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Saturday, April 16, 2016
Anonymous
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Function Handles Anonymous Functions Inline Functions
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Anonymous Client
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Man leaves anonymous $1000 tip for college-bound waitress
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Viagra Anonymous
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[FD] Announcing NorthSec 2016 - Montreal, May 19-22
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[FD] Microsoft Internet Explorer 11 MSHTML.DLL Remote Binary Planting Vulnerability
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Mercury and Crescent Moon Set
Friday, April 15, 2016
Handschrift Camphuysen
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Orioles Video: Mark Trumbo launches two homers in the 9-run 7th inning of 11-5 comeback win over the Rangers (ESPN)
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[FD] [ERPSCAN-16-002] SAP HANA - log injection and no size restriction
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[FD] [ERPSCAN-16-001] SAP NetWeaver 7.4 - XSS vulnerability
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[FD] PfSense Community Edition Multiple Vulnerabilities
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Re: [FD] end of useable crypto in browsers?
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Re: [FD] end of useable crypto in browsers?
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Canadian Police obtained Master Key to Crack BlackBerry Messenger Encryption
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Report: Nothing useful found on San Bernardino Shooter's iPhone
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ISS Daily Summary Report – 04/14/16
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Shopaholics Anonymous Unite
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Full Venus and Crescent Moon Rise
Thursday, April 14, 2016
Email verification for anonymous failed
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2016 Schedule Released: Ravens open season Sept. 11 vs. Bills; key games Nov. 6 vs. Steelers, Dec. 12 MNF at Patriots (ESPN)
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Visual Storytelling. (arXiv:1604.03968v1 [cs.CL])
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The first release of this dataset, SIND v.1, includes 81,743 unique photos in 20,211 sequences, aligned to both descriptive (caption) and story language. We establish several strong baselines for the storytelling task, and motivate an automatic metric to benchmark progress. Modelling concrete description as well as figurative and social language, as provided in this dataset and the storytelling task, has the potential to move artificial intelligence from basic understandings of typical visual scenes towards more and more human-like understanding of grounded event structure and subjective expression.
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A General Framework for Describing Creative Agents. (arXiv:1604.04096v1 [cs.AI])
Computational creativity is a subfield of AI focused on developing and studying creative systems. Few academic studies analysing the behaviour of creative agents from a theoretical viewpoint have been proposed. The proposed frameworks are vague and hard to exploit; moreover, such works are focused on a notion of creativity tailored for humans.
In this paper we introduce General Creativity, which extends that traditional notion. General Creativity provides the basis for a formalised theoretical framework, that allows one to univocally describe any creative agent, and their behaviour within societies of creative systems. Given the growing number of AI creative systems developed over recent years, it is of fundamental importance to understand how they could influence each other as well as how to gauge their impact on human society. In particular, in this paper we exploit the proposed framework for (i) identifying different forms of creativity; (ii) describing some typical creative agents behaviour, and (iii) analysing the dynamics of societies in which both human and non-human creative systems coexist.
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An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems. (arXiv:1604.04138v1 [cs.NE])
Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's $t$-test, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.
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A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy. (arXiv:1604.04146v1 [cs.NE])
A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics.
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A Deterministic Annealing Approach to the Multiple Traveling Salesmen and Related Problems. (arXiv:1604.04169v1 [math.OC])
This paper presents a novel and efficient heuristic framework for approximating the solutions to the multiple traveling salesmen problem (m-TSP) and other variants on the TSP. The approach adopted in this paper is an extension of the Maximum-Entropy-Principle (MEP) and the Deterministic Annealing (DA) algorithm. The framework is presented as a general tool that can be suitably adapted to a number of variants on the basic TSP. Additionally, unlike most other heuristics for the TSP, the framework presented in this paper is independent of the edges defined between any two pairs of nodes. This makes the algorithm particularly suited for variants such as the close-enough traveling salesman problem (CETSP) which are challenging due to added computational complexity. The examples presented in this paper illustrate the effectiveness of this new framework for use in TSP and many variants thereof.
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Harnessing Deep Neural Networks with Logic Rules. (arXiv:1603.06318v2 [cs.LG] UPDATED)
Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce unpredictability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop an iterative distillation method that transfers the structured information of logic rules into the weights of neural networks. We deploy the framework on a CNN for sentiment analysis, and an RNN for named entity recognition. With a few highly intuitive rules, we obtain substantial improvements and achieve state-of-the-art or comparable results to previous best-performing systems.
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Microsoft Sues US Govt Over Unconstitutional Secret Data Requests
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Mysterious Italian novelist could become first-ever anonymous Booker winner
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activate question from anonymous user default status pending
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Anti-Encryption Bill Released, would Kill your Privacy and Security
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Re: [FD] end of useable crypto in browsers?
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Journalist Matthew Keys gets 2-Year Prison term for helping Anonymous Hackers
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Re: [FD] end of useable crypto in browsers?
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Re: [FD] end of useable crypto in browsers?
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Re: [FD] end of useable crypto in browsers?
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[FD] DAVOSET v.1.2.8
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ISS Daily Summary Report – 04/13/16
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[FD] Django CMS v3.2.3 - Filter Bypass & Persistent Vulnerability
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I have a new follower on Twitter
BOCA HOY
Seguí la mejor actualidad de #Boca! #BocaHoy #0Descensos #UnicoGrande https://t.co/HAquU0OqLv
Following: 2640 - Followers: 3399
April 14, 2016 at 04:02AM via Twitter http://twitter.com/BocaHoycom
Orion in Red and Blue
Wednesday, April 13, 2016
Orioles Video: Chris Davis drills a 3-0 pitch over the Green Monster to give Baltimore the lead (ESPN)
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Strategyproof Peer Selection. (arXiv:1604.03632v1 [cs.GT])
Peer review, evaluation, and selection is the foundation on which modern science is built. Funding bodies the world over employ experts to study and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more universal: a professional society may want give a subset of its members awards based on the opinions of all the members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision making problem studied in computer science, economics, political science, and beyond. First, we detail a novel mechanism that is strategyproof, i.e., agents cannot benefit themselves by reporting insincere valuations, in addition to other desirable normative properties. Second, we demonstrate the effectiveness of our mechanism through a comprehensive simulation based comparison of our mechanism with a suite of mechanisms found in the computer science and economics literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it can be used as a randomized method to addresses the ubiquitous apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided fairly.
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A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents. (arXiv:1604.03655v1 [cs.DS])
We consider the well-studied cake cutting problem in which the goal is to find an envy-free allocation based on queries from the agents. The problem has received attention in computer science, mathematics, and economics. It has been a major open problem whether there exists a bounded and discrete envy-free protocol. We resolve the problem by proposing a discrete and bounded envy-free protocol for any number of agents.
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Learning Interactive Affordance for Human-Robot Interaction. (arXiv:1604.03692v1 [cs.RO])
In this paper, we present an approach for robot learning of affordance from human activity videos. We consider the problem particularly in the context of human-robot interaction: Our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to complete each sub-event (i.e., sub-goal). This enables the robot to infer its own movement in reaction to the human body motion, allowing it to naturally replicate such interactions.
We introduce the representation of interactive affordance and propose a generative model for its weakly supervised learning from human demonstration videos. Our approach discovers critical steps (i.e., latent sub-events) in an interaction and the typical motion associated with them, learning what body-parts should be involved and how. The experimental results demonstrate that our Markov Chain Monte Carlo (MCMC) based learning algorithm automatically discovers semantically meaningful interactive affordance from RGB-D videos, which allows us to generate appropriate full body motion for an agent.
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HordeQBF: A Modular and Massively Parallel QBF Solver. (arXiv:1604.03793v1 [cs.LO])
The recently developed massively parallel satisfiability (SAT) solver HordeSAT was designed in a modular way to allow the integration of any sequential CDCL-based SAT solver in its core. We integrated the QCDCL-based quantified Boolean formula (QBF) solver DepQBF in HordeSAT to obtain a massively parallel QBF solver---HordeQBF. In this paper we describe the details of this integration and report on results of the experimental evaluation of HordeQBF's performance. HordeQBF achieves superlinear average and median speedup on the hard application instances of the 2014 QBF Gallery.
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Hierarchical Compound Poisson Factorization. (arXiv:1604.03853v1 [cs.LG])
Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scalable recommendation systems with extreme sparsity. HPF, however, suffers from a tight coupling of sparsity model (absence of a rating) and response model (the value of the rating), which limits the expressiveness of the latter. Here, we introduce hierarchical compound Poisson factorization (HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to high-dimensional extremely sparse matrices. More importantly, HCPF decouples the sparsity model from the response model, allowing us to choose the most suitable distribution for the response. HCPF can capture binary, non-negative discrete, non-negative continuous, and zero-inflated continuous responses. We compare HCPF with HPF on nine discrete and three continuous data sets and conclude that HCPF captures the relationship between sparsity and response better than HPF.
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Single-Image Depth Perception in the Wild. (arXiv:1604.03901v1 [cs.CV])
This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.
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Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics. (arXiv:1604.03912v1 [cs.AI])
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is significantly influenced by both. Current IRL approaches assume that if the transition model is unknown, additional samples from the system's dynamics are accessible, or the observed behavior provides enough samples of the system's dynamics to solve the inverse problem accurately. These assumptions are often not satisfied. To overcome this, we present a gradient-based IRL approach that simultaneously estimates the system's dynamics. By solving the combined optimization problem, our approach takes into account the bias of the demonstrations, which stems from the generating policy. The evaluation on a synthetic MDP and a transfer learning task shows improvements regarding the sample efficiency as well as the accuracy of the estimated reward functions and transition models.
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Learning with Memory Embeddings. (arXiv:1511.07972v6 [cs.AI] UPDATED)
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Latent variable models are well suited to deal with the high dimensionality and sparsity of typical knowledge graphs. In recent publications the embedding models were extended to also consider temporal evolutions, temporal patterns and subsymbolic representations. In this paper we map embedding models, which were developed purely as solutions to technical problems for modelling temporal knowledge graphs, to various cognitive memory functions, in particular to semantic and concept memory, episodic memory, sensory memory, short-term memory, and working memory. We discuss learning, query answering, the path from sensory input to semantic decoding, and relationships between episodic memory and semantic memory. We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models. There are three main hypotheses. The first one is that semantic memory is described as triples and that episodic memory is described as triples in time. A second main hypothesis is that generalized entities have unique latent representations which are shared across memory functions and that are the basis for prediction, decision support and other functionalities executed by working memory. A third main hypothesis is that the latent representation for a time $t$, which summarizes all sensory information available at time $t$, is the basis for episodic memory. The proposed model includes both a recall of previous memories and the mental imagery of future events and sensory impressions.
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Plan Explicability and Predictability for Robot Task Planning. (arXiv:1511.08158v2 [cs.AI] UPDATED)
Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in "natural ways", and work that investigated legible motion planning, there lacks a general solution for high level task planning. To address this issue, we introduce the notions of plan {\it explicability} and {\it predictability}. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with human subjects using physical robots to show the effectiveness of our approach
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Submodular Optimization under Noise. (arXiv:1601.03095v2 [cs.DS] UPDATED)
We consider the problem of maximizing a monotone submodular function under noise, which to the best of our knowledge has not been studied in the past. There has been a great deal of work on optimization of submodular functions under various constraints, with many algorithms that provide desirable approximation guarantees. However, in many applications we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under cardinality constraints when given access to a noisy oracle of the function. We show that:
For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$;
For $k=1$ there is an approximation algorithm whose approximation ratio is arbitrarily close to $1/2$ in expectation. No randomized algorithm can obtain an approximation ratio in expectation better than $1/2+O(1/\sqrt n)$ and $(2k - 1)/2k + O(1/\sqrt{n})$ for general $k$;
If the noise is adversarial, no non-trivial approximation guarantee can be obtained.
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An Online Mechanism for Ridesharing in Autonomous Mobility-on-Demand Systems. (arXiv:1603.02208v2 [cs.AI] UPDATED)
With proper management, Autonomous Mobility-on-Demand (AMoD) systems have great potential to satisfy the transport demands of urban populations by providing safe, convenient, and affordable ridesharing services. Meanwhile, such systems can substantially decrease private car ownership and use, and thus significantly reduce traffic congestion, energy consumption, and carbon emissions. To achieve this objective, an AMoD system requires private information about the demand from passengers. However, due to self-interestedness, passengers are unlikely to cooperate with the service providers in this regard. Therefore, an online mechanism is desirable if it incentivizes passengers to truthfully report their actual demand. For the purpose of promoting ridesharing, we hereby introduce a posted-price, integrated online ridesharing mechanism (IORS) that satisfies desirable properties such as ex-post incentive compatibility, individual rationality, and budget-balance. Numerical results indicate the competitiveness of IORS compared with two benchmarks, namely the optimal assignment and an offline, auction-based mechanism.
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Former Editor Sentenced for Helping Anonymous
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MLB: Orioles (7-0) looking to remain the only perfect team in baseball against David Ortiz, Red Sox; watch live on ESPN2 (ESPN)
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MLB: Orioles (7-0) looking to remain the only perfect team in baseball against David Ortiz, Red Sox; watch live on ESPN2 (ESPN)
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Orioles: Eddie Matz says \"maybe it's time to start believing in the Orioles\" after taking down David Price, Red Sox (ESPN)
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Anonymous Coward
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British Authorities Order Hacker Lauri Love to hand Over Encryption Keys
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Anonymous and Message
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Ravens: Despite going 5-11, John Harbaugh says \"Last year wasn't a failure. (It) was setting us up for what's coming\" (ESPN)
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So, FBI Director also Puts Tape Over His Webcam
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ISS Daily Summary Report – 04/12/16
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[FD] Webline CMS (2016Q2) - SQL Injection Vulnerability
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