Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances and throwbacks in hardware-assisted security: special session

Hardware security architectures and primitives are becoming increasingly important in practice providing trust anchors and trusted execution environment to protect modern software systems. Over the past two decades we have witnessed various hardware security solutions and trends from Trusted Platform Modules (TPM), performance counters for security, ARM's TrustZone, and Physically Unclonable Functions (PUFs), to very recent advances such as Intel's Software Guard Extension (SGX). Unfortunately, these solutions are rarely used by third party developers, make strong trust assumptions (including in manufacturers), are too expensive for small constrained devices, do not easily scale, or suffer from information leakage. Academic research has proposed a variety of solutions, in hardware security architectures, these advancements are rarely deployed in practice.
Analyzing The Impact Of Feedback In GitHub On The Software Developer's Mood

Software development depends on cooperation between people, and the way it works can define the future of the software project. Developers emotions affect their productivity and way they work, yet there is little information about how developers can influence the mood of each other. As a first step toward understanding how feedback may affect the developers’ sentiment, this paper analyzes the mood variations on more than 78k pull requests and 268k pull comments on GitHub. We found that in 31.16% of the cases the developers presented a significant mood variation within one hour when receiving feedback on their pull requests. The variation reduces to 18.16% when evaluating one day before and after the commentary. In software projects with less than 34k lines of code, the number of developers that never contribute again after receiving a negative comment on the first pull request is 10.97%; this number more than doubles to 24.02% when evaluating projects with more than
CRELD1 is an evolutionarily-conserved maturational enhancer of ionotropic acetylcholine receptors

The assembly of neurotransmitter receptors in the endoplasmic reticulum limits the number of receptors delivered to the plasma membrane, ultimately controlling neurotransmitter sensitivity and synaptic transfer function. In a forward genetic screen conducted in the nematode C. elegans, we identified crld-1 as a gene required for the synaptic expression of ionotropic acetylcholine receptors (AChR). We demonstrated that the CRLD-1A isoform is a membrane-associated ER-resident protein disulfide isomerase (PDI). It physically interacts with AChRs and promotes the assembly of AChR subunits in the ER. Mutations of Creld1, the human ortholog of crld-1a, are responsible for developmental cardiac defects. We showed that Creld1 knockdown in mouse muscle cells decreased surface expression of AChRs and that expression of mouse Creld1 in C. elegans rescued crld-1a mutant phenotypes. Altogether these results identify a novel and evolutionarily-conserved maturational enhancer of AChR biogenesis, which controls the abundance of functional receptors at the cell surface.
Generative Kernel PCA

Kernel PCA has shown to be a powerful feature extractor within many applications. Using the Restricted Kernel Machine formulation, a representation using visible and hidden units is obtained. This enables the exploration of new insights and connections between Restricted Boltzmann machines and kernel methods. This paper explores these connections, introducing a generative kernel PCA which can be used to generate new data, as well as denoise a given training dataset. This in a non-probabilistic setting. Moreover, relations with linear PCA and a pre-image reconstruction method are introduced in this paper.
Facial Incidence Colorings of Embedded Multigraphs

Abstract Let G be a cellular embedding of a multigraph in a 2-manifold. Two distinct edges e1, e2 ∈ E(G) are facially adjacent if they are consecutive on a facial walk of a face f ∈ F(G). An incidence of the multigraph G is a pair (v, e), where v ∈ V (G), e ∈ E(G) and v is incident with e in G. Two distinct incidences (v1, e1) and (v2, e2) of G are facially adjacent if either e1= e2 or e1, e2are facially adjacent and either v1 = v2or v1≠v2and there is i ∈ {1, 2} such that eiis incident with both v1, v2. A facial incidence coloring of G assigns a color to each incidence of G in such a way that facially adjacent incidences get distinct colors. In this note we show that any embedded multigraph has a facial incidence coloring with seven colors. This bound is improved to six for several wide families of plane graphs and to four for plane triangulations.
Influence Factors in Software Productivity - A Tertiary Literature Review

Software organizations need to increase their productivity to stay competitive. Although there is a lot of research on productivity in software development, software organizations still do not know what are the most significant productivity factors in which they should invest. This paper presents a Tertiary Literature Review (TLR) that aims to identify and analyze Systematic Literature Reviews (SLR) on the influence factors of software productivity reported in the scientific literature. We extracted and classified the influence factors into organizational factors (organizational-dependent factors) and human factors (people-dependent factors). The relevance of the factors was extracted according to the amount of references found in the secondary studies. Using this information, software organizations can improve the productivity of their projects by evaluating the influence factors that best fit their context.
Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.
Somatosensory Regions Show Limited Functional Connectivity Differences in Youth with Autism Spectrum Disorder

An estimated 70-90% of children with autism spectrum disorder (ASD) have sensory symptoms, which may present as hyper- or hyporesponsivity in one or more sensory modalities. These sensitivities correlate with social symptoms, activity, and social interaction levels. Interestingly, sensory symptoms appear to be most prevalent in late childhood, suggesting a developmental component. Although the neural basis of sensory sensitivities remains unclear, atypical functional connectivity of sensory brain regions has been suggested as a potential mechanism. Tactile sensitivities are among the most predictive of social functioning, yet no studies to our knowledge have examined somatosensory functional connectivity in children and adolescents with ASD, when symptoms are typically most prominent. In this study, we used human data from the Autism Brain Imaging Data Exchange (ABIDE-I) to assess functional connectivity differences of somatosensory regions during resting state functional magnetic resonance imaging, in youth aged 8-15 years. After head motion exclusion, our sample included 67 participants with ASD and 121 typically developing controls. We additionally examined associations between functional connectivity and age, as well as ASD symptom severity. Together, these seed-based analyses showed limited differences in functional connectivity between groups, either to hypothesized target regions or in terms of global connectivity. Our findings suggest that hyper- or hyposomatosensory functional connectivity at rest is not a population-level feature in ASD. However, this does not preclude increased variability of somatosensory networks across the ASD population. Furthermore, as sensory sensitivities were not specifically assessed in this sample, future studies may be better able to identify patterns of functional connectivity, reflecting individual differences in sensory symptoms.
Optim: A mathematical optimization package for Julia

Optim provides a range of optimization capabilities written in the Julia programming language (Bezanson et al. 2017). Our aim is to enable researchers, users, and other Julia packages to solve optimization problems without writing such algorithms themselves. The package supports optimization on manifolds, functions of complex numbers, and input types such as arbitrary precision vectors and matrices. We have implemented routines for derivative free, first-order, and second-order optimization methods. The user can provide derivatives themselves, or request that they are calculated using automatic differentiation or finite difference methods. The main focus of the package has currently been on unconstrained optimization, however, box-constrained optimization is supported, and a more comprehensive support for constraints is underway. Similar to Optim, the C library NLopt (Johnson 2008) contains a collection of nonlinear optimization routines. In Python, scipy.optimize supports many of the same algorithms as Optim does, and Pymanopt (Townsend, Niklas, and Weichwald 2016) is a toolbox for manifold optimization. Within the Julia community, the packages BlackBoxOptim.jl and Optimize.jl provide optimization capabilities focusing on derivative-free and large-scale smooth problems respectively. The packages Convex.jl and JuMP.jl (Dunning, Huchette, and Lubin 2017) define modelling languages for which users can formulate optimization problems. In contrast to the previously mentioned optimization codes, Convex and JuMP work as abstraction layers between the user and solvers from a other packages.
Speculative parallelization needs rigor: probabilistic analysis for optimal speculation of finite-state machine applications

Finite-State Machine (FSM) applications are important for many domains. But FSM computation is inherently sequential, making such applications notoriously difficult to parallelize. Most prior methods address the problem through speculations on simple heuristics, offering limited applicability and inconsistent speedups. This paper provides some principled understanding of FSM parallelization, and offers the first disciplined way to exploit application-specific information to inform speculations for parallelization. Through a series of rigorous analysis, it presents a probabilistic model that captures the relations between speculative executions and the properties of the target FSM and its inputs. With the formulation, it proposes two model-based speculation schemes that automatically customize themselves with the suitable configurations to maximize the parallelization benefits. This rigorous treatment yields near-linear speedup on applications that stateof-the-art techniques can barely accelerate.
Digital innovation in platform-based ecosystems: an evolutionary framework

Creating a digital platform to leverage the potential of an ecosystem is recognized as an effective strategy to optimize innovation efforts in the digital era. In this paper, we analyze how platform-based ecosystems can ensure the commercialization of a constant flow of digital innovations. Our contribution focuses the role of platform-owners and the way they orchestrate the coupling process between two subsystems of the ecosystem: the innovation factory (IF) and the business development (BizDev). We apply a life-cycle perspective, analyzing how the relationships between platform design, value creation and knowledge are dynamically aligned. Existing accounts of ecosystem dynamics are quite scarce in the academic literature and they do not systematically acknowledge these two subsystems. By considering the two parts of ecosystems, we contribute to a better understanding of platform-based ecosystem evolution's process. Three case studies illustrate platform-owners' choices regarding the management of the coupling process.
Long-term tracking algorithm using deep features and a single shot multibox detector

Abstract. The ability for computers to target and track objects within video sequences has attracted increasing amounts of attention in recent years. Many tracking algorithms adopt correlation filtering-based algorithms, in which targeting of features in the previous video frame is essential to predicting the target’s position in the next frame. A serious problem in tracking is drift. If drift occurs in the present frame, it becomes increasingly difficult to correct this error in later frames, which can lead to loss of the tracked target and greatly reduced tracking accuracy and stability. To solve this problem and improve tracking performance, we propose an algorithm that incorporates deep features. A sparse representation method is used to filter deep features and improve their complexity. Then, a Siamese network is used to judge the similarities between the target and template and determine a confidence level. Next, a single shot multibox detector (SSD) is introduced to support tracking, which is activated when the confidence level falls below a certain threshold. The correct target can be found by scanning the present frame using the detector and then updating the template. The confidence level is then reduced and the SSD continues working to resolve the drift. Tracking stability and accuracy are greatly improved after the redetection process. We use large-scale benchmark datasets to demonstrate that the proposed approach performs better than existing state-of-the-art approaches.
Load-aware traffic control in software-defined enterprise Wireless Local Area Networks

With the growing popularity of Bring Your Own Device (BYOD), modern enterprise Wireless Local Area Networks (WLANs) deployments always consist of multiple Access Points (APs) to meet the fast-increasing demand for wireless access. In order to avoid network congestion which leads to issues such as suboptimal Quality of Service (QoS) and degraded user Quality of Experience (QoE), intelligent network traffic control is needed. Software Defined Networking (SDN) is an emerging architecture and intensively discussed as one of the most promising technologies to simplify network management and service development. In the SDN architecture, network management is directly programmable because it is decoupled from forwarding layer. Leveraging SDN to the existing enterprise WLANs framework, network services can be flexibly implemented to support intelligent network traffic control. This thesis studies the architecture of software-defined enterprise WLANs and how to improve network traffic control from a client-side and an AP-side perspective. By extending an existing software-defined enterprise WLANs framework, two adaptive algorithms are proposed to provide client-based mobility management and load balancing. Custom protocol messages and AP load metric are introduced to enable the proposed adaptive algorithms. Moreover, a software-defined enterprise WLAN system is designed and implemented on a testbed. A load-aware automatic channel switching algorithm and a QoS-aware bandwidth control algorithm are proposed to achieve AP-based network traffic control. Experimental results from the testbed show that the designed system and algorithms significantly improve the performance of traffic control in enterprise WLANs in terms of network throughput, packet loss rate, transmission delay and jitter.
A New Standard DNA Damage (SDD) Data Format

Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called “indirect” damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates intermodel comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.
Fully Digital Driving Circuit for TFT LCD Using the Concept of Ramp-Stop

A fully digital driving circuit for thin-film transistor liquid crystal display can offer benefits, including less power consumption and a shortened design schedule. Using the pulsewidth signal to control the moment to stop the ramp voltage, the pixel voltage can be set accurately. In this paper, the possible issues of power consumption, device nonuniformity, and parasitic capacitance are well-studied to prove the feasibility and advantages of the digital driving method. It is prospectively expected to substitute for the conventional analog driving method in the future.
5GC +: an Experimental Proof of a Programmable Mobile Core for 5G

The telecom industry is striving to evolve the current 4G system to the 5th generation of mobile networks, aiming at boosting data rates and performance for the traditional broadband services, as well as supporting a wide range of new use cases with different technical requirements. The first specifications of the 5G system are included in 3GPP's Release 15 and expected to be enhanced in the future Release 16. In order to cater for the diverse network requirements posed by the 5G use cases and in the view of instantiating customized networks that suit the vertical industry, a programmable and flexible data plane is envisioned as a key element of the future system architecture. In this context, this paper presents an advanced core network design underpinned by SDN principles, introducing enhancements to the current mobile networks' functional architecture and leveraging programmatic approaches for the agile provisioning and re-configuration of customized data planes. Our proposal is substantiated and validated by means of an initial experimental prototype, carrying out a functional proof of concepts including commercial 4G cellular base stations and devices.
Lichen planopilaris: retrospective study on the characteristics and treatment of 291 patients

Abstract Lichen planopilaris (LPP) is an immune-mediated cicatricial alopecia. The main clinical presentations of LPP include classic form, frontal fibrosing alopecia (FFA), and Graham–Little–Piccardi–Lassueur syndrome (GLPLS). We reviewed medical records of all 291 patients diagnosied with LPP from 2006 to 2017 in Department of Dermatology, Tehran University of Medical Sciences. LPP was more common in women than men. Lichen planus (LP) was seen in 59 of patients (20.3%). Parietal lesions (69.75%), frontal (27.14%), occipital (23.71%), and temporal (21.64%) were frequently seen in LPP patients. However, trunk hair involvement (15.4% vs. 2.7%; p = .011) and eyebrow involvement (57.7% vs. 0%; p < .0001) were high in FFA patients. The response rates of cyclosporine (CSP) and methotrexate (MTX) were highest, 100% and 85%, respectively. Those treated with CSP achieved partial remission (PR) and complete remission (CR) faster than MTX-treated group. Moreover, MTX was more effective than MMF but not different in time to reach PR (p = .23) or CR (p = .56). However, CSP and MTX were less safe compared with MMF. 5-alpha reductase inhibitors, systemic retinoids (isotretinoin) or their combination were the most effective therapeutic options for FFA patients.
PVT2: Process, Voltage, Temperature and Time-dependent Variability in Scaled CMOS Process

In addition to the conventional PVT (Process, Voltage and Temperature) variation, time-dependent current fluctuation such as random telegraph noise (RTN) poses a new challenge on VLSI reliability. In this paper, we show that compared with the static random variation, RTN amplitude of a particular device is not constant across supply voltages and temperatures. A device may show large RTN amplitude at one operating condition and small amplitude at another operating condition. As a result, RTN amplitude distribution becomes uncorrelated across a wide range of voltage and temperature. The emergence of uncorrelated distribution causes significant degradation of worst-case values. Analysis results based on variability models from a 65 nm silicon-on-insulator process show that uncorrelated RTN degrades the worst-case threshold voltage value significantly compared with that where RTN is not considered. Delay variation analysis shows that consideration of RTN in the statistical analysis have little impact at high supply voltage. However, at low voltage operation, RTN can degrade the worst-case value by more than 5%.