Miracle-dz/UniFAR-FTUs huggingface dataset:
Previous research datasets DeBot ( from API ) Public Research Datasets Journalist attack Datasets Real User Dataset ( Through BFS ) Twitter User IDs User Profiles User Tweets Twitter API Profile Features Tweet Features

Botnets in online social networks are increasingly often affecting the regular flow of discussion, attacking regular users and their posts, spamming them with irrelevant or offensive content, and even manipulating the popularity of messages and accounts. Researchers and cybercriminals are involved in an arms race, and new and updated botnets designed to defeat current detection systems are constantly developed, rendering such detection systems obsolete. In this paper, we motivate the need for a generalized evaluation in Twitter bot detection and propose a methodology to evaluate bot classifiers by testing them on unseen bot classes. We show that this methodology is empirically robust, using bot classes of varying sizes and characteristics and reaching similar results, and argue that methods trained and tested on single bot classes or datasets might not able to generalize to new bot classes. We train one such classifier on over 200,000 data points and show that it achieves over 97% accuracy. The data used to train and test this classifier includes some of the largest and most varied collections of bots used in literature. We then test this theoretically sound classifier using our methodology, highlighting that it does not generalize well to unseen bot classes. Finally, we discuss the implications of our results, and reasons why some bot classes are easier and faster to detect than others.
A finite element method for nonlinear elliptic problems

(2013) A finite element method for nonlinear elliptic problems. This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract. We present a Galerkin method with piecewise polynomial continuous elements for fully nonlinear elliptic equations. A key tool is the discretization proposed in Lakkis and Pryer, 2011, allowing us to work directly on the strong form of a linear PDE. An added benefit to making use of this discretization method is that a recovered (finite element) Hessian is a byproduct of the solution process. We build on the linear method and ultimately construct two different methodologies for the solution of second order fully nonlinear PDEs. Benchmark numerical results illustrate the convergence properties of the scheme for some test problems as well as the Monge–Ampère equation and the Pucci equation. 1. Introduction. Fully nonlinear partial differential equations (PDEs) arise in many areas, including differential geometry (the Monge–Ampère equation), mass transportation (the Monge–Kantorovich problem), dynamic programming (the Bell-man equation), and fluid dynamics (the geostrophic equations). The numerical approximation of the solutions of such equations is thus an important scientific task. There are at least three main difficulties apparent when attempting to derive numerical methods for fully nonlinear equations. The first is the strong nonlinearity on the highest order derivative which generally precludes a variational formulation. The second is that a fully nonlinear equation does not always admit a classical solution, even if the problem data is smooth, and the solution has to sought in a generalized sense (e.g., viscosity solutions), which is …
Grouping time-varying data for interactive exploration

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
C R ] 7 D ec 2 00 5 Poseidon : a 2-tier Anomaly-based Network Intrusion Detection System ∗

We present Poseidon, a new anomaly based network intrusion detection system. Poseidon is payload-based, and has a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system [30]. Our benchmarks on the 1999 DARPA data set [22] show a higher detection rate and lower number of false positives than PAYL and PHAD.
MAC en tournoi avec fenètre de congestion constante pour le WLAN

In the context of radio distributed networks, we present a generalized approach for the Medium Access Control (MAC) with fixed congestion window. Our protocol is quite simple to analyze and can be used in a lot of different situations. We give mathematical evidence showing that our performance is tight, in the sense that no protocol with fixed congestion window can do better. We also place ourselves in the WiFi/WiMAX framework, and show experimental results enlightening collision reduction of 14% to 21% compared to the best known other methods. We show channel capacity improvement, and fairness considerations. MAC en tournoi avec fenètre de congestion constante pour le WLAN Résumé : Dans le contexte des réseaux radio distribués, nous présentons une approche généralisée du protocole de controle d'accès (MAC pour medium access control)) avec une fenètre de congestionà taille fixe. Notre protocole est simplè a analyser et peut-ˆ etre utilisé dans beaucoup de contextes différents. Nous don-nons des argument mathématiques pour prouver que la performance du protocol est optimale, dans le sens o` u aucun protocole avec fenètre de congestion fixe ne peut l'améliorer. Nous nous plaçons aussi dans le contexte WiFi/WiMAX, et nous montrons expérimentalement une réduction du taux de collision entre 14% et 21% par rapport aux meilleures méthodes connues. Nous montrons aussi l'amélioration obtenue en capacié de canal, et analysons les questions d'´ equité pour ce protocole.
MAC en tournoi avec fenètre de congestion constante pour le WLAN

In the context of radio distributed networks, we present a generalized approach for the Medium Access Control (MAC) with fixed congestion window. Our protocol is quite simple to analyze and can be used in a lot of different situations. We give mathematical evidence showing that our performance is tight, in the sense that no protocol with fixed congestion window can do better. We also place ourselves in the WiFi/WiMAX framework, and show experimental results enlightening collision reduction of 14% to 21% compared to the best known other methods. We show channel capacity improvement, and fairness considerations. MAC en tournoi avec fenètre de congestion constante pour le WLAN Résumé : Dans le contexte des réseaux radio distribués, nous présentons une approche généralisée du protocole de controle d'accès (MAC pour medium access control)) avec une fenètre de congestionà taille fixe. Notre protocole est simplè a analyser et peut-ˆ etre utilisé dans beaucoup de contextes différents. Nous donnons des argument mathématiques pour prouver que la performance du protocol est optimale, dans le sens o` u aucun protocole avec fenètre de congestion fixe ne peut l'améliorer. Nous nous plaçons aussi dans le contexte WiFi/WiMAX, et nous montrons expérimentalement une réduction du taux de collision entre 14% et 21% par rapport aux meilleures méthodes connues. Nous montrons aussi l'amélioration obtenue en capacié de canal, et analysons les questions d'´ equité pour ce protocole.
2 Timing Side-Channel Debugging with Discriminer

What properties about the internals of a program explain the possible differences in its overall running time for different inputs? In this paper, we propose a formal framework for considering this question we dub trace-set discrimination. We show that even though the algorithmic problem of computing maximum likelihood discriminants is NP-hard, approaches based on integer linear programming (ILP) and decision tree learning can be useful in zeroing-in on the program internals. On a set of Java benchmarks, we find that compactly-represented decision trees scalably discriminate with high accuracy—more scalably than maximum likelihood discriminants and with comparable accuracy. We demonstrate on three larger case studies how decision-tree discriminants produced by our tool are useful for debugging timing side-channel vulnerabilities (i.e., where a malicious observer infers secrets simply from passively watching execution times) and availability vulnerabilities.
SkIn: Skimming-Intensive Long-Text Classiﬁcation Based on BERT and Application to Medical Corpus

BERT is a widely used pre-trained model in natural language processing. However, because its time and space requirements increase with a quadratic level of the text length, the BERT model is diﬃcult to use directly on the long-text corpus. The collected text data is usually quite long in some ﬁelds, such as health care. Therefore, to apply the pre-trained language knowledge of BERT to long text, in this paper, imitating the skimming-intensive reading method used by humans when reading a long paragraph, the Sk imming-In tensive Model (SkIn) is proposed. It can dynamically select the critical information in the text so that the length of the input into the BERT-Base model is signiﬁcantly reduced, which can eﬀectively save the cost of the classiﬁcation algorithm. Experiments show that the SkIn method has achieved better results than the baselines on long-text classiﬁcation datasets in the medical ﬁeld, while its time and space requirements increase linearly with the text length, alleviating the time and space overﬂow problem of BERT on long-text data.
A Distribution Free Summarization Method for Affymetrix GeneChip Arrays

Motivation: Affymetrix GeneChip brand arrays require a summarization step in order to combine the information in a probe set into one value representing the expression level of the corresponding gene. Here we present a new summarization method, Distribution Free Weighted (DFW) fold change, that uses the information of fold change but does not make any distributional assumptions for the data. Results: Based on spikein data sets, we compare DFW with several popular methods, via both our own calculations and the ‘Affycomp II’ competition. The results show that DFW outperforms other methods when sensitivity and specificity are considered simultaneously. In fact, the area under the Receiver Operating Characteristic (ROC) curve for DFW is nearly 1.0 (a perfect value). Furthermore, DFW can obtain all the true positives with a small number of false positives. It is also computationally faster than most methods in current use. Availability: The R package for DFW is available upon request. Contact: mmcgee@mail.smu. edu
Epsilon-Nets for Halfspaces Revisited

Given a set $P$ of $n$ points in $\mathbb{R}^3$, we show that, for any $\varepsilon >0$, there exists an $\varepsilon$-net of $P$ for halfspace ranges, of size $O(1/\varepsilon)$. We give five proofs of this result, which are arguably simpler than previous proofs \cite{msw-hnlls-90, cv-iaags-07, pr-nepen-08}. We also consider several related variants of this result, including the case of points and pseudo-disks in the plane.
Wide-Residual-Inception Networks for Real-time Object Detection

Since convolutional neural network (CNN) models emerged, several tasks in computer vision have actively deployed CNN models for feature extraction. However, the conventional CNN models have a high computational cost and require high memory capacity, which is impractical and unaffordable for commercial applications such as real-time on-road object detection on embedded boards or mobile platforms. To tackle this limitation of CNN models, this paper proposes a wide-residual-inception (WR-Inception) network, which constructs the architecture based on a residual inception unit that captures objects of various sizes on the same feature map, as well as shallower and wider layers, compared to state-of-the-art networks like ResNets. To verify the proposed networks, this paper conducted two experiments; one is a classification task on CIFAR-10/100 and the other is an on-road object detection task using a Single-Shot Multi-box Detector (SSD) on the KITTI dataset. WR-Inception achieves comparable accuracy on CIFAR-10/100, with test errors at 4.82% and 23.12%, respectively, which outperforms 164-layer Pre-ResNets. In addition, the detection experiments demonstrate that the WR-Inception–based SSD outperforms ResNet-101–based SSD on KITTI. Besides, WR-Inception–based SSD achieves 16 frames per seconds, which is 3.85 times faster than ResNet-101-based SSD. We could expect WR-Inception to be used for real application systems.
Bayesian Selection for the l 2-Potts Model Regularization Parameter : 1-D Piecewise Constant Signal Denoising

Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whose automated selection remains an involved and challenging problem. Conversely, fully Bayesian formalisms encapsulate the regularization parameter selection into hierarchical models, at the price of high computational costs. This contribution proposes an operational strategy that combines hierarchical Bayesian and Potts model formulations, with the double aim of automatically tuning the regularization parameter and maintaining computational efficiency. The proposed procedure relies on formally connecting a Bayesian framework to a l2 -Potts functional. Behaviors and performance for the proposed piecewise constant denoising and regularization parameter tuning techniques are studied qualitatively and assessed quantitatively, and shown to compare favorably against those of a fully Bayesian hierarchical procedure, both in accuracy and computational load.
Liquidity in credit networks: a little trust goes a long way

Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the network by passing IOUs between trusting nodes in their respective currencies---and obviates the need for a common currency. Nodes can repeatedly transact with each other and pay for the transaction using trusted currency. A natural question to ask in this setting is: how long can the network sustain liquidity, i.e. how long can the network support the routing of payments before credit dries up? We answer this question in terms of the long term failure probability of transactions for various network topologies and credit values. We prove that the transaction failure probability is independent of the path along which transactions are routed. We show that under symmetric transaction rates, the transaction failure probability in a number of well-known graph families goes to zero as the size, density or credit capacity of the network increases. We also show via simulations that even networks of small size and credit capacity can route transactions with high probability if they are well-connected. Further, we characterize a centralized currency system as a special type of a star network (one where edges to the root have infinite credit capacity, and transactions occur only between leaf nodes) and compute the steady-state transaction failure probability in a centralized system. We show that liquidity in star networks, complete graphs and Erdos-Renyi networks is comparable to that in equivalent centralized currency systems; thus we do not lose much liquidity in return for their robustness and decentralized properties.
Using Inverse lambda and Generalization to Translate English to Formal Languages

We present a system to translate natural language sentences to formulas in a formal or a knowledge representation language. Our system uses two inverse lambda-calculus operators and using them can take as input the semantic representation of some words, phrases and sentences and from that derive the semantic representation of other words and phrases. Our inverse lambda operator works on many formal languages including first order logic, database query languages and answer set programming. Our system uses a syntactic combinatorial categorial parser to parse natural language sentences and also to construct the semantic meaning of the sentences as directed by their parsing. The same parser is used for both. In addition to the inverse lambda-calculus operators, our system uses a notion of generalization to learn semantic representation of words from the semantic representation of other words that are of the same category. Together with this, we use an existing statistical learning approach to assign weights to deal with multiple meanings of words. Our system produces improved results on standard corpora on natural language interfaces for robot command and control and database queries.
Visual Model-predictive Localization for Computationally Efficient Autonomous Racing of a 72-gram Drone

Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a strategy for autonomous drone racing which is computationally more efficient than navigation methods like visual inertial odometry and simultaneous localization and mapping. This fast light-weight vision-based navigation algorithm estimates the position of the drone by fusing race gate detections with model dynamics predictions. Theoretical analysis and simulation results show the clear advantage compared to Kalman filtering when dealing with the relatively low frequency visual updates and occasional large outliers that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named "Trashcan", which was equipped with a Jevois smart-camera for a total of 72g. The test track consists of 3 laps around a 4-gate racing track. The gates spaced 4 meters apart and can be displaced from their supposed position. An average speed of 2m/s is achieved while the maximum speed is 2.6m/s. To the best of our knowledge, this flying platform is the smallest autonomous racing drone in the world and is 6 times lighter than the existing lightest autonomous racing drone setup (420g), while still being one of the fastest autonomous racing drones in the world.
Generalizing DP-SGD with Shuffling and Batching Clipping

Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible ﬁrst order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shufﬂing. Our DP analysis follows the f -DP approach and introduces a new proof technique which allows us to also analyse group privacy. In particular, for E epochs work and groups of size g , we show a √ gE DP dependency for batch clipping with shufﬂing. This is much better than the previously anticipated linear dependency in g and is much better than the previously expected square root dependency on the total number of rounds within E epochs which is generally much more than √ E .
Voting-based Approaches for Differentially Private Federated Learning

Differentially Private Federated Learning (DPFL) is an emerging field with many 1 applications. Gradient averaging based DPFL methods require costly commu-2 nication rounds and hardly work with large-capacity models, due to the explicit 3 dimension dependence in its added noise. In this paper, inspired by the non-4 federated knowledge transfer privacy learning methods, we design two DPFL 5 algorithms ( AE-DPFL and kNN-DPFL ) that provide provable DP guarantees for 6 both instance-level and agent-level privacy regimes. By voting among the data 7 labels returned from each local model, instead of averaging the gradients, our 8 algorithms avoid the dimension dependence and significantly reduces the commu-9 nication cost. Theoretically, by applying secure multi-party computation, we could 10 exponentially amplify the (data-dependent) privacy guarantees when the margin 11 of the voting scores are distinctive. Empirical evaluation on both instance and 12 agent level DP is conducted across five datasets. When aligning privacy cost the 13 same, we show 2% to 12% higher accuracy compared to DP-FedAvg, or aligning 14 accuracy the same, we show that less than 65% privacy cost is achieved. 15
IJSRD || National Conference on Inspired Learning || October 2015

The introduction of nanosized pollutants in the natural environment is termed as Nanopluution or Nanocontamiation. Parallel to the growth of nanotechnology in every sphere of life, Nano contamination has also increased. It affects every aspect of our life especially plants and animals. It is an interdisciplinary field and it is very important to tackle this problem. For that we need trained personnel in this field. Unfortunately, there is a real shortage of any significant teaching strategies in the curriculum that can deal with the multidisciplinary character of this subject. This module is a small step in this direction. There has been very little study of effect of nanoparticles on plants. Keeping this in mind, the students were trained in preparation of silver nanoparticles, preparation of MS (Murashige and Skoog’s) nutrient medium, incorporation of silver NPs (Nanoparticles) into the nutrient medium, sterilization of seeds, culture conditions, in vitro studies, transfer of plantlets to soil and in silico studies.
Maximum likelihood estimation for discrete exponential families and random graphs

We characterize the existence of the maximum likelihood estimator for discrete exponential family on ﬁnite set. Our criterion is simple to apply as we show in various settings, most notably for exponential models of random graphs. As an application, we point out the size of independent identically distributed samples for which the maximum likelihood estimator exists with high probability. characterization of the existence of the maximum likelihood estimator (MLE) for exponential family and the data at hand. We also present applications, in particular for speciﬁc exponential families we give a threshold of the sample size suﬃcient for the existence of MLE with high probability for i.i. d. samples.