OpenRaiser/Intern-Atlas huggingface dataset:
Fruit Quantity and Quality Estimation using a Robotic Vision System
Venue: arXiv cs.CV 2018
Year: 2018

Accurate localisation of crop remains highly challenging in unstructured environments such as farms. Many of the developed systems still rely on the use of hand selected features for crop identification and often neglect the estimation of crop quantity and quality, which is key to assigning labor during farming processes. To alleviate these limitations we present a robotic vision system that can accurately estimate the quantity and quality of sweet pepper (Capsicum annuum L), a key horticultural crop. This system consists of three parts: detection, quality estimation, and tracking. Efficient detection is achieved using the FasterRCNN framework. Quality is then estimated in the same framework by learning a parallel layer which we show experimentally results in superior performance than treating quality as extra classes in the traditional Faster-RCNN framework. Evaluation of these two techniques outlines the improved performance of the parallel layer, where we achieve an F1 score of 77.3 for the parallel technique yet only 72.5 for the best scoring (red) of the multi-class implementation. To track the crop we present a tracking via detection approach, which uses the FasterRCNN with parallel layers, that is also a vision-only solution. This approach is cheap to implement as it only requires a camera and in experiments across 2 days we show that our proposed system can accurately estimate the number of sweet pepper present, within 4.1% of the ground truth.
Light-weight pixel context encoders for image inpainting
Venue: arXiv cs.CV 2018
Year: 2018

In this work we propose Pixel Content Encoders (PCE), a light-weight image inpainting model, capable of generating novel con-tent for large missing regions in images. Unlike previously presented convolutional neural network based models, our PCE model has an order of magnitude fewer trainable parameters. Moreover, by incorporating dilated convolutions we are able to preserve fine grained spatial information, achieving state-of-the-art performance on benchmark datasets of natural images and paintings. Besides image inpainting, we show that without changing the architecture, PCE can be used for image extrapolation, generating novel content beyond existing image boundaries.
White or Blue, the Whale gets its Vengeance: A Social Media Analysis of the Blue Whale Challenge
Venue: arXiv cs.SI 2018
Year: 2018

The Blue Whale Challenge is a series of self-harm causing tasks that are propagated via online social media under the disguise of a "game." The list of tasks must be completed in a duration of 50 days and they cause both physical and mental harm to the player. The final task is to commit suicide. The game is supposed to be administered by people called "curators" who incite others to cause self-mutilation and commit suicide. The curators and potential players are known to contact each other on social networking websites and the conversations between them are suspected to take place mainly via direct messages which are difficult to track. Though, in order to find curators, the players make public posts containing certain hashtags/keywords to catch their attention. Even though a lot of these social networks have moderated posts talking about the game, yet some posts manage to pass their filters. Our research focuses on (1) understanding the social media spread of the challenge, (2) spotting the behaviour of the people taking interest in Blue Whale challenge and, (3) analysing demographics of the users who may be involved in playing the game.
On Decoding Schemes for the MDPC-McEliece Cryptosystem
Venue: arXiv cs.CR 2018
Year: 2018

Recently, it has been shown how McEliece public-key cryptosystems based on moderate-density parity-check (MDPC) codes allow for very compact keys compared to variants based on other code families. In this paper, classical (iterative) decoding schemes for MPDC codes are considered. The algorithms are analyzed with respect to their error-correction capability as well as their resilience against a recently proposed reaction-based key-recovery attack on a variant of the MDPC-McEliece cryptosystem by Guo, Johansson and Stankovski (GJS). New message-passing decoding algorithms are presented and analyzed. Two proposed decoding algorithms have an improved error-correction performance compared to existing hard-decision decoding schemes and are resilient against the GJS reaction-based attack for an appropriate choice of the algorithm's parameters. Finally, a modified belief propagation decoding algorithm that is resilient against the GJS reaction-based attack is presented.
Face Recognition via Centralized Coordinate Learning
Venue: arXiv cs.CV 2018
Year: 2018

Owe to the rapid development of deep neural network (DNN) techniques and the emergence of large scale face databases, face recognition has achieved a great success in recent years. During the training process of DNN, the face features and classification vectors to be learned will interact with each other, while the distribution of face features will largely affect the convergence status of network and the face similarity computing in test stage. In this work, we formulate jointly the learning of face features and classification vectors, and propose a simple yet effective centralized coordinate learning (CCL) method, which enforces the features to be dispersedly spanned in the coordinate space while ensuring the classification vectors to lie on a hypersphere. An adaptive angular margin is further proposed to enhance the discrimination capability of face features. Extensive experiments are conducted on six face benchmarks, including those have large age gap and hard negative samples. Trained only on the small-scale CASIA Webface dataset with 460K face images from about 10K subjects, our CCL model demonstrates high effectiveness and generality, showing consistently competitive performance across all the six benchmark databases.
StreetGen : In base city scale procedural generation of streets: road network, road surface and street objects
Venue: arXiv cs.OH 2018
Year: 2018

Streets are large, diverse, and used for several (and possibly conflicting) transport modalities as well as social and cultural activities. Proper planning is essential and requires data. Manually fabricating data that represent streets (street reconstruction) is error-prone and time consuming. Automatising street reconstruction is a challenge because of the diversity, size, and scale of the details (few centimetres for cornerstone) required. The state-of-the-art focuses on roads (no context, no urban features) and is strongly determined by each application (simulation, visualisation, planning). We propose a unified framework that works on real Geographic Information System (GIS) data and uses a strong, yet simple modelling hypothesis when possible to robustly model streets at the city level or street level. Our method produces a coherent street-network model containing topological traffic information, road surface and street objects. We demonstrate the robustness and genericity of our method by reconstructing the entire city of Paris streets and exploring other similar reconstruction (airport driveway).
M-STAR: A Modular, Evidence-based Software Trustworthiness Framework
Venue: arXiv cs.CR 2018
Year: 2018

Despite years of intensive research in the field of software vulnerabilities discovery, exploits are becoming ever more common. Consequently, it is more necessary than ever to choose software configurations that minimize systems' exposure surface to these threats. In order to support users in assessing the security risks induced by their software configurations and in making informed decisions, we introduce M-STAR, a Modular Software Trustworthiness ARchitecture and framework for probabilistically assessing the trustworthiness of software systems, based on evidence, such as their vulnerability history and source code properties. Integral to M-STAR is a software trustworthiness model, consistent with the concept of computational trust. Computational trust models are rooted in Bayesian probability and Dempster-Shafer Belief theory, offering mathematical soundness and expressiveness to our framework. To evaluate our framework, we instantiate M-STAR for Debian Linux packages, and investigate real-world deployment scenarios. In our experiments with real-world data, M-STAR could assess the relative trustworthiness of complete software configurations with an error of less than 10%. Due to its modular design, our proposed framework is agile, as it can incorporate future advances in the field of code analysis and vulnerability prediction. Our results point out that M-STAR can be a valuable tool for system administrators, regular users and developers, helping them assess and manage risks associated with their software configurations.
Random Construction of Partial MDS Codes
Venue: arXiv cs.IT 2018
Year: 2018

This work deals with partial MDS (PMDS) codes, a special class of locally repairable codes, used for distributed storage system. We first show that a known construction of these codes, using Gabidulin codes, can be extended to use any maximum rank distance code. Then we define a standard form for the generator matrices of PMDS codes and use this form to give an algebraic description of PMDS generator matrices. This implies that over a sufficiently large finite field a randomly chosen generator matrix in PMDS standard form generates a PMDS code with high probability. This also provides sufficient conditions on the field size for the existence of PMDS codes.
Batch Auction Design For Cloud Container Services
Venue: arXiv cs.GT 2018
Year: 2018

Cloud containers represent a new, light-weight alternative to virtual machines in cloud computing. A user job may be described by a container graph that specifies the resource profile of each container and container dependence relations. This work is the first in the cloud computing literature that designs efficient market mechanisms for container based cloud jobs. Our design targets simultaneously incentive compatibility, computational efficiency, and economic efficiency. It further adapts the idea of batch online optimization into the paradigm of mechanism design, leveraging agile creation of cloud containers and exploiting delay tolerance of elastic cloud jobs. The new and classic techniques we employ include: (i) compact exponential optimization for expressing and handling non-traditional constraints that arise from container dependence and job deadlines; (ii) the primal-dual schema for designing efficient approximation algorithms for social welfare maximization; and (iii) posted price mechanisms for batch decision making and truthful payment design. Theoretical analysis and trace-driven empirical evaluation verify the efficacy of our container auction algorithms.
Security in Mobile Edge Caching with Reinforcement Learning
Venue: arXiv cs.CR 2018
Year: 2018

Mobile edge computing usually uses cache to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this paper, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present light-weight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.
Faster Learning by Reduction of Data Access Time
Venue: arXiv cs.LG 2018
Year: 2018

Nowadays, the major challenge in machine learning is the Big Data challenge. The big data problems due to large number of data points or large number of features in each data point, or both, the training of models have become very slow. The training time has two major components: Time to access the data and time to process (learn from) the data. So far, the research has focused only on the second part, i.e., learning from the data. In this paper, we have proposed one possible solution to handle the big data problems in machine learning. The idea is to reduce the training time through reducing data access time by proposing systematic sampling and cyclic/sequential sampling to select mini-batches from the dataset. To prove the effectiveness of proposed sampling techniques, we have used Empirical Risk Minimization, which is commonly used machine learning problem, for strongly convex and smooth case. The problem has been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each using two step determination techniques, namely, constant step size and backtracking line search method. Theoretical results prove the same convergence for systematic sampling, cyclic sampling and the widely used random sampling technique, in expectation. Experimental results with bench marked datasets prove the efficacy of the proposed sampling techniques and show up to six times faster training.
Toward Scalable Verification for Safety-Critical Deep Networks
Venue: arXiv cs.AI 2018
Year: 2018

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state of the art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification.
On a Generic Security Game Model
Venue: arXiv cs.CR 2018
Year: 2018

To protect the systems exposed to the Internet against attacks, a security system with the capability to engage with the attacker is needed. There have been attempts to model the engagement/interactions between users, both benign and malicious, and network administrators as games. Building on such works, we present a game model which is generic enough to capture various modes of such interactions. The model facilitates stochastic games with imperfect information. The information is imperfect due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over other multiple states, we use Euclidean distances between the outputs of the sensors. We build a 5-state game to represent the interaction of the administrator with the user. The states correspond to 1) the user being out of the system in the Internet, and after logging in to the system; 2) having low privileges; 3) having high privileges; 4) when he successfully attacks and 5) gets trapped in a honeypot by the administrator. Each state has its own action set. We present the game with a distinct perceived action set corresponding to each distinct information set of these states. The model facilitates stochastic games with imperfect information. The imperfect information is due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over the states, we use Euclidean distances between outputs of the sensors. A numerical simulation of an example game is presented to show the evaluation of rewards to the players and the preferred strategies. We also present the conditions for formulating the strategies when dealing with more than one attacker and making collaborations.
A universality theorem for allowable sequences with applications
Venue: arXiv cs.CG 2018
Year: 2018

Order types are a well known abstraction of combinatorial properties of a point set. By Mnëv's universality theorem for each semi-algebraic set $V$ there is an order type with a realization space that is \emph{stably equivalent} to $V$. We consider realization spaces of \emph{allowable sequences}, a refinement of order types. We show that the realization spaces of allowable sequences are \emph{universal} and consequently deciding the realizability is complete in the \emph{existential theory of the reals} (\ER). This result holds even if the realization space of the order type induced by the allowable sequence is non-empty. Furthermore, we argue that our result is a useful tool for further geometric reductions. We support this by giving \ER-hardness proofs for the realizability of abstract convex geometries and for the recognition problem of visibility graphs of polygons with holes using the hardness result for allowable sequences. This solves two longstanding open problems.
In-RDBMS Hardware Acceleration of Advanced Analytics
Venue: arXiv cs.DB 2018
Year: 2018

The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently. As such, there is a void of solutions that enables hardware acceleration at the intersection of these disjoint fields. This paper sets out to be the initial step towards a unifying solution for in-Database Acceleration of Advanced Analytics (DAnA). Deploying specialized hardware, such as FPGAs, for in-database analytics currently requires hand-designing the hardware and manually routing the data. Instead, DAnA automatically maps a high-level specification of advanced analytics queries to an FPGA accelerator. The accelerator implementation is generated for a User Defined Function (UDF), expressed as a part of an SQL query using a Python-embedded Domain-Specific Language (DSL). To realize an efficient in-database integration, DAnA accelerators contain a novel hardware structure, Striders, that directly interface with the buffer pool of the database. Striders extract, cleanse, and process the training data tuples that are consumed by a multi-threaded FPGA engine that executes the analytics algorithm. We integrate DAnA with PostgreSQL to generate hardware accelerators for a range of real-world and synthetic datasets running diverse ML algorithms. Results show that DAnA-enhanced PostgreSQL provides, on average, 8.3x end-to-end speedup for real datasets, with a maximum of 28.2x. Moreover, DAnA-enhanced PostgreSQL is, on average, 4.0x faster than the multi-threaded Apache MADLib running on Greenplum. DAnA provides these benefits while hiding the complexity of hardware design from data scientists and allowing them to express the algorithm in =30-60 lines of Python.
rcss: Subgradient and duality approach for dynamic programming
Venue: arXiv cs.MS 2018
Year: 2018

This short paper gives an introduction to the \emph{rcss} package. The R package \emph{rcss} provides users with a tool to approximate the value functions in the Bellman recursion using convex piecewise linear functions formed using operations on tangents. A pathwise method is then used to gauge the quality of the numerical results.
Lab Based Curriculum for CIS and Related Technology
Venue: arXiv cs.CY 2018
Year: 2018

The Computer Information System (CIS) is information and communication technology in support of business processes. In this paper, we present a typical undergraduate computer information system curriculum examining the degree of lab intensity and its effect on the course efficacy. A CIS program is usually part of the school of business as it is in support of business processes. We also explore the differences between a CIS curriculum and other computer related technology courses, such as Information Technology (IT), Computer Science (CS), and Software Engineering (SE). The curriculum is composed of several elements such as content and sequence of subjects, classrooms equipped with computer projection, internet, and local network access, and appropriate computing and software infrastructure. We will focus on the importance and adequacy of labs for the CIS curriculum. The proposed CIS curriculum works for a 4-year as well as a 3-year program. This paper provides a recommendation for local and Federal Accreditation agencies and curriculum committees.
Degree-constrained 2-partitions of graphs
Venue: arXiv cs.DS 2018
Year: 2018

A $(δ\geq k_1,δ\geq k_2)$-partition of a graph $G$ is a vertex-partition $(V_1,V_2)$ of $G$ satisfying that $δ(G[V_i])\geq k_i$ for $i=1,2$. We determine, for all positive integers $k_1,k_2$, the complexity of deciding whether a given graph has a $(δ\geq k_1,δ\geq k_2)$-partition. We also address the problem of finding a function $g(k_1,k_2)$ such that the $(δ\geq k_1,δ\geq k_2)$-partition problem is ${\cal NP}$-complete for the class of graphs of minimum degree less than $g(k_1,k_2)$ and polynomial for all graphs with minimum degree at least $g(k_1,k_2)$. We prove that $g(1,k)=k$ for $k\ge 3$, that $g(2,2)=3$ and that $g(2,3)$, if it exists, has value 4 or 5.
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Venue: arXiv cs.CV 2018
Year: 2018

The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a result, the research challenge shifts from devising algorithms to creating suitable and abundant training data for supervised learning. How to efficiently create such training data? The dominant data acquisition method in visual recognition is based on web data and manual annotation. Yet, for many computer vision problems, such as stereo or optical flow estimation, this approach is not feasible because humans cannot manually enter a pixel-accurate flow field. In this paper, we promote the use of synthetically generated data for the purpose of training deep networks on such tasks.We suggest multiple ways to generate such data and evaluate the influence of dataset properties on the performance and generalization properties of the resulting networks. We also demonstrate the benefit of learning schedules that use different types of data at selected stages of the training process.
Minimum spanning tree release under differential privacy constraints
Venue: arXiv cs.CR 2018
Year: 2018

We investigate the problem of nodes clustering under privacy constraints when representing a dataset as a graph. Our contribution is threefold. First we formally define the concept of differential privacy for structured databases such as graphs, and give an alternative definition based on a new neighborhood notion between graphs. This definition is adapted to particular frameworks that can be met in various application fields such as genomics, world wide web, population survey, etc. Second, we introduce a new algorithm to tackle the issue of privately releasing an approximated minimum spanning tree topology for a simple-undirected-weighted graph. It provides a simple way of producing the topology of a private almost minimum spanning tree which outperforms, in most cases, the state of the art "Laplace mechanism" in terms of weight-approximation error. Finally, we propose a theoretically motivated method combining a sanitizing mechanism (such as Laplace or our new algorithm) with a Minimum Spanning Tree (MST)-based clustering algorithm. It provides an accurate method for nodes clustering in a graph while keeping the sensitive information contained in the edges weights of the private graph. We provide some theoretical results on the robustness of an almost minimum spanning tree construction for Laplace sanitizing mechanisms. These results exhibit which conditions the graph weights should respect in order to consider that the nodes form well separated clusters both for Laplace and our algorithm as sanitizing mechanism. The method has been experimentally evaluated on simulated data, and preliminary results show the good behavior of the algorithm while identifying well separated clusters.