-global output regulation of continuous-time linear systems with input saturation.
LWPTool: A Lightweight Profiler to Guide Data Layout Optimization

Memory access latency continues to be a dominant bottleneck in a large class of applications on modern architectures. To optimize memory performance, it is important to utilize the locality in the memory hierarchy. Data layout optimization can significantly improve memory locality. However, pinpointing inefficient code and providing insightful guidance for data layout optimization is challenging. Existing tools typically leverage heavyweight memory instrumentations, which hinders the applicability of these tools for real long-running programs. To address this issue, we develop LWPTool, a profiler to pinpoint top candidates that benefit from data layout optimization. LWPTool makes three unique contributions. First, it adopts lightweight address sampling to collect and analyze memory traces. Second, LWPTool employs a set of novel methods to determine memory access patterns to guide data layout optimization. We also formally prove that our method has high accuracy even with sparse memory access samples. Third, LWPTool scales on multithreaded machines. LWPTool works on fully optimized, unmodified binary executables independently from their compiler and language, incurring around 6.2 percent runtime overhead. To evaluate LWPTool, we study ten sequential and parallel benchmarks. With the guidance of LWPTool, we are able to significantly improve all these benchmarks; the speedup is up to 1.39× on average.
Exploring Subsethood to Determine Firing Strength in Non-Singleton Fuzzy Logic Systems

Real world environments face a wide range of sources of noise and uncertainty. Thus, the ability to handle various uncertainties, including noise, becomes an indispensable element of automated decision making. Non-Singleton Fuzzy Logic Systems (NSFLSs) have the potential to tackle uncertainty within the design of fuzzy systems. The firing strength has a significant role in the accuracy of FLSs, being based on the interaction of the input and antecedent fuzzy sets. Recent studies have shown that the standard technique for determining firing strengths risks substantial information loss in terms of the interaction of the input and antecedents. Recently, this issue has been addressed through exploration of alternative approaches which employ the centroid of the intersection (cen-NS) and the similarity (sim-NS) between input and antecedent fuzzy sets. This paper identifies potential shortcomings in respect to the previously introduced similarity-based NSFLSs in which firing strength is defined as the similarity between an input FS and an antecedent. To address these shortcomings, this paper explores the potential of the subsethood measure to generate a more suitable firing level (sub-NS) in NSFLSs featuring various noise levels. In the experiment, the basic waiter tipping fuzzy logic system is used to examine the behaviour of sub-NS in comparison with the current approaches. Analysis of the results shows that the sub-NS approach can lead to more stable behaviour in real world applications.
Comparison of Predictor of Desaturation Disorders and Daytime Sleepiness Based On Epworth Sleepiness Scale and STOP-BANG Questionnaires in Mild to Moderate Obstructive Sleep Apnea Patients

BACKGROUND: Obstructive sleep apnea (OSA) is characterised by recurrence in upper airway obstruction during sleep. AIM: This study aimed to compare the predictive values of the Epworth Sleepiness Scale (ESS) and STOP-BANG in the desaturation of patients with mild to moderate obstructive apnea based on the apnea-hypopnea index (AHI) scale. METHODS: A group of 79 patients (43 male and 36 female) were selected. The suspected patients were introduced to the sleep clinic, and the ESS and STOP-BANG questionnaires were filled up, then subjected to polysomnography test, and the scores of the disease were also determined based on an apnoea-hypopnoea index (AHI). Finally, the desaturation rate (SO2 < 3% based on the baseline) and desaturation index were determined in patients. Consequently, the finding was compared with the results of the questionnaires. RESULTS: Patients with STOP-BANG score above 3 had significantly higher weight, oxygen desaturation index (ODI) index and average desatu, while peripheral capillary oxygen saturation (SpO2) base and average SpO2 were lower than those with scores below 3 (P < 0.05). However, there was no significant difference between the patients with the ESS questionnaire score above 10 and below 10 (P > 0.05). CONCLUSION: The results of these two questionnaires reflect the unsaturated oxygen index in the blood, and can be considered for the evaluation of the severity of the disease.
Light Field Coding With Field-of-View Scalability and Exemplar-Based Interlayer Prediction

Light field imaging based on microlens arrays—a.k. a. holoscopic, plenoptic, and integral imaging—has currently risen up as a feasible and prospective technology for future image and video applications. However, deploying actual light field applications will require identifying more powerful representations and coding solutions that support arising new manipulation and interaction functionalities. In this context, this paper proposes a novel scalable coding solution that supports a new type of scalability, referred to as field-of-view scalability. The proposed scalable coding solution comprises a base layer compliant with the High Efficiency Video Coding (HEVC) standard, complemented by one or more enhancement layers that progressively allow richer versions of the same light field content in terms of content manipulation and interaction possibilities. In addition, to achieve high-compression performance in the enhancement layers, novel exemplar-based interlayer coding tools are also proposed, namely: 1) a direct prediction based on exemplar texture samples from lower layers and 2) an interlayer compensated prediction using a reference picture that is built relying on an exemplar-based algorithm for texture synthesis. Experimental results demonstrate the advantages of the proposed scalable coding solution to cater to users with different preferences/requirements in terms of interaction functionalities, while providing better rate-distortion performance (independently of the optical setup used for acquisition) compared to HEVC and other scalable light field coding solutions in the literature.
An Improvement of H∞ Filtering Design for T-S fuzzy Time-Varying Delay Systems: Input-Output Approach

This paper discusses the problem of delay-dependent H∞ filtering design for T-S fuzzy time-varying delay systems. By considering an input-output approach, a new model transformation is applied by employing a three-term approximation, which has a smaller approximation error than the two-term approximation. Thus, based on the scaled small gain Theorem (SSG) combined with the quadratic Lyapunov Function approach, the H∞ full- and reduced-order filter are designed in terms of linear matrix inequalities (LMIs) via simple linearization technique. Finally, an illustrative example is presented to demonstrate the validity of the proposed methods.
Matrix resemblance functions for image comparison

In this work we present a neighbourhood-based image comparison algorithm that makes use of the class of matrix resemblance functions. Based on the concepts of restricted equivalence functions and inclusion grade in the terms of Sinha and Dougherty, we discuss two construction methods for matrix equivalence functions. Moreover, we study the conditions under which this class of functions satisfy a set of properties that are favourable for image comparison operators. We conclude this work with an instance of a potential application for this proposal in video motion detection.
Pocket: Elastic Ephemeral Storage for Serverless Analytics

Serverless computing is becoming increasingly popular, enabling users to quickly launch thousands of short-lived tasks in the cloud with high elasticity and fine-grain billing. These properties make serverless computing appealing for interactive data analytics. However exchanging intermediate data between execution stages in an analytics job is a key challenge as direct communication between serverless tasks is difficult. The natural approach is to store such ephemeral data in a remote data store. However, existing storage systems are not designed to meet the demands of serverless applications in terms of elasticity, performance, and cost. We present Pocket, an elastic, distributed data store that automatically scales to provide applications with desired performance at low cost. Pocket dynamically rightsizes resources across multiple dimensions (CPU cores, network bandwidth, storage capacity) and leverages multiple storage technologies to minimize cost while ensuring applications are not bottlenecked on I/O. We show that Pocket achieves similar performance to ElastiCache Redis for serverless analytics applications while reducing cost by almost 60%.
Egg consumption is associated with a lower risk of type 2 diabetes in middle-aged and older men

BACKGROUND/OBJECTIVES This prospective study examined gender-specific associations between egg intake and the risk of developing type 2 diabetes using data from a large-scale cohort study. SUBJECTS/METHODS A total of 7,002 Korean adults (40–69 years) without type 2 diabetes at baseline were analyzed. Dietary intake was evaluated by a food frequency questionnaire administered at baseline (2001–2002) and the second follow-up examination (2005–2006). Type 2 diabetes was diagnosed as a fasting glucose concentration ≥ 126 mg/dL or current use of glucose-lowering medications or insulin injection. Multivariate Cox proportional hazard models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for incident type 2 diabetes according to egg consumption or cholesterol intake. RESULTS During a 14-year follow up period, 857 subjects developed type 2 diabetes. In men, frequent egg intake (2- < 4 servings/week) was associated with a 40% lower risk of developing type 2 diabetes than infrequent egg intake (0- < 1 serving/week) (HR = 0.60, 95% CI: 0.37–0.97), whereas no association between egg intake and incidence of type 2 diabetes was observed in women (HR = 0.61, 95% CI: 0.27–1.37). There was no association between cholesterol intake and risk of incident type 2 diabetes in either men or women. CONCLUSIONS Egg consumption was inversely related to the risk of incident type 2 diabetes in men, but not in women, suggesting gender differences in the relationship between diet and disease risk.
Identification of Novel Scaffolds with Dual Role as Antiepileptic and Anti-Breast Cancer

Aromatase inhibitors with an $\mathrm{IC}_{50}$ IC 50 value ranging from 1.4 to 49.7 µM are known to act as antiepileptic drugs besides being potential breast cancer inhibitors. The aim of the present study is to identify novel antiepileptic aromatase inhibitors with higher activity exploiting the ligand-based pharmacophore approach utilizing the experimentally known inhibitors. The resultant Hypo1 consists of four features and was further validated by using three different strategies. Hypo1 was allowed to screen different databases to identify lead molecules and were further subjected to Lipinski's Rule of Five and ADMET to establish their drug-like properties. Consequently, the obtained 68-screened molecules were subjected to molecular docking by GOLD v5.2. 2. Furthermore, the compounds with the highest dock scores were assessed for molecular interactions. Later, the MD simulation was applied to evaluate the protein backbone stabilities and binding energies adapting GROMACS v5.0. 6 and MM/PBSA which was followed by the density functional theory (DFT), to analyze their orbital energies, and further the energy gap between them. Eventually, the number of Hit molecules was culled to three projecting Hit1, Hit2, and Hit3 as the potential lead compounds based on their highest dock scores, hydrogen bond interaction, lowest energy gap, and the least binding energies and stable MD results.
Guided Deep Reinforcement Learning in the GeoFriends2 Environment

In recent years, the artificial intelligence community has taken big strides in the application of reinforcement learning to games or similar environments using deep learning. From Atari to board games, including motor control or riddle solving, fairly generic deep learning algorithms can now achieve great policies by simply learning to play from experience, and minimal knowledge of the specific domain. However, these algorithms are very demanding in terms of time and hardware in order to achieve the results reported in the literature. So much so, that some algorithms would take years to achieve state-of-the-art performance in commodity hardware. Not only that, but even the learning environments can hinder the speed of the learning process, if they have not been performance optimized. In this paper, we evaluate a complex existing environment, and propose a performance-oriented version, which we call GeoFriends2. We describe the motivation behind the creation of our version, and how it is suitable for both single- and multi-agent reinforcement learning. We then use Asynchronous Deep Learning to create complex policies that can act as baselines for future research on this environment. We also describe a set of techniques that speed up the learning process such that tests can be run with commodity hardware in hours, and not weeks, and using much simpler network architectures.
A global synthesis of the small-island effect in habitat islands

Habitat loss and fragmentation are considered to be the leading drivers of biodiversity loss. The small-island effect (SIE) can be used to predict species extinctions resulting from habitat loss and has important implications for species conservation. However, to date, no study has explicitly evaluated the prevalence of SIEs in habitat islands. Here, we compiled 90 global datasets to systematically investigate the prevalence and underlying factors determining the ubiquity of SIEs in habitat island systems. Among the 90 datasets, SIEs were unambiguously detected in 36 cases. We found significant effects of habitat island types and taxon groups on the threshold area of SIEs. The number of islands, area range, species range, island type and taxon group were key variables that determined the prevalence of SIEs. Our study demonstrates that SIEs occur in 40% of cases and thus are common in habitat islands. We conclude that conservation biologists and applied ecologists should consider the prevalence of SIEs when making management strategies in fragmented landscapes.
Adversarial mRMR against Evasion Attacks

Machine learning (ML) algorithms provide a good solution for many security sensitive applications, they themselves, however, face the threats of adversary attacks. As a key problem in machine learning, how to design robust feature selection algorithms against these attacks becomes a hot issue. The current researches on defending evasion attacks mainly focus on wrapped adversarial feature selection algorithm, i.e., WAFS, which is dependent on the classification algorithms, and time cost is very high for large-scale data. Since mRMR (minimum Redundancy and Maximum Relevance) algorithm is one of the most popular filter algorithms for feature selection without considering any classifier during feature selection process. In this paper, we propose a novel adversary-aware feature selection algorithm under filter model based on mRMR, named FAFS. The algorithm, on the one hand, takes the correlation between a single feature and a label, and the redundancy between features into account; on the other hand, when selecting features, it not only considers the generalization ability in the absence of attack, but also the robustness under attack. The performance of four algorithms, i.e., mRMR, TWFS (Traditional Wrapped Feature Selection algorithm), WAFS, and FAFS is evaluated on spam filtering and PDF malicious detection in the Perfect Knowledge attack scenarios. The experiment results show that FAFS has a better performance under evasion attacks with less time complexity, and comparable classification accuracy.