Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Online Traffic Statistical Matching for Optimizing Packet Filtering Performance

Packet classification plays a critical role in many of the current networking technologies, and efficient yet lightweight packet classification techniques are highly crucial for their successful deployment. Most of the current packet classification techniques exploit the characteristics of classification policies, without considering the traffic behavior in optimizing their search data structures. In this paper, we present novel techniques that utilize traffic characteristics coupled with careful analysis of the policy to obtain adaptive methods that can accommodate varying traffic statistics while maintaining a high throughput. The first technique uses segmentation of the traffic space to achieve disjoint subsets of traffic properties and build bounded depth Huffman trees using the statistics collected for these segments. The second technique simplifies the structure maintenance by keeping the segments ordered in a most-recently-used (MRU) list instead of a tree. The techniques are evaluated and their performance are compared. Moreover, attacks targeting the firewall performance are discussed and corresponding protection schemes are presented.
Novel Approach for Secure Communication of Digital Signature using on-Demand Multipath Routing for Enhancing Network Security in Wireless Mobile Ad Hoc Networks

-- With the advances of wireless communication technologies, small-size and high-performance computing and communication devices like laptops and personal digital assistants are increasingly used in daily life. After the success of second generation mobile system, more interest was started in wireless communications. This interest has led to two types of wireless networks such as infrastructured networks and infrastructure less networks. The infrastructure less networks is also called as Mobile Ad hoc Networks (MANETs). The MANETs are essentially suitable when infrastructure is not present or difficult or costly to setup or when network setup is to be done quickly within a short period. They are very active for tactical communication in the military, rescue missions and natural disasters like earth quake, flood, tsunami, fire and emergency services. They are also expected to play an important role in the civilian as a convention centers, conferences, and electronic classrooms. One best example is attack on world trade center at New York in USA in 2001. Many such applications require security and Quality of Service (QoS) guarantees. These applications are extremely delay-sensitive and need high packet delivery ratio. In this kind of networks routing, security and key management are important and also complex problems. They will be exposed to all kinds of attacks. A fundamental problem is how to deliver data packets among nodes confidentially and efficiently without predetermined topology or centralized control in the insured MANET. In such an open hostile environment where there is a need of network security services that can provide secure communication in such public network is most significant. In this paper, we propose a novel approach to enhance the confidentiality service by using Multipath Routing that allows the communication between two parties by preventing various attacks. We compare its performance by using various parameters and we have reduced the probability that message is eavesdropped when multiple paths are used.
Fast and Secure Distributed Learning in High Dimension

Modern machine learning is distributed and the work of several machines is typically aggregated by \emph{averaging} which is the optimal rule in terms of speed, offering a speedup of $n$ (with respect to using a single machine) when $n$ processes are learning together. Distributing data and models poses however fundamental vulnerabilities, be they to software bugs, asynchrony, or worse, to malicious attackers controlling some machines or injecting misleading data in the network. Such behavior is best modeled as Byzantine failures, and averaging does not tolerate a single one from a worker. Krum, the first provably Byzantine resilient aggregation rule for SGD only uses one worker per step, which hampers its speed of convergence, especially in best case conditions when none of the workers is actually Byzantine. An idea, coined multi-Krum, of using $m$ different workers per step was mentioned, without however any proof neither on its Byzantine resilience nor on its slowdown. More recently, it was shown that in high dimensional machine learning, guaranteeing convergence is not a sufficient condition for \emph{strong} Byzantine resilience. A improvement on Krum, coined Bulyan, was proposed and proved to guarantee stronger resilience. However, Bulyan suffers from the same weakness of Krum: using only one worker per step. This adds up to the aforementioned open problem and leaves the crucial need for both fast and strong Byzantine resilience unfulfilled. The present paper proposes using Bulyan over Multi-Krum (we call it Multi-Bulyan), a combination for which we provide proofs of strong Byzantine resilience, as well as an ${\frac{m}{n}}$ slowdown, compared to averaging, the fastest (but non Byzantine resilient) rule for distributed machine learning, finally we prove that Multi-Bulyan inherits the $O(d)$ merits of both multi-Krum and Bulyan.
Interpret Federated Learning with Shapley Values

Federated Learning is introduced to protect privacy by distributing training data into multiple parties. Each party trains its own model and a meta-model is constructed from the sub models. In this way the details of the data are not disclosed in between each party. In this paper we investigate the model interpretation methods for Federated Learning, specifically on the measurement of feature importance of vertical Federated Learning where feature space of the data is divided into two parties, namely host and guest. For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party. We propose a method to balance the model interpretability and data privacy in vertical Federated Learning by using Shapley values to reveal detailed feature importance for host features and a unified importance value for federated guest features. Our experiments indicate robust and informative results for interpreting Federated Learning models.
A Method of Automatically Evaluating Machine Translations Using a Word-alignment-based Classifier

Constructing a classifier that distinguishes machine translations from human translations is a promising approach to automatically evaluating machine-translated sentences. We developed a classifier with this approach that distinguishes translations based on word-alignment distributions between source sentences and human/machine translations. We used Support Vector Machines as machinelearning algorithms for this classifier. Our experimental results revealed that our method of evaluation had a weak correlation with human evaluations. We further found that our method outperformed wellknown automatic-evaluation metrics with respect to correlation with the manual evaluation, and that it could identify the qualitative characteristics of machine translations, which greatly help improve their quality.
Passport: Enabling Accurate Country-Level Router Geolocation using Inaccurate Sources

When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance.
Magnetic Helicity of Solar Wind Fluctuations at Ion-kinetic Scales

We use magnetic helicity to characterise solar wind fluctuations at ion-kinetic scales. For the first time, we separate the contributions to helicity from fluctuations propagating at angles quasi-parallel and oblique to the local mean magnetic field, $\mathbf{B}_0$. We find that the helicity of quasi-parallel fluctuations is consistent with Alfven-ion cyclotron and fast magnetosonic-whistler modes driven by proton temperature anisotropy instabilities and the presence of a relative drift between $\alpha$-particles and protons. We also show that the helicity of oblique fluctuations has little dependence on proton temperature anisotropy and is consistent with kinetic Alfven wave-like fluctuations from the anisotropic turbulent cascade. Our results provide evidence that the cascade does not produce quasi-parallel propagating fluctuations at ion-kinetic scales in the solar wind.
Programmable Spectrometry - Per-pixel Classification of Materials using Learned Spectral Filters

Many materials have distinct spectral profiles. This facilitates estimation of the material composition of a scene at each pixel by first acquiring its hyperspectral image, and subsequently filtering it using a bank of spectral profiles. This process is inherently wasteful since only a set of linear projections of the acquired measurements contribute to the classification task. We propose a novel programmable camera that is capable of producing images of a scene with an arbitrary spectral filter. We use this camera to optically implement the spectral filtering of the scene's hyperspectral image with the bank of spectral profiles needed to perform per-pixel material classification. This provides gains both in terms of acquisition speed --- since only the relevant measurements are acquired --- and in signal-to-noise ratio --- since we invariably avoid narrowband filters that are light inefficient. Given training data, we use a range of classical and modern techniques including SVMs and neural networks to identify the bank of spectral profiles that facilitate material classification. We verify the method in simulations on standard datasets as well as real data using a lab prototype of the camera.
Object Detection in Specific Traffic Scenes using YOLOv2

object detection framework plays crucial role in autonomous driving. In this paper, we introduce the real-time object detection framework called You Only Look Once (YOLOv1) and the related improvements of YOLOv2. We further explore the capability of YOLOv2 by implementing its pre-trained model to do the object detecting tasks in some specific traffic scenes. The four artificially designed traffic scenes include single-car, single-person, frontperson-rearcar and frontcar-rearperson.
Sine-wave amplitude coding using wavelet basis functions

by Pankaj Oberoi S.B. , Massachusetts Institute of Technology (1991) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY Feburary 1996 © Pankaj Oberoi, MCMXCVI. The author hereby grants to MIT permission to reproduce and to distribute copies of this thesis document in whole or in part, and to grant others the right to do so. J j
Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold

We consider optimization problems over the Stiefel manifold whose objective function is the summation of a smooth function and a nonsmooth function. Existing methods for solving this kind of problems can be classified into three classes. Algorithms in the first class rely on information of the subgradients of the objective function and thus tend to converge slowly in practice. Algorithms in the second class are proximal point algorithms, which involve subproblems that can be as difficult as the original problem. Algorithms in the third class are based on operator-splitting techniques, but they usually lack rigorous convergence guarantees. In this paper, we propose a retraction-based proximal gradient method for solving this class of problems. We prove that the proposed method globally converges to a stationary point. Iteration complexity for obtaining an $\epsilon$-stationary solution is also analyzed. Numerical results on solving sparse PCA and compressed modes problems are reported to demonstrate the advantages of the proposed method.
Retrieval of MPEG-7 based Semantic Descriptions

With the growing amount of people using the Internet, and creating digital content and information, knowledge retrieval becomes a critical task. Knowledge retrieval allows people to find content they search for when they need it. Ongoing efforts provide frameworks and standards for annotating digital and non-digital content semantically to describe resources more precisely and processable in comparison to simple descriptive structured and unstructured metadata. One example for a framework for semantic annotations is MPEG-7. But although the MPEG group provides with MPEG-7 a useful and well defined theoretical framework for the creation of semantic annotation, retrieval of the annotations is not discussed. In this paper the authors present a retrieval process for MPEG-7 based semantic annotations founded on well proved information retrieval techniques, namely query expansion and regular expressions. Additionally a prototype implementation extending an existing MPEG-7 retrieval application will be presented.
Dtwiki: a disconnection and intermittency tolerant wiki

Wikis have proven to be a valuable tool for collaboration and content generation on the web. Simple semantics and ease-of-use make wiki systems well suited for meeting many emerging region needs in the areas of education, collaboration and local content generation. Despite their usefulness, current wiki software does not work well in the network environments found in emerging regions. For example, it is common to have long-lasting network partitions due to cost, power and poor connectivity. Network partitions make a traditional centralized wiki architecture unusable due to the unavailability of the central server. Existing solutions towards addressing connectivity problems include web-caching proxies and snapshot distribution. While proxies and snapshots allow wiki data to be read while disconnected, they prevent users from contributing updates back to the wiki. In this paper we detail the design and implementation of DTWiki, a wiki system which explicitly addresses the problem of operating a wiki system in an intermittent environment. The DTWiki system is able to cope with long-lasting partitions and bad connectivity while providing the functionality of popular wiki software such as MediaWiki and TWiki.
Development of robotics tools for agricultural task achievement: The example of robot formation control

Nowadays it is eagerly expected that, on one hand the environmental impact of agricultural activities is decreasing, and on the other hand the level of production is increasing in order to match the consumption demand of the growing worldwide population. To meet these opposite expectations, new production tools have to be developed. Recent advances in off-road mobile robotics may bring to promising solution in order to address such a problematic. Since large area coverage, as well as high flexibility, are more particularly looked for in this paper, multi-robot cooperation for field operations is investigated. The proposed framework relies on a path tracking approach dedicated to the formation control of several robots. In an off-road context, because of bad grip conditions, classical control algorithms cannot be used straightforwardly. Moreover, low level delays and stability issues (rollover risk or obstacle collision) have also to be taken into account. Adaptive and predictive control laws are here designed to achieve accurate motion control of each robot, despite highly varying and unpredictable contact conditions. The multi-robot configuration is imposed by defining set points in terms of lateral deviations and longitudinal distances between each robot. In addition, robot safety is addressed through a traversability evaluation within the vicinity of each robot. Finally, several alternative paths are constantly considered and the optimal one is decided with respect to robot stability and maximal velocity criteria, assessed from a numerical terrain model.
The LOFAR Tied-Array All-Sky Survey (LOTAAS): Survey overview and initial pulsar discoveries

We present an overview of the LOFAR Tied-Array All-Sky Survey (LOTAAS) for radio pulsars and fast transients. The survey uses the high-band antennas of the LOFAR Superterp, the dense inner part of the LOFAR core, to survey the northern sky (δ > 0°) at a central observing frequency of 135 MHz. A total of 219 tied-array beams (coherent summation of station signals, covering 12 square degrees), as well as three incoherent beams (covering 67 square degrees) are formed in each survey pointing. For each of the 222 beams, total intensity is recorded at 491.52 μs time resolution. Each observation integrates for 1 hr and covers 2592 channels from 119 to 151 MHz. This instrumental setup allows LOTAAS to reach a detection threshold of 1–5 mJy for periodic emission. Thus far, the LOTAAS survey has resulted in the discovery of 73 radio pulsars. Among these are two mildly recycled binary millisecond pulsars (P = 13 and 33 ms), as well as the slowest-spinning radio pulsar currently known (P = 23.5 s). The survey has thus far detected 311 known pulsars, with spin periods ranging from 4 ms to 5.0 s and dispersion measures from 3.0 to 217 pc cm−3. Known pulsars are detected at flux densities consistent with literature values. We find that the LOTAAS pulsar discoveries have, on average, longer spin periods than the known pulsar population. This may reflect different selection biases between LOTAAS and previous surveys, though it is also possible that slower-spinning pulsars preferentially have steeper radio spectra. LOTAAS is the deepest all-sky pulsar survey using a digital aperture array; we discuss some of the lessons learned that can inform the approach for similar surveys using future radio telescopes such as the Square Kilometre Array.
Partially Specified Space Time Autoregressive Model with Artificial Neural Network

Spatial autoregressive model, introduced by Clif and Ord in 1970s has been widely applied in many areas of science and econometrics such as regional economics, public finance, political sciences, agricultural economics, environmental studies and transportation analyses. As information technology grows rapidly, observations are seldom independent from others so a space autoregressive models can take this dependence into account and draw more reliable conclusions between covariates and the target variable itself. Based on the classical spatial model, Su and Jin proposed a semi-parametric model named as partially specified spatial autoregressive model (PSAR) to allow for more flexibility in modeling. And to estimate this nonparametric component, we use the neural network model which adds more flexibility to the classical model and allows for variations in the choice of activation functions according to different types of data. This paper extends an artificial neural network model to a partially specified space autoregressive model and proposes maximum likelihood estimators instead of quasi-maximum likelihood estimates. We establish the consistency and asymptotic normality of the estimators in this model. These results are obtained under some standard conditions in spatial models as well as neural network models. To illustrate, we investigate the quality of the normal approximation for finite samples by means of numerical simulation studies with three common choices of error distributions (standard normal, student-t distribution and the Laplace distribution). We apply our proposed model to a soil-water tension problem and a criminal study in Chicago. The results showed that our model can capture the spatial dependence between units as well as the unknown correlation structure between the target variable and covariates.
Modular Organization of the ESX-5 Secretion System in Mycobacterium tuberculosis

Mycobacteria utilize type VII secretion systems (T7SS) to export many of their important virulence proteins. The T7SS encompasses five homologous secretion systems (ESX-1 to ESX-5). Most pathogenic mycobacterial species, including the human pathogen Mycobacterium tuberculosis, possess all five ESX systems. The ESX-1, -3, and -5 systems are important for virulence of mycobacteria but the molecular mechanisms of their secretion apparatus and the identity and activity of secreted effector proteins are not well characterized. The different ESX systems show similarities in gene composition due to their common phylogenetic origin but recent studies demonstrate mechanistic as well as functional variations between the systems. For example, the ESX-1 system is involved in lysis of the phagosomal membrane and phagosomal escape of the bacteria while the ESX-5 system is required for mycobacterial cell wall stability and host cell lysis. Mechanistically, the ESX-1 substrates show interdependence during secretion while the ESX-5 system may use a duplicated four-gene region (ESX-5a) as an accessory system for transport of a subset of proteins of the ESX-5 secretome. In the present review we will provide an overview of the molecular components of the T7SS and their function with a particular focus on the ESX-5 system.