Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Multi-valued Literal Using Lukasiewicz Logic

The synthesis of multi-valued combinational functions is well-studied topic, albeit less compared to the synthesis of two-valued logic families. The synthesis of multi-valued functions consists of bi-decomposition or functional decomposition of the given target function to obtain a multilevel network comprising of min and max gates. Synthesis tools, such as YADE and those based on Multiple-Valued Decision Diagrams make the implicit assumption regarding the availability of literals or CASE operator, while focusing on the optimization of the logic network solely based on the min and max gates. However, a literal cannot be assumed to exist as a primitive in a multi-valued logic system and therefore, renders it difficult for one to directly apply the existing synthesis flows in practical settings [1]. We address this important gap in MVL synthesis flows. Our target multivalued logic is £ukasiewicz logic, which supports implication and negation. We derive literals and CASE operators using these primitives, and propose a heuristic algorithm to synthesize it automatically. Our techniques are implemented as an extension in the YADE tool. Our experimental studies on a wide range of benchmarks reveal that an average overhead of 216% in terms of number implication gates, along with 55% increase in the number of levels is encountered, in contrast to a synthesis flow that assumes existence of literals.
Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion

Running is extremely popular and around 10.6 million people run regularly in the United States alone. Unfortunately, estimates indicated that between 29% to 79% of runners sustain an overuse injury every year. One contributing factor to such injuries is excessive fatigue, which can result in alterations in how someone runs that increase the risk for an overuse injury. Thus being able to detect during a running session when excessive fatigue sets in, and hence when these alterations are prone to arise, could be of great practical importance. In this paper, we explore whether we can use machine learning to predict the rating of perceived exertion (RPE), a validated subjective measure of fatigue, from inertial sensor data of individuals running outdoors. We describe how both the subjective target label and the realistic outdoor running environment introduce several interesting data science challenges. We collected a longitudinal dataset of runners, and demonstrate that machine learning can be used to learn accurate models for predicting RPE.
Microdream neurophenomenology

Abstract Nightly transitions into sleep are usually uneventful and transpire in the blink of an eye. But in the laboratory these transitions afford a unique view of how experience is transformed from the perceptually grounded consciousness of wakefulness to the hallucinatory simulations of dreaming. The present review considers imagery in the sleep-onset transition—“microdreams” in particular—as an alternative object of study to dreaming as traditionally studied in the sleep lab. A focus on microdream phenomenology has thus far proven fruitful in preliminary efforts to (i) develop a classification for dreaming’s core phenomenology (the “oneiragogic spectrum”), (ii) establish a structure for assessing dreaming’s multiple memory inputs (“multi-temporal memory sources”), (iii) further Silberer’s project for classifying sleep-onset images in relation to waking cognition by revealing two new imagery types (“autosensory imagery,” “exosensory imagery”), and (iv) embed a potential understanding of microdreaming processes in a larger explanatory framework (“multisensory integration approach”). Such efforts may help resolve outstanding questions about dream neurophysiology and dreaming’s role in memory consolidation during sleep but may also advance discovery in the neuroscience of consciousness more broadly.
In Production Performance Testing of SDN Control Plane for Telecom Operators

One of the biggest emerging challenges for telco operators is to dynamically create new services while maintaining the network at its optimal performance/revenue break point. To this end, operators are moving to a much leaner cloud-based Software Defined Network (SDN) infrastructure to achieve a truly programmable network fabric. While cloud services can be provisioned in seconds and tested in-production, service provisioning in SDNs still lasts many weeks and requires substantial manual effort. A large part of this service creation time can be attributed to testing and tuning the control plane. In this paper we present SCP-CLUB (SDN Control Plane CLoUd-based Benchmarking), a platform for in-production performance testing of telco operator SDNs, offering a level of automation as available in deploying cloud services. Telco cloud SDN performance testing with SCP-CLUB focuses on the analysis of how design choices in the cloud and SDN control planes influence SLA metrics like throughput and latency. We describe the SCP-CLUB architecture and its performance testing support capabilities. Experiments are performed on an SDN telco cloud built to demonstrate SCP-CLUB under production load conditions.
Automatically Classifying Posts Into Question Categories on Stack Overflow

Software developers frequently solve development issues with the help of question and answer web forums, such as Stack Overflow (SO). While tags exist to support question searching and browsing, they are more related to technological aspects than to the question purposes. Tagging questions with their purpose can add a new dimension to the investigation of topics discussed in posts on SO. In this paper, we aim to automate such a classification of SO posts into seven question categories. As a first step, we have manually created a curated data set of 500 SO posts, classified into the seven categories. Using this data set, we apply machine learning algorithms (Random Forest and Support Vector Machines) to build a classification model for SO questions. We then experiment with 82 different configurations regarding the preprocessing of the text and representation of the input data. The results of the best performing models show that our models can classify posts into the correct question category with an average precision and recall of 0.88 and 0.87 when using Random Forest and the phrases indicating a question category as input data for the training. The obtained model can be used to aid developers in browsing so discussions or researchers in building recommenders based on SO.
Infrastructure-as-Code for Data-Intensive Architectures: A Model-Driven Development Approach

As part of the DevOps tactics, Infrastructure-as-Code (IaC) provides the ability to create, configure, and manage complex infrastructures by means of executable code. Writing IaC, however, is not an easy task, since it requires blending different infrastructure programming languages and abstractions, each specialized on a particular aspect of infrastructure creation, configuration, and management. Moreover, the more the architectures become large and complex (e.g. Data-Intensive or Microservice-based architectures), the more dire the need of IaC becomes. The goal of this paper is to exploit Model-Driven Engineering (MDE) to create language-agnostic models that are then automatically transformed into IaC. We focus on the domain of Data-Intensive Applications as these typically exploit complex infrastructures which demand sophisticated and fine-grained configuration and re-configuration — we show that, through our approach, called DICER, it is possible to create complex IaC with significant amounts of time savings, both in IaC design as well as deployment and re-deployment times.
Manufacturing Resilient Bi-Opaque Predicates Against Symbolic Execution

Control-flow obfuscation increases program complexity by semantic-preserving transformation. Opaque predicates are essential gadgets to achieve such transformation. However, we observe that real-world opaque predicates are generally very simple and engage little security consideration. Recently, such insecure opaque predicates have been severely attacked by symbolic execution-based adversaries and jeopardize the security of control-flow obfuscation. This paper, therefore, proposes symbolic opaque predicates which can be resilient to symbolic execution-based adversaries. We design a general framework to compose such opaque predicates, which requires introducing challenging symbolic analysis problems (e.g., symbolic memory) in each opaque predicate. In this way, we may mislead symbolic execution engines into reaching false conclusions. We observe a novel bi-opaque property about symbolic opaque predicates, which can incur not only false negative issues but also false positive issues to attackers. To evaluate the efficacy of our idea, we have implemented a prototype obfuscation tool based on Obfuscator-LLVM and conduct experiments with real-world programs. Our evaluation results show that symbolic opaque predicates demonstrate excellent resilience to prevalent symbolic execution engines, such as BAP, Triton, and Angr. Moreover, although the costs of symbolic opaque predicates may vary for different problem settings, some predicates can be very efficient. Therefore, our framework is both secure and usable. Users can follow the framework to introduce symbolic opaque predicates into their obfuscation tools and made them more powerful.
A Taxonomy for Analysing Smart Cities Developments in Greece

Smart city initiatives are designed to develop information technology capacities and establish an agenda for change. It is also very difficult for the municipal authorities to identify, evaluate and implement the proper developments in a world characterised by the multiplicity of available solutions. Consequently, the policy aspect of a smart city is a major challenge in order to create a collaborative approach to innovation ecosystems based on sustainable partnerships among the main stakeholders from business, research, policy and citizen groups and achieve an alignment of local, regional and European policy levels and resources. The objective of this inquiry is to develop an analysis framework to better understand smart city practices in Greece and to identify the barriers where adaptation and improvement may be needed. Furthermore, this study aims at examining the Greek Municipalities in order to identify homogeneous and heterogeneous characteristics in the process of planning and developing a smart city. The study identifies the most important impediments towards smart cities actions development and the correlation between the smart cities population and the number of implemented actions. The results highlight the deviation of implemented and planned smart cities actions based on the proposed taxonomy identifying the most important development areas for Greece.
Moderate sedation induced by general anaesthetics disrupts audio-spatial feature binding with sustained P3 components in healthy humans

Abstract Feature binding is considered to be the basis for conscious stimulus perception, while anaesthetics exert a gradient effect on the loss of consciousness (LOC). By integrating these two streams of research, the present study assessed the effect of two anaesthetic agents (i.e. propofol and midazolam) on audio-spatial feature binding. We also recorded the electrophysiological activity of the frontal channels. Using pharmacokinetic simulation, we determined the effect-site concentration (Ce) of the anaesthetics at loss of response to verbal command and eyelash reflex. We subsequently adjusted Ce to 75%, 50% and 25% of Ce-LOC to achieve deep, moderate and light sedation, respectively. Behavioural results showed that moderate sedation selectively disrupted feature binding. The frontal channels showed a P3 component (350–600 ms peristimulus period) following the presentation of audio-spatial stimuli at baseline and under moderate and light sedations. Critically, the late event-related potential component (600–1000 ms) returned to the pre-activated level (0–350 ms) at baseline and under light sedation but was sustained under moderate sedation. We propose that audio-spatial feature binding may require the presence of a P3 component and its subsequent and sufficient decline, as under anaesthetic-induced moderate sedation the P3 component was sustained and featured binding was impaired.
Staphylococcus aureus Uses the GraXRS Regulatory System To Sense and Adapt to the Acidified Phagolysosome in Macrophages

ABSTRACT Macrophages are critical to innate immunity due to their ability to phagocytose bacteria. The macrophage phagolysosome is a highly acidic organelle with potent antimicrobial properties, yet remarkably, ingested Staphylococcus aureus replicates within this niche. Herein we demonstrate that S. aureus requires the GraXRS regulatory system for growth within this niche, while the SaeRS and AgrAC two-component regulatory systems and the α-phenol soluble modulins are dispensable. Importantly, we find that it is exposure to acidic pH that is required for optimal growth of S. aureus inside fully acidified macrophage phagolysosomes. Exposure of S. aureus to acidic pH evokes GraS signaling, which in turn elicits an adaptive response that endows the bacteria with increased resistance to antimicrobial effectors, such as antimicrobial peptides, encountered inside macrophage phagolysosomes. Notably, pH-dependent induction of antimicrobial peptide resistance in S. aureus requires the GraS sensor kinase. GraS and MprF, a member of the GraS regulon, play an important role for bacterial survival in the acute stages of systemic infection, where in murine models of infection, S. aureus resides within liver-resident Kupffer cells. We conclude that GraXRS represents a vital regulatory system that functions to allow S. aureus to evade killing, prior to commencement of replication, within host antibacterial immune cells. IMPORTANCE S. aureus can infect any site of the body, including the microbicidal phagolysosome of the macrophage. The ability of S. aureus to infect diverse niches necessitates that the bacteria be highly adaptable. Here we show that S. aureus responds to phagolysosome acidification to evoke changes in gene expression that enable the bacteria to resist phagolysosomal killing and to promote replication. Toxin production is dispensable for this response; however, the bacteria require the sensor kinase GraS, which transduces signals in response to acidic pH. GraS is necessary for phagolysosomal replication and survival of S. aureus in the acute stage of systemic infection. Disruption of this S. aureus adaptation would render S. aureus susceptible to phagocyte restriction. IMPORTANCE S. aureus can infect any site of the body, including the microbicidal phagolysosome of the macrophage. The ability of S. aureus to infect diverse niches necessitates that the bacteria be highly adaptable. Here we show that S. aureus responds to phagolysosome acidification to evoke changes in gene expression that enable the bacteria to resist phagolysosomal killing and to promote replication. Toxin production is dispensable for this response; however, the bacteria require the sensor kinase GraS, which transduces signals in response to acidic pH. GraS is necessary for phagolysosomal replication and survival of S. aureus in the acute stage of systemic infection. Disruption of this S. aureus adaptation would render S. aureus susceptible to phagocyte restriction.
Common Statement Kind Changes to Inform Automatic Program Repair

The search space for automatic program repair approaches is vast and the search for mechanisms to help restrict this search are increasing. We make a granular analysis based on statement kinds to find which statements are more likely to be modified than others when fixing an error. We construct a corpus for analysis by delimiting debugging regions in the provided dataset and recursively analyze the differences between the Simplified Syntax Trees associated with EditEvent's. We build a distribution of statement kinds with their corresponding likelihood of being modified and we validate the usage of this distribution to guide the statement selection. We then build association rules with different confidence thresholds to describe statement kinds commonly modified together for multi-edit patch creation. Finally we evaluate association rule coverage over a held out test set and find that when using a 95% confidence threshold we can create less and more accurate rules that fully cover 93.8% of the testing instances.
Compact Convolution Mapping on Neuromorphic Hardware using Axonal Delay

Mapping Convolutional Neural Network (CNN) to a neuromorphic hardware has been inefficient in synapse memory usage because both kernel/input reuse are not exploited well. We propose a method to enable kernel reuse by utilizing axonal delay, which is a biological parameter for a spiking neuron. Using IBM TrueNorth as a test platform, we demonstrate that the number of cores, neurons, synapses, and synaptic operations per time step can be reduced by up to 20.9x, 27.9x, 88.4x, and 1586x, respectively, compared to the conventional scheme, which raises the possibility of implementing large-scale CNN on neuromorphic hardware.
Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation

In this paper, we study the utility fairness resource allocation in a multi-user orthogonal-based cognitive radio network with cochannel interference (CCI) mitigation. In our proposed system model, we introduce the correct reception probability (CRP) model as a network utility metric. Furthermore, useful bounds on CRP are derived to analyze the performance of proposed allocation schemes. The optimal resource allocation is formulated as a worst-case user CRP maximum problem with both average CCI and average power budget constraints. However, this problem is non-convex and generally challenging to solve. Therefore, we solve this problem by successively performing subchannel allocation and power allocation. Firstly, a $k$ -means clustering inspired subchannel allocation strategy is proposed to divide secondary users (SUs) into multiple groups by minimizing the average mutual-signal-to-interference-ratio degree between any two SUs. The concept of reference user is employed to guarantee the quality of service of the primary user. In each subchannel, we formulate a max-min utility optimal power allocation problem. The non-linear Perron Frobenius theory is applied to solve this power allocation problem. Simulation results show that the proposed resource allocation scheme is fair and has relatively fast convergence.
A Large-Scale System for Real-Time Glucose Monitoring

We present the design, implementation, and deployment of an industry-first solution for real-time remote diabetes monitoring at large-scale. Our system delivers live insights to patients, via their mobile phones, regarding the current and future influence of their food choices and daily activities on their glucose levels. The platform, built using a distributed stream processing system, ingests real-time continuous glucose readings from sensors, insulin and meal information entered by patients within a mobile phone app, and activity levels from sensors embedded on the phone, and couples these with real-time analysis to determine the impact that the patient behavior will have on their glucose levels. We describe our design that achieves scale and system high availability through data and pipelined parallelism, decoupled system components, caches, and stateless jobs. An initial pilot of the system was deployed for use by a few hundred real diabetic patients and showed improvements in patient health – in terms of reduced periods out of range and experienced lows and highs. [1]. A full-scale release will be deployed for several thousand patients by the end of in 2018.
Towards Mitigating the Class-Imbalance Problem for Partial Label Learning

Partial label (PL) learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one is valid. It is well-known that the problem of class-imbalance stands as a major factor affecting the generalization performance of multi-class classifier, and this problem becomes more pronounced as the ground-truth label of each PL training example is not directly accessible to the learning approach. To mitigate the negative influence of class-imbalance to partial label learning, a novel class-imbalance aware approach named CIMAP is proposed by adapting over-sampling techniques for handling PL training examples. Firstly, for each PL training example, CIMAP disambiguates its candidate label set by estimating the confidence of each class label being ground-truth one via weighted k-nearest neighbor aggregation. After that, the original PL training set is replenished for model induction by over-sampling existing PL training examples via manipulation of the disambiguation results. Extensive experiments on artificial as well as real-world PL data sets show that CIMAP serves as an effective data-level approach to mitigate the class-imbalance problem for partial label learning.
A 2.6 mW Single-Ended Positive Feedback LNA for 5G Applications

This paper presents the design of a single-ended positive feedback Common Gate (CG) Low Noise Amplifier (LNA) for 5G applications. Positive feedback is utilized to achieve the trade-off between the input matching, the gain and the noise factor (NF) of the LNA. The positive feedback inherently cancels the noise produced by the input CG transistor. The proposed LNA is designed and fabricated in 150 nm CMOS by L-Foundry. At 1.41 GHz, the measured S11 and S22 are better than -20 dB and -8.4 dB, respectively. The highest voltage gain is 16.17 dB with a NF of 3.64 dB. The complete chip has an area of 1 mm2. The LNA's power dissipation is only 2.6 mW with a 1 dB compression point of -13 dBm. The simple, low power and single-ended architecture of the proposed LNA allows it to be implemented in phase array and Multiple Input Multiple Output (MIMO) radars, which have limited input and output pads and constrained power budgets for on-board components.
EvoGraph: An Effective and Efficient Graph Upscaling Method for Preserving Graph Properties

Nowadays, many researchers and industry groups often suffer from the lack of a variety of large-scale real graphs. Although a lot of synthetic graph generation methods,(or models) such as RMAT and BA have been developed, their output graphs tend to be quite different from real-world graphs in terms of graph properties. There are a few graph upscaling methods such as Gscaler, they still fail to preserve important properties of the original graph and fail to upscale due to out of memory or too long runtime. In this paper, we propose a novel graph upscaling method called EvoGraph that can upscale the original graph with preserving its properties regardless of a scale factor. It determines and attaches new edges to the real graph using the preferential attachment mechanism in an effective and efficient way. Through extensive experiments, we have demonstrated that EvoGraph significantly outperforms the state-of-the-art graph upscaling method Gscaler in terms of preserving graph properties and performance measures such as runtime, memory usage, and scalability.