Miracle-dz/UniFAR-FTUs huggingface dataset:
Assembling Strategic Narratives

Social media are becoming sites of information operations-activities that seek to undermine information systems and manipulate civic discourse [26,36,44,47]. Through a mixed methods approach, our research extends investigations of online activism to examine the "work" of online information operations conducted on Twitter. In particular, we analyze the English-language conversation surrounding the reemergence of Omran Daqneesh (the "Aleppo Boy") on Syrian state television, almost a year after his family's home was bombed in an airstrike conducted by the Syrian government. We uncover: a network of clustered users that contributes to a contested and politicized information space surrounding Omran's story; the presence of undermining narratives that serve to disrupt the mainstream media's narrative and confuse the audience; and the techniques used when promoting, defending, or undermining narratives. In the current climate of increasing polarization in online social spaces, this work contributes an improved understanding of information operations online and of the collaborations that take shape around and through them.
HARNet: Towards On-Device Incremental Learning using Deep Ensembles on Constrained Devices

Recent advancements in the domain of pervasive computing have seen the incorporation of sensor-based Deep Learning algorithms in Human Activity Recognition (HAR). Contemporary Deep Learning models are engineered to alleviate the difficulties posed by conventional Machine Learning algorithms which require extensive domain knowledge to obtain heuristic hand-crafted features. Upon training and deployment of these Deep Learning models on ubiquitous mobile/embedded devices, it must be ensured that the model adheres to their computation and memory limitations, in addition to addressing the various mobile- and user-based heterogeneities prevalent in actuality. To handle this, we propose HARNet - a resource-efficient and computationally viable network to enable on-line Incremental Learning and User Adaptability as a mitigation technique for anomalous user behavior in HAR. Heterogeneity Activity Recognition Dataset was used to evaluate HARNet and other proposed variants by utilizing acceleration data acquired from diverse mobile platforms across three different modes from a practical application perspective. We perform Decimation as a Down-sampling technique for generalizing sampling frequencies across mobile devices, and Discrete Wavelet Transform for preserving information across frequency and time. Systematic evaluation of HARNet on User Adaptability yields an increase in accuracy by ~35% by leveraging the model's capability to extract discriminative features across activities in heterogeneous environments.
From datalog rules to efficient programs with time and space guarantees

This paper describes a method for transforming any given set of Datalog rules into an efficient specialized implementation with guaranteed worst-case time and space complexities, and for computing the complexities from the rules. The running time is optimal in the sense that only useful combinations of facts that lead to all hypotheses of a rule being simultaneously true are considered, and each such combination is considered exactly once. The associated space usage is optimal in that it is the minimum space needed for such consideration modulo scheduling optimizations that may eliminate some summands in the space usage formula. The transformation is based on a general method for algorithm design that exploits fixed-point computation, incremental maintenance of invariants, and combinations of indexed and linked data structures. We apply the method to a number of analysis problems, some with improved algorithm complexities and all with greatly improved algorithm understanding and greatly simplified complexity analysis.
Practical network-wide packet behavior identification by AP classifier

Identifying the network-wide forwarding behaviors of a packet is essential for many network management applications, including rule verification, policy enforcement, attack detection, traffic engineering, and fault localization. Current tools that can perform packet behavior identification either incur large time and memory costs or do not support real-time updates. In this paper we present AP Classifier, a control plane tool for packet behavior identification. AP Classifier is developed based on the concept of atomic predicates which can be used to characterize the forwarding behaviors of packets. Experiments using the data plane network state of two real networks show that the processing speed of AP Classifier is faster than existing tools by at least an order of magnitude. Furthermore, AP Classifier uses very small memory and is able to support real-time updates.
Performance Analysis of SIMD Vectorization of High-Order Finite-Element Kernels

Physics-based three-dimensional numerical simulations are becoming more predictive and are already essential for improving the understanding of natural phenomena, such as earthquakes, tsunami, flooding or climate change and global warming. Among the numerical methods available to support these simulations, Finite-Element formulations have been implemented in several major software packages. The efficiency of these algorithms remains a challenge due to the irregular memory access that prevents the squeezing out of the maximum level of performance out of current architectures. This is particularly true at the shared-memory level with several levels of parallelism and complex memory hierarchies. Despite significant efforts, automatic optimizations provided by compilers and high-level frameworks are often far from the performances obtained from hand-tuned implementations. In this paper, we have extracted a kernel from the EFISPEC software package developed at BRGM (the French Geological Survey). This application implements a high-order finite-element method to solve the elastodynamic equation. We characterize the performance of the extracted mini-app considering key parameters such as the order of the approximation, the memory access pattern or the vector length. Based on this study, we detail specific optimizations and we discuss the results measured as regards to the roofline performance model on Intel Broadwell and Skylake architectures.
On‐fiber display of a functional peptide at sites distant from the cell surface using a long bacterionanofiber of a trimeric autotransporter adhesin

In the cell surface display system, the distance of a surface‐displayed molecule from the cell surface should influence its functionality due to the interference by other surface structures. For the purpose of developing this distance‐variable surface display system, we utilized a long fibrous adhesin, Acinetobacter trimeric autotransporter adhesin (AtaA) of the strain Tol 5. We constructed His‐tagged full‐length and shorter AtaA fibers designed by N‐terminal deletion and expressed them in the ΔataA mutant. Immunoelectron microscopy clearly showed that they formed fibers on the cell surface and the His‐tag was displayed on the fiber tip located at fixed distances from the cell surface. N‐terminal deletion of AtaA shortened the distance between the His‐tag and the cell surface, as designed. Time‐course analyses of the cell‐to‐Ni‐Sepharose beads binding revealed that cells producing the longer fibers bound more rapidly to the beads. The His‐tagged AtaA derivatives were also displayed on Escherichia coli cells, and a similar tendency was shown; the His‐tag on the longer fiber was more functional than that on the shorter one. Thus, we developed an on‐fiber display system of a functional peptide using a long trimeric autotransporter adhesin (TAA) fiber, which can vary the distance between the displayed molecule and the cell surface.
Constraint Solving Approach to Schedulability Analysis in Real-Time Systems

In real-time systems, the satisfaction of real-time properties is as important as the correct behavior of the function. There are many safety-critical systems among the real-time systems, and thus, the satisfaction of real-time properties is directly related to safety in those cases. By performing the schedulability analysis, we can predict the behavior of real-time systems and ensure that real-time properties are met. In this paper, we propose the schedulability analysis of a real-time system through a constraint solving approach, that is, by treating a scheduling problem as a constraint solving problem. To do this, we describe a method of representing the task behavior and schedulable properties of real-time tasks in the form of constraints and finding answers that satisfy all the constraints using a constraint solver.
Enhanced Biofilm Formation and Membrane Vesicle Release by Escherichia coli Expressing a Commonly Occurring Plasmid Gene, kil

Escherichia coli is one of the most prevalent microorganisms forming biofilms on indwelling medical devices, as well as a representative model to study the biology and ecology of biofilms. Here, we report that a small plasmid gene, kil, enhances biofilm formation of E. coli. The kil gene is widely conserved among naturally occurring colicinogenic plasmids such as ColE1 plasmid, and is also present in some plasmid derivatives used as cloning vectors. First, we found that overexpression of the kil gene product dramatically increased biofilm mass enriched with extracellular DNA in the outer membrane-compromised strain RN102, a deep rough LPS mutant E. coli K-12 derivative. We also found that the kil-enhanced biofilm formation was further promoted by addition of physiologically relevant concentrations of Mg2+, not only in the case of RN102, but also with the parental strain BW25113, which retains intact core-oligosaccharide LPS. Biofilm formation by kil-expressing BW25113 strain (BW25113 kil+) was significantly inhibited by protease but not DNase I. In addition, a large amount of proteinous materials were released from the BW25113 kil+ cells. These materials contained soluble cytoplasmic and periplasmic proteins, and insoluble membrane vesicles (MVs). The kil-induced MVs were composed of not only outer membrane/periplasmic proteins, but also inner membrane/cytoplasmic proteins, indicating that MVs from both of the outer and inner membranes could be released into the extracellular milieu. Subcellular fractionation analysis revealed that the Kil proteins translocated to both the outer and inner membranes in whole cells of BW25113 kil+. Furthermore, the BW25113 kil+ showed not only reduced viability in the stationary growth phase, but also increased susceptibility to killing by predator bacteria, Vibrio cholerae expressing the type VI secretion system, despite no obvious change in morphology and physiology of the bacterial membrane under regular culture conditions. Taken together, our findings suggest that there is risk of increasing biofilm formation and spreading of numerous MVs releasing various cellular components due to kil gene expression. From another point of view, our findings could also offer efficient MV production strategies using a conditional kil vector in biotechnological applications.
Design Choices for Next-Generation Neurotechnology Can Impact Motion Artifact in Electrophysiological and Fast-Scan Cyclic Voltammetry Measurements

Implantable devices to measure neurochemical or electrical activity from the brain are mainstays of neuroscience research and have become increasingly utilized as enabling components of clinical therapies. In order to increase the number of recording channels on these devices while minimizing the immune response, flexible electrodes under 10 µm in diameter have been proposed as ideal next-generation neural interfaces. However, the representation of motion artifact during neurochemical or electrophysiological recordings using ultra-small, flexible electrodes remains unexplored. In this short communication, we characterize motion artifact generated by the movement of 7 µm diameter carbon fiber electrodes during electrophysiological recordings and fast-scan cyclic voltammetry (FSCV) measurements of electroactive neurochemicals. Through in vitro and in vivo experiments, we demonstrate that artifact induced by motion can be problematic to distinguish from the characteristic signals associated with recorded action potentials or neurochemical measurements. These results underscore that new electrode materials and recording paradigms can alter the representation of common sources of artifact in vivo and therefore must be carefully characterized.
Engaging with Health Data

Self-tracking data is often seen as a means to reflect and achieve a goal, usually focusing on positive insights and actions. Lately, some studies have discussed the negative consequences of self-tracking, suggesting that people interact with personal data in different ways. We explored how self-tracking activities and the emotional context characterize how people engage with personal health data through the analysis of a complex and emotionally-loaded use case: fertility self-tracking. We qualitatively analyzed patient-generated content in an online health community dedicated to fertility. We found five distinct types of engagement with data: positive, burdened, obsessive, trapped, and abandoning. Each of them is composed of an action and an emotional component that mutually influence each other. We discuss how the interplay of these components characterize a person's engagement with data, how the online forum made these issues visible, and how they are embedded in the self-tracking culture. We also provide insights into the implications of these issues for self-tracking tools. Finally, we hypothesize how people transition through the types of relationships with data, suggesting directions for future research in the area.
LiteHAX: Lightweight Hardware-Assisted Attestation of Program Execution

Unlike traditional processors, embedded Internet of Things (IoT) devices lack resources to incorporate protection against modern sophisticated attacks resulting in critical consequences. Remote attestation (RA) is a security service to establish trust in the integrity of a remote device. While conventional RA is static and limited to detecting malicious modification to software binaries at load-time, recent research has made progress towards runtime attestation, such as attesting the control flow of an executing program. However, existing control-flow attestation schemes are inefficient and vulnerable to sophisticated data-oriented programming (DOP) attacks subvert these schemes and keep the control flow of the code intact. In this paper, we present LiteHAX, an efficient hardware-assisted remote attestation scheme for RISC-based embedded devices that enables detecting both control-flow attacks as well as DOP attacks. LiteHAX continuously tracks both the control-flow and data-flow events of a program executing on a remote device and reports them to a trusted verifying party. We implemented and evaluated LiteHAX on a RISC-V System-on-Chip (SoC) and show that it has minimal performance and area overhead.
TRIANGLE: a Platform to Validate 5G KPIs in End to End scenarios

The mobile Apps market is flourishing and will become an important part of the 5G business model. Ensuring the proper functioning of 5G Apps and services is then paramount and a homogenous testing and certification scheme can help achieve it. Building on the experience on 4G and App testing methodologies, the TRIANGLE project is pursuing the creation of such a 5G certification scheme, together with the development of a testing platform that can make such scheme feasible and practical. In the present paper the initial investigation on the definition of the certification scheme, together with the impact on practical measurement is presented. The YouTube API and ExoPlayer will be used as use cases for extracting a sets of Key Performance Indicators for comparison.
Supportive Attention in End-To-End Memory Networks

Attention over natural language aims to spotlight on the meaningful region with representative keywords which can extract desirable meanings to accomplish a task of interest. The attention parameter is a latent variable which was indirectly estimated by minimizing the classification loss. For the task of question answering (QA), the classification loss may not sufficiently reflect the target answer. This paper proposes a direct solution which attends the meaningful region by minimizing the reconstruction loss due to auxiliary or supporting data which are available in different scenarios. In particular, minimizing the classification and reconstruction losses are carried out under the end-to-end memory network so that the memory-augmented question answering is realized. Such a supportive attention is implemented as a sequence-to-sequence model which reconstructs the supporting sentence to assure the translation invariance. The merit of this method is sufficiently demonstrated for sequential learning by using the bAbI QA and dialog tasks.
SMO Lattices for the Parallel Training of Support Vector Machines

In this work, a method is proposed to train Support Vector Machines in parallel. The dierence to other parallel implementations is that the problem is decomposed into hierarchically connected nodes and that each node does not have to fully optimize its local problem. Instead Lagrange multipliers are ltered and transferred between nodes during runtime, with important ones ascending and unimportant ones descending inside the architecture. Experimental validation demonstrates the advan- tages in terms of speed in comparison to other approaches.
Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech’s Participation in the WMT 2018 Quality Estimation Task

We describe Vicomtech’s participation in the WMT 2018 shared task on quality estimation, for which we submitted minimalist quality estimators. The core of our approach is based on two simple features: lexical translation overlaps and language model cross-entropy scores. These features are exploited in two system variants: uMQE is an unsupervised system, where the final quality score is obtained by averaging individual feature scores; sMQE is a supervised variant, where the final score is estimated by a Support Vector Regressor trained on the available annotated datasets. The main goal of our minimalist approach to quality estimation is to provide reliable estimators that require minimal deployment effort, few resources, and, in the case of uMQE, do not depend on costly data annotation or post-editing. Our approach was applied to all language pairs in sentence quality estimation, obtaining competitive results across the board.
Demo: programming enterprise WLANs with odin

We present a demo of Odin, an SDN framework to program enterprise wireless local area networks (WLANs). Enterprise WLANs need to support a wide range of services and functionalities. This includes authentication, authorization and accounting, policy, mobility and interference management, and load balancing. WLANs also exhibit unique challenges. In particular, access point (AP) association decisions are not made by the infrastructure, but by clients. In addition, the association state machine combined with the broadcast nature of the wireless medium requires keeping track of a large amount of state changes. To this end, Odin builds on a light virtual AP abstraction that greatly simplifies client management. Odin does not require any client side modifications and its design supports WPA2 Enterprise. With Odin, a network operator can implement enterprise WLAN services as network applications.
Unmasking Android Obfuscation Tools Using Spatial Analysis

Android has become one of the most popular mo-bile device operating systems. Indeed, its security issues have attracted a lot of attention. One of the major security concerns is the use of obfuscation strategies to evade anti-malware solutions. Android malware authors are increasingly using code obfuscation tools and techniques to hide malicious code. In this work, we in- troduce a novel fingerprinting approach for Android obfuscation tools based on spatial analysis. We investigate first-order and second-order statistical features to analyze spatial distribution of pixels representing Android binary images. With our approach, we are able to achieve nearly 90% accuracy in fingerprinting several obfuscation tools with specific configuration options.
CoHSI IV: Unifying Horizontal and Vertical Gene Transfer - is Mechanism Irrelevant ?

In previous papers we have described with strong experimental support, the organising role that CoHSI (Conservation of Hartley-Shannon Information) plays in determining important global properties of all known proteins, from defining the length distribution, to the natural emergence of very long proteins and their relationship to evolutionary time. Here we consider the insight that CoHSI might bring to a different problem, the distribution of identical proteins across species. Horizontal and Vertical Gene Transfer (HGT/VGT) both lead to the replication of protein sequences across species through a diversity of mechanisms some of which remain unknown. In contrast, CoHSI predicts from fundamental theory that such systems will demonstrate power law behavior independently of any mechanisms, and using the Uniprot database we show that the global pattern of protein re-use is emphatically linear on a log-log plot (adj. $R^{2} = 0.99, p < 2.2 \times 10^{-16}$ over 4 decades); i.e. it is extremely close to the predicted power law. Specifically we show that over 6.9 million proteins in TrEMBL 18-02 are re-used, i.e. their sequence appears identically in between 2 and 9,812 species, with re-used proteins varying in length from 7 to as long as 14,596 amino acids. Using (DL+V) to denote the three domains of life plus viruses, 21,676 proteins are shared between two (DL+V); 22 between three (DL+V) and 5 are shared in all four (DL+V). Although the majority of protein re-use occurs between bacterial species those proteins most frequently re-used occur disproportionately in viruses, which play a fundamental role in this distribution. These results suggest that diverse mechanisms of gene transfer (including traditional inheritance) are irrelevant in determining the global distribution of protein re-use.
The use of name spaces in plan 9

Plan 9 is a distributed system built at the Computing Sciences Research Center of AT&T Bell Laboratories over the last few years. Its goal is to provide a production-quality system for software development and general computation using heterogeneous hardware and minimal software. A Plan 9 system comprises CPU and file servers in a central location connected together by fast networks. Slower networks fan out to workstation-class machines that serve as user terminals. Plan 9 argues that given a few carefully implemented abstractions it is possible to produce a small operating system that provides support for the largest systems on a variety of architectures and networks. The foundations of the system are built on two ideas: a per-process name space and a simple message-oriented file system protocol.
Hierarchical Neural Network Architecture In Keyword Spotting

Keyword Spotting (KWS) provides the start signal of ASR problem, and thus it is essential to ensure a high recall rate. However, its real-time property requires low computation complexity. This contradiction inspires people to find a suitable model which is small enough to perform well in multi environments. To deal with this contradiction, we implement the Hierarchical Neural Network(HNN), which is proved to be effective in many speech recognition problems. HNN outperforms traditional DNN and CNN even though its model size and computation complexity are slightly less. Also, its simple topology structure makes easy to deploy on any device.