Miracle-dz/UniFAR-FTUs huggingface dataset:
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks

In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for solving a class of linearly constrained non-convex optimization problems. We show that with random initialization of the primal and dual variables, both algorithms are able to compute second-order stationary solutions (ss2) with probability one. This is the first result showing that primal-dual algorithm is capable of finding ss2 when only using first-order information; it also extends the existing results for first-order, but primal-only algorithms. An important implication of our result is that it also gives rise to the first global convergence result to the ss2, for two classes of unconstrained distributed non-convex learning problems over multi-agent networks.
Efficient Convention Emergence through Decoupled Reinforcement Social Learning with Teacher-Student Mechanism

In this paper, we design reinforcement learning based (RL-based) strategies to promote convention emergence in multiagent systems (MASs) with large convention space. We apply our approaches to a language coordination problem in which agents need to coordinate on a dominant lexicon for efficient communication. By modeling each lexicon which maps each concept to a single word as a Markov strategy representation, the original single-state convention learning problem can be transformed into a multi-state multiagent coordination problem. The dynamics of lexicon evolutions during an interaction episode can be modeled as a Markov game, which allows agents to improve the action values of each concept separately and incrementally. Specifically we propose two learning strategies, multiple-Q and multiple-R, and also propose incorporating teacher-student mechanism on top of the learning strategies to accelerate lexicon convergence speed. Extensive experiments verify that our approaches outperform the state-of-the-art approaches in terms of convergence efficiency, convention quality and scalability.
Data-Driven Filtered Reduced Order Modeling of Fluid Flows

We propose a data-driven filtered reduced order model (DDF-ROM) framework for the numerical simulation of fluid flows. The novel DDF-ROM framework consists of two steps: (i) In the first step, we use explicit ROM spatial filtering of the nonlinear PDE to construct a filtered ROM. This filtered ROM is low-dimensional, but is not closed (because of the nonlinearity in the given PDE). (ii) In the second step, we use data-driven modeling to close the filtered ROM, i.e., to model the interaction between the resolved and unresolved modes. To this end, we use a quadratic ansatz to model this interaction and close the filtered ROM. To find the new coefficients in the closed filtered ROM, we solve an optimization problem that minimizes the difference between the full order model data and our ansatz. We emphasize that the new DDF-ROM is built on general ideas of spatial filtering and optimization and is independent of (restrictive) phenomenological arguments. We investigate the DDF-ROM in the numerical simulation of a 2D channel flow past a circular cylinder at Reynolds number $Re=100$. The DDF-ROM is significantly more accurate than the standard projection ROM. Furthermore, the computational costs of the DDF-ROM and the standard projection ROM are similar, both costs being orders of magnitude lower than the computational cost of the full order model. We also compare the new DDF-ROM with modern ROM closure models in the numerical simulation of the 1D Burgers equation. The DDF-ROM is more accurate and significantly more efficient than these ROM closure models.
Synchronized Scene Views in Mixed Virtual Reality for Guided Viewing

Virtual Reality devices are available with different resolutions and fields of view. Users can simultaneously interact within environments on head mounted displays, cell phones, tablets, and PowerWalls. Sharing scenes across devices requires solutions that smoothly synchronize shared navigation, minimize jitter and avoid visual confusion. In this paper we present a system that allows a single user to remotely guide many remote users within a virtual reality environment. A variety of mixed device environments are supported to let different users connect to the system. Techniques are implemented to minimize jitter and synchronize views, and deal with different fields of view.
Varys: Protecting SGX Enclaves from Practical Side-Channel Attacks

Numerous recent works have experimentally shown that Intel Software Guard Extensions (SGX) are vulnerable to cache timing and page table side-channel attacks which could be used to circumvent the data confidentiality guarantees provided by SGX. Existing mechanisms that protect against these attacks either incur high execution costs, are ineffective against certain attack variants, or require significant code modifications. We present Varys, a system that protects unmodified programs running in SGX enclaves from cache timing and page table side-channel attacks. Varys takes a pragmatic approach of strict reservation of physical cores to security-sensitive threads, thereby preventing the attacker from accessing shared CPU resources during enclave execution. The key challenge that we are addressing is that of maintaining the core reservation in the presence of an untrusted OS. Varys fully protects against all L1/L2 cache timing attacks and significantly raises the bar for page table side-channel attacks--all with only 15% overhead on average for Phoenix and PARSEC benchmarks. Additionally, we propose a set of minor hardware extensions that hold the potential to extend Varys' security guarantees to L3 cache and further improve its performance.
An adaptative approach for ontology alignment visualization

The increase in the volume of unstructured web data in recent decades has been driven by the arising of new media, devices and technologies. In this context, the Semantic Web was developed, whose objective is to provide a layer of knowledge representation to that data, facilitating the treatment by automated processes. Ontologies are key elements of the Semantic Web, providing a description of the concepts and relationships between them, for a specific domain. However, ontologies of the same domain may differ in structure, granularity or terminology, requiring a process of matching between them to be performed, producing a set of correspondences between semantically related entities (alignment). A growing number of matching approaches have emerged in the literature, and the need to evaluate and qualitatively compare the produced alignments is presented. Tasks that make use of alignments started to demand better graphical representations for it. In this context, a survey was conducted with alignment specialists to identify the most important aspects in an alignment visualization. This work presents an adaptative approach for alignment visualization, that allows users to choose how and what to visualize, according to their own preferences or the task being performed at that moment (creation, manipulation, evaluation, etc.). Finally, a prototype was built with the purpose of validating the solution. The results obtained from the prototype validation with users show that the approach handles the problems it proposes to solve, with a margin for future work on alignment visualization.
Twitter Universal Dependency Parsing for African-American and Mainstream American English

Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools. We broaden English dependency parsing to handle social media English, particularly social media African-American English (AAE), by developing and annotating a new dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework. We describe our standards for handling Twitter- and AAE-specific features and evaluate a variety of cross-domain strategies for improving parsing with no, or very little, in-domain labeled data, including a new data synthesis approach. We analyze these methods’ impact on performance disparities between AAE and Mainstream American English tweets, and assess parsing accuracy for specific AAE lexical and syntactic features. Our annotated data and a parsing model are available at: http://slanglab.cs. umass.edu/TwitterAAE/.
Evaluation of exposure to effervescent drugs in a large health check-up population in France: a cross-sectional study

Objectives The relationship between high dietary sodium intake and hypertension is well established. Some drugs are associated with high-sodium content, particularly effervescent tablets (ETs). Despite a possible cardiovascular risk associated with the use of such drugs, observational data describing exposure to ETs in ambulatory subjects are lacking. This study aims to estimate the prevalence of exposure to ETs and to highlight factors associated with this exposure in a large French health check-up population. Design This was a cross-sectional study. Setting and participants Participants were French individuals who underwent medical check-ups at the Investigations Préventives et Cliniques centre between April and June 2017. Results In total, 1043 subjects were included in the study. The prevalence of exposure to ETs in the last 30 days was 26.9% (95% CI 24.2% to 29.6%). Exposure was frequent (ie, two ETs per week or more in the last 30 days) for 7.3% of subjects. Self-medication was the major source of exposure (93.8%). Paracetamol, aspirin, vitamins and betaine accounted for 95.3% of the ETs used. The factors associated with this exposure by multivariate analysis were: male gender, Overseas French origin, depression and body mass index ≥25 kg/m2. A diagnosis of hypertension or treatment with diuretics were not protective factors against exposure to ETs. Conclusion Exposure to ETs is frequent in the general population, particularly through self-medication. Clinical conditions associated with low-salt requirements were not associated with lower exposure to ETs, suggesting a lack of awareness by practitioners and patients about this iatrogenic issue.
Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.
TCP-GEN Framework to Achieve High Performance for HAIPE-Encrypted TCP Traffic in a Satellite Communication Environment

A satellite communication environment has a high latency and a high data error rate, and thus the performance of TCP is greatly impaired. To overcome this, Performance Enhancing Proxies (PEPs) are commonly deployed around the satellite links. However, the operation of PEPs is disabled when TCP traffic is encrypted by High Assurance Internet Protocol Encryptions (HAIPE). As a result the performance of the HAIPE-encrypted TCP traffic across satellite links becomes very low. Numerous approaches have been proposed to resolve this problem, but a practical solution is yet to be developed. In this research, we developed a method that can achieve the high performance offered by PEPs for HAIPE-encrypted TCP traffic across satellite links. This method encodes and relays the original TCP flow information across HAIPE without any modification to the existing HAIPE while preserving the same level of security. It then reconstructs new TCP streams and encapsulates HAIPE-encrypted original TCP packets in them. These new TCP streams can be natively handled by PEPs and thus the full TCP performance can be achieved. This method is also applicable to both IPv4 and IPv6. However, this scheme faces a challenge of handling TCP-over-TCP that suffers from a phenomenon called TCP meltdown. We propose a method that can prevent TCP meltdown and briefly describe it.
On the Performance of the DNPS-Based Relay Networks under Masquerading Attack

In the relay networks, two typical issues of physical layer security are selfishness and garbling. As a matter of fact, a certain nontypical but severely harmful misbehavior can also remove the cooperative diversity gain. Here, we coin the masquerading attack to indicate this kind of misbehavior. A masquerade relay can always pretend to be the best one to forward signals and, in consequence, deprive the others of the opportunities to cooperate. To the best of our knowledge, the impact of the masquerading attack has not yet been fully investigated. In this paper, multiple masquerade relays with random masquerading behavior are taken into account. Also, the complete channel effects, including the effects of the flat Rayleigh fading, log-normal shadowing, and path loss, are considered such that the geographical effects of the network topology can be completely captured. At last, the impact of the masquerade relays are evaluated in terms of the outage probability and end-to-end capacity.
Invalid certificates in modern browsers: A socio-technical analysis

The authentication of a web server is a crucial procedure in the security of web browsing. It relies on certificate validation, a process that may require the participation of the user. Thus, the security of certificate validation is socio-technical as it depends on traditional security technology as well as on social elements such as cultural values, trust and human-computer interaction. This manuscript analyzes extensively the socio-technical security of certificate validation as carried out through today’s most popular browsers. First, we model processes, protocols and ceremonies that browsers run with servers and users as UML activity diagrams. We consider both classic and private browsing modes and focus on the certificate validation. We then translate each UML activity diagram to a CSP# model. The model is expanded with the LTL formalization of five socio-technical properties pivoted on user involvement with certificate validation. We automatically check whether the CSP# models are socio-technically secure against Man-in-the-Middle attacks using the PAT model checker. The findings turn out to be far from straightforward. From them, we state best-practice recommendations to browser vendors.
Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks

We present a new neural sequence-to-sequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP-NET that models the interaction of key words and salient sentences using a new two-level pointer network based architecture. SWAP-NET identifies both salient sentences and key words in an input document, and then combines them to form the extractive summary. Experiments on large scale benchmark corpora demonstrate the efficacy of SWAP-NET that outperforms state-of-the-art extractive summarizers.
Inductive Two-layer Modeling with Parametric Bregman Transfer

Latent prediction models, exemplified by multilayer networks, employ hidden variables that automate abstract feature discovery. They typically pose nonconvex optimization problems and effective semi-definite programming (SDP) relaxations have been developed to enable global solutions (Aslan et al., 2014). However, these models rely on nonparametric training of layer-wise kernel representations, and are therefore restricted to transductive learning which slows down test prediction. In this paper, we develop a new inductive learning framework for parametric transfer functions using matching losses. The result for ReLU utilizes completely positive matrices, and the inductive learner not only delivers superior accuracy but also offers an order of magnitude speedup over SDP with constant approximation guarantees.
Assessing the impact of requirements review on quality outcomes

Studies demonstrate that differences in how product development organizations and product realization organizations interpret requirements can lead to poor‐quality outcomes. Other research shows that in comparison to communicating requirements inside an organization, communicating requirements in buyer‐supplier relationships can be more difficult and has been identified as a source of rework, poor quality, and increased costs. The Raytheon Company identified misaligned requirements interpretation between the company and its suppliers as a source of program performance inefficiency and therefore instituted a collaborative requirements review process between the company and its critical suppliers. This study examines the impact of these requirements reviews on quality outcomes and demonstrates that these reviews result in positive quality outcomes. In addition, correlation analysis shows a relationship between conducting the requirements reviews and a reduction in failure modes that are characterized by high detection and remediation effort. Considered broadly, our methodology can be applied to other program performance improvement efforts to test their impact on quality outcomes and to reveal related failure modes.
An Optimized and Secure Authentication Scheme for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) offer many interesting services such as infotainment applications and safety information to vehicles. In order to protect these networks, we should ensure the confidentiality, integrity and authentication for V2I (Vehicles to Infrastructure) communications and V2V (Vehicle to Vehicle) communications. Nevertheless, in VANET environments, mobility of the vehicles is very high, so that a sound security can increase the Quality of Service (QoS) of the protocol. "The Dual Authentication and Key Management Techniques" is a solution that is created to authenticate and secure VANET communication. However, it suffers from some lack of security aspects. In this paper, we devise an improvement to this scheme in order to get a deliberate compromise between security and quality of service.
Automatic Sign Detection with Application to an Assistive Robot

This paper explores automatic detection and classiﬁcation of exit signs with the aim of enabling a service robot to assist the visually impaired with indoor navigation, inspired by a guide dog. The ultimate aim is to achieve autonomous indoor navigation using computer vision to identify navigational goals in an unfamiliar environment. In particular, we focus on the task of exiting a building by following exit signs that may include arrows that indicate where the next door or sign is located. The proposed method utilizes a deep learning framework, Faster Regional Convolutional Neural Network (Faster R-CNN), to classify and localize exit signs in real time. The Faster R-CNN model achieved competitive results on more sizable dataset 1 than existing approaches.
Monsieur Poirot: Detecting Botnets Using Re-Identification Algorithm and Nontrivial Feature Selection Technique

Modern botnets are progressively migrating to P2P network to resist against take-down attempts. In addition, new botnets use randomization in their behavior to evade detection. In this paper, we propose a new method for detecting stealthy P2P bots. We formulate the problem as a re-identification problem. This opens the possibility of powerful instantiations of detection algorithms to address the botnet detection problem. We also use a nontrivial feature selection technique to discover the best feature pairs for conducting comparison between two flows. We use real-world botnet data to evaluate the performance of Monsieur Poirot and compare it with existing flow-based algorithms. Monsieur Poirot is robust towards injection of noise in the communication patterns. The experimental results show that Monsieur Poirot is able to identify P2P bots with an average TPR of 98.65% and an average FPR of 0.21%.
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation

To achieve high translation performance, neural machine translation models usually rely on the beam search algorithm for decoding sentences. The beam search finds good candidate translations by considering multiple hypotheses of translations simultaneously. However, as the algorithm produces hypotheses in a monotonic left-to-right order, a hypothesis can not be revisited once it is discarded. We found such monotonicity forces the algorithm to sacrifice some good decoding paths. To mitigate this problem, we relax the monotonic constraint of the beam search by maintaining all found hypotheses in a single priority queue and using a universal score function for hypothesis selection. The proposed algorithm allows discarded hypotheses to be recovered in a later step. Despite its simplicity, we show that the proposed decoding algorithm enhances the quality of selected hypotheses and improve the translations even for high-performance models in English-Japanese translation task.
Modeling Malware as a Language

Malware detection and malware construction are evolving in parallel. As malware authors incorporate evasive techniques into malware construction, antivirus software developers incorporate new static and dynamic analysis techniques into malware detection and classification with the aim of thwarting such evasive techniques. In this paper, we propose a new approach to static malware analysis, aiming to treat malware analysis as natural language analysis. We propose modeling malware as a language and assess the feasibility of finding semantics in instances of that language. We concretize this abstract problem into a classification task. Given a large dataset of malware instances categorized into 9 classes, we isolate strong semantic similarities between malware instances of the same class and classify unknown instances by strength of similarity to a class. Our approach consists of a proposed method for defining a malware-language, where malware instances are documents written in that language. We use the word2vec model to generate a computational representation of such documents and choose a document-distance as the measure of semantic closeness between them. We classify malware-documents by applying the k nearest neighbors algorithm (kNN). Validating our model using leave-one-out cross validation, we record a classification accuracy of up to 98%. We conclude that we can find, and ultimately manipulate semantics in malware.