OpenRaiser/Intern-Atlas huggingface dataset:
Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research
Venue: arXiv cs.NI 2018
Year: 2018

Anonymity services have seen high growth rates with increased usage in the past few years. Among various services, Tor is one of the most popular peer-to-peer anonymizing service. In this survey paper, we summarize, analyze, classify and quantify 26 years of research on the Tor network. Our research shows that `security' and `anonymity' are the most frequent keywords associated with Tor research studies. Quantitative analysis shows that the majority of research studies on Tor focus on `deanonymization' the design of a breaching strategy. The second most frequent topic is analysis of path selection algorithms to select more resilient paths. Analysis shows that the majority of experimental studies derived their results by deploying private testbeds while others performed simulations by developing custom simulators. No consistent parameters have been used for Tor performance analysis. The majority of authors performed throughput and latency analysis.
Towards an MDD Based Framework for Self Adaptive IoT Applications Development
Venue: arXiv cs.SE 2018
Year: 2018

As technology and communication advances, more devices (and things) are able to connect to the Internet and talk to each other to achieve a common goal which results in the emergence of the Internet of Things (IoT) era. It is believed that IoT will bring up a limitless number of applications and business opportunities that will affect almost every aspect of our life. Research has already been conducted to investigate the challenges that obstruct the realization of IoT along with the promising solutions that pave the way for the acceptance and enabling of IoT. Among the research areas that is of a great importance to making IoT paradigm possible is the presence of a unified programming framework that masks the heterogeneity of the involved devices of the IoT platform. Such a framework guides system developers throughout the IoT application development process. In this paper, we investigate the IoT concept and its high level architecture in general and focus more on the application development aspect. We believe that IoT applications are highly dynamic in nature and thus need to be engineered with the self adaptive and autonomic concepts in mind. Therefore, our proposed IoT software development lifecycle was based on the IBM architecture blueprint for autonomic systems. To cater for the runtime dynamic and heterogeneity aspects of IoT applications, we adopt the MDD paradigm for our proposed development framework. We highlight the core requirements of a resilient development framework that accommodates the necessary concepts and processes for a successful IoT application.
Challenges: Bridge between Cloud and IoT
Venue: arXiv cs.SE 2018
Year: 2018

In the real time processing, a new emerging technology where the need of connecting smart devices with cloud through Internet has raised. IoT devices processed information is to be stored and accessed anywhere needed with a support of powerful computing performance, efficient storage infrastructure for heterogeneous systems and software which configures and controls these different devices. A lot of challenges to be addressed are listed with this new emerging technology as it needs to be compatible with the upcoming 5G wireless devices too. In this paper, the benefits and challenges of this innovative paradigm along with the areas open to do research are shown.
Multi-objective evolution for 3D RTS Micro
Venue: arXiv cs.NE 2018
Year: 2018

We attack the problem of controlling teams of autonomous units during skirmishes in real-time strategy games. Earlier work had shown promise in evolving control algorithm parameters that lead to high performance team behaviors similar to those favored by good human players in real-time strategy games like Starcraft. This algorithm specifically encoded parameterized kiting and fleeing behaviors and the genetic algorithm evolved these parameter values. In this paper we investigate using influence maps and potential fields alone to compactly represent and control real-time team behavior for entities that can maneuver in three dimensions. A two-objective fitness function that maximizes damage done and minimizes damage taken guides our multi-objective evolutionary algorithm. Preliminary results indicate that evolving friend and enemy unit potential field parameters for distance, weapon characteristics, and entity health suffice to produce complex, high performing, three-dimensional, team tactics.
Digital Identity: The Effect of Trust and Reputation Information on User Judgement in the Sharing Economy
Venue: arXiv cs.CY 2018
Year: 2018

The Sharing Economy (SE) is a growing ecosystem focusing on peer-to-peer enterprise. In the SE the information available to assist individuals (users) in making decisions focuses predominantly on community generated trust and reputation information. However, how such information impacts user judgement is still being understood. To explore such effects, we constructed an artificial SE accommodation platform where we varied the elements related to hosts' digital identity, measuring users' perceptions and decisions to interact. Across three studies, we find that trust and reputation information increases not only the users' perceived trustworthiness, credibility, and sociability of hosts, but also the propensity to rent a private room in their home. This effect is seen when providing users both with complete profiles and profiles with partial user-selected information. Closer investigations reveal that three elements relating to the host's digital identity are sufficient to produce such positive perceptions and increased rental decisions, regardless of which three elements are presented. Our findings have relevant implications for human judgment and privacy in the SE, and question its current culture of ever increasing information-sharing.
Some Problems in Compact Message Passing
Venue: arXiv cs.DC 2018
Year: 2018

This paper seeks to address the question of designing distributed algorithms for the setting of compact memory i.e. sublinear bits working memory for arbitrary connected networks. The nodes in our networks may have much lower internal memory as compared to the number of their possible neighbours implying that a node may not be able to store all the IDs of its neighbours. These algorithms are useful for large networks of small devices such as the Internet of Things, for wireless or ad-hoc networks, and, in general, as memory efficient algorithms. We introduce the Compact Message Passing(CMP) model;an extension of the standard message passing model considered at a finer granularity where a node can interleave reads and writes with internal computations, using a port only once in a round. The interleaving is required for meaningful computations due to the low memory requirement and is akin to a distributed network with nodes executing streaming algorithms. Note that the internal memory size upper bounds the message sizes and hence e.g. for log-memory, the model is weaker than the Congest model; for such models our algorithms will work directly too. We present early results in the CMP model for nodes with log^2-memory. We introduce the concepts of local compact functions and compact protocols and give solutions for some classic distributed problems (leader election, tree constructions and traversals). We build on these to solve the open problem of compact preprocessing for the compact self-healing routing algorithm CompactFTZ posed in Compact Routing Messages in Self-Healing Trees(TCS2017) by designing local compact functions for finding particular subtrees of labeled binary trees. Hence, we introduce the first fully compact self-healing routing algorithm. We also give independent fully compact versions of the Forgiving Tree[PODC08] and Thorup-Zwick's tree based compact routing[SPAA01].
An exploratory study on how Internet of Things developing companies handle User Experience Requirements
Venue: arXiv cs.SE 2018
Year: 2018

[Context and motivation] Internet of Things (IoT) is becoming common throughout everyday lives. However, the interaction is often different from when using e.g. computers and other smart devices. Furthermore, an IoT device is often dependent on several other systems, heavily impacting the user experience (UX). Finally, the domain is changing rapidly and is driven by technological innovation. [Question/problem] In this qualitative study, we explore how companies elicit UX requirements in the context of IoT. A key part of contemporary IoT development is also data-driven approaches. Thus, these are also considered in the study. [Principal idea / Results] There is a knowledge gap around data-driven methodologies, there are examples of companies that collect large amount of data but do not always know how to utilize it. Furthermore, many of the companies struggle to handle the larger system context, where their products and the UX they control are only one part of the complete IoT ecosystem. [Contribution] We provide qualitative empirical data from IoT developing companies. Based on our findings, we identify challenges for the companies and areas for future work.
A Multiple-Input Multiple-Output Cepstrum
Venue: arXiv cs.SY 2018
Year: 2018

This paper extends the concept of scalar cepstrum coefficients from single-input single-output linear time invariant dynamical systems to multiple-input multiple-output models, making use of the Smith-McMillan form of the transfer function. These coefficients are interpreted in terms of poles and transmission zeros of the underlying dynamical system. We present a method to compute the MIMO cepstrum based on input/output signal data for systems with square transfer function matrices (i.e. systems with as many inputs as outputs). This allows us to do a model-free analysis. Two examples to illustrate these results are included: a simple MIMO system with 3 inputs and 3 outputs, of which the poles and zeros are known exactly, that allows us to directly verify the equivalences derived in the paper, and a case study on realistic data. This case study analyses data coming from a (model of) a non-isothermal continuous stirred tank reactor, which experiences linear fouling. We analyse normal and faulty operating behaviour, both with and without a controller present. We show that the cepstrum detects faulty behaviour, even when hidden by controller compensation. The code for the numerical analysis is available online.
Motion deblurring of faces
Venue: arXiv cs.CV 2018
Year: 2018

Face analysis is a core part of computer vision, in which remarkable progress has been observed in the past decades. Current methods achieve recognition and tracking with invariance to fundamental modes of variation such as illumination, 3D pose, expressions. Notwithstanding, a much less standing mode of variation is motion deblurring, which however presents substantial challenges in face analysis. Recent approaches either make oversimplifying assumptions, e.g. in cases of joint optimization with other tasks, or fail to preserve the highly structured shape/identity information. Therefore, we propose a data-driven method that encourages identity preservation. The proposed model includes two parallel streams (sub-networks): the first deblurs the image, the second implicitly extracts and projects the identity of both the sharp and the blurred image in similar subspaces. We devise a method for creating realistic motion blur by averaging a variable number of frames to train our model. The averaged images originate from a 2MF2 dataset with 10 million facial frames, which we introduce for the task. Considering deblurring as an intermediate step, we utilize the deblurred outputs to conduct a thorough experimentation on high-level face analysis tasks, i.e. landmark localization and face verification. The experimental evaluation demonstrates the superiority of our method.
Deep RNN-Oriented Paradigm Shift through BOCANet: Broken Obfuscated Circuit Attack
Venue: arXiv cs.CR 2018
Year: 2018

This is the first work augmenting hardware attacks mounted on obfuscated circuits by incorporating deep recurrent neural network (D-RNN). Logic encryption obfuscation has been used for thwarting counterfeiting, overproduction, and reverse engineering but vulnerable to attacks. There have been efficient schemes, e.g., satisfiability-checking (SAT) based attack, which can potentially compromise hardware obfuscation circuits. Nevertheless, not only there exist countermeasures against such attacks in the state-of-the-art (including the recent delay+logic locking (DLL) scheme in DAC'17), but the sheer amount of time/resources to mount the attack could hinder its efficacy. In this paper, we propose a deep RNN-oriented approach, called BOCANet, to (i) compromise the obfuscated hardware at least an order-of magnitude more efficiently (>20X faster with relatively high success rate) compared to existing attacks; (ii) attack such locked hardware even when the resources to the attacker are only limited to insignificant number of I/O pairs (< 0.5\%) to reconstruct the secret key; and (iii) break a number of experimented benchmarks (ISCAS-85 c423, c1355, c1908, and c7552) successfully.
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these. To address this limitation, we propose a novel saliency estimation model which leverages the semantic modelling power of conditional generative adversarial networks together with memory architectures which capture the subject's behavioural patterns and task dependent factors. We make contributions aiming to bridge the gap between bottom-up feature learning capabilities in modern deep learning architectures and traditional top-down hand-crafted features based methods for task specific saliency modelling. The conditional nature of the proposed framework enables us to learn contextual semantics and relationships among different tasks together, instead of learning them separately for each task. Our studies not only shed light on a novel application area for generative adversarial networks, but also emphasise the importance of task specific saliency modelling and demonstrate the plausibility of fully capturing this context via an augmented memory architecture.
On contact graphs of paths on a grid
Venue: arXiv cs.CG 2018
Year: 2018

In this paper we consider Contact graphs of Paths on a Grid (CPG graphs), i.e. graphs for which there exists a family of interiorly disjoint paths on a grid in one-to-one correspondence with their vertex set such that two vertices are adjacent if and only if the corresponding paths touch at a grid-point. Our class generalizes the well studied class of VCPG graphs (see [1]). We examine CPG graphs from a structural point of view which leads to constant upper bounds on the clique number and the chromatic number. Moreover, we investigate the recognition and 3-colorability problems for $B_0$-CPG, a subclass of CPG. We further show that CPG graphs are not necessarily planar and not all planar graphs are CPG.
Model Predictive Control for Autonomous Driving considering Actuator Dynamics
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we propose a new model predictive control (MPC) formulation for autonomous driving. The novelty of our MPC stems from the following results. Firstly, we adopt an alternating minimization approach wherein linear velocities and angular accelerations are alternately optimized. We show that in contrast to the joint optimization, the alternating minimization exploits the structure of the problem better, which in turn translates to reduction in computation time. Secondly, our MPC explicitly incorporates the time dependent non-linear actuator dynamics that captures the transient response of the vehicle for a given commanded velocity. This added complexity improves the predictive component of MPC resulting in improved margin of inter-vehicle distance during maneuvers like overtaking, lane-change, etc. Although, past works have also incorporated actuator dynamics within MPC, there has been very few attempts towards coupling actuator dynamics to collision avoidance constraints through the non-holonomic motion model of the vehicle and analyzing the resulting behavior. We use a high fidelity simulator to benchmark our actuator dynamics augmented MPC with other related approaches in terms of metrics like inter-vehicle distance, trajectory smoothness, and velocity overshoot.
Ensuring referential integrity under causal consistency
Venue: arXiv cs.DC 2018
Year: 2018

Referential integrity (RI) is an important correctness property of a shared, distributed object storage system. It is sometimes thought that enforcing RI requires a strong form of consistency. In this paper, we argue that causal consistency suffices to maintain RI. We support this argument with pseudocode for a reference CRDT data type that maintains RI under causal consistency. QuickCheck has not found any errors in the model.
Modelos de Resposta para Experimentos Randomizados em Redes Sociais de Larga Escala
Venue: arXiv cs.SI 2018
Year: 2018

A/B tests are randomized experiments frequently used by companies that offer services on the Web for assessing the impact of new features. During an experiment, each user is randomly redirected to one of two versions of the website, called treatments. Several response models were proposed to describe the behavior of a user in a social network website, where the treatment assigned to her neighbors must be taken into account. However, there is no consensus as to which model should be applied to a given dataset. In this work, we propose a new response model, derive theoretical limits for the estimation error of several models, and obtain empirical results for cases where the response model was misspecified.
Stable and Consistent Membership at Scale with Rapid
Venue: arXiv cs.DC 2018
Year: 2018

We present the design and evaluation of Rapid, a distributed membership service. At Rapid's core is a scheme for multi-process cut detection (CD) that revolves around two key insights: (i) it suspects a failure of a process only after alerts arrive from multiple sources, and (ii) when a group of processes experience problems, it detects failures of the entire group, rather than conclude about each process individually. Implementing these insights translates into a simple membership algorithm with low communication overhead. We present evidence that our strategy suffices to drive unanimous detection almost-everywhere, even when complex network conditions arise, such as one-way reachability problems, firewall misconfigurations, and high packet loss. Furthermore, we present both empirical evidence and analyses that proves that the almost-everywhere detection happens with high probability. To complete the design, Rapid contains a leaderless consensus protocol that converts multi-process cut detections into a view-change decision. The resulting membership service works both in fully decentralized as well as logically centralized modes. We present an evaluation of Rapid in moderately scalable cloud settings. Rapid bootstraps 2000 node clusters 2-5.8x faster than prevailing tools such as Memberlist and ZooKeeper, remains stable in face of complex failure scenarios, and provides strong consistency guarantees. It is easy to integrate Rapid into existing distributed applications, of which we demonstrate two.
Fault Localization Models in Debugging
Venue: arXiv cs.SE 2018
Year: 2018

Debugging is considered as a rigorous but important feature of software engineering process. Since more than a decade, the software engineering research community is exploring different techniques for removal of faults from programs but it is quite difficult to overcome all the faults of software programs. Thus, it is still remains as a real challenge for software debugging and maintenance community. In this paper, we briefly introduced software anomalies and faults classification and then explained different fault localization models using theory of diagnosis. Furthermore, we compared and contrasted between value based and dependencies based models in accordance with different real misbehaviours and presented some insight information for the debugging process. Moreover, we discussed the results of both models and manifested the shortcomings as well as advantages of these models in terms of debugging and maintenance.
Enhancing Evolutionary Conversion Rate Optimization via Multi-armed Bandit Algorithms
Venue: arXiv cs.NE 2018
Year: 2018

Conversion rate optimization means designing web interfaces such that more visitors perform a desired action (such as register or purchase) on the site. One promising approach, implemented in Sentient Ascend, is to optimize the design using evolutionary algorithms, evaluating each candidate design online with actual visitors. Because such evaluations are costly and noisy, several challenges emerge: How can available visitor traffic be used most efficiently? How can good solutions be identified most reliably? How can a high conversion rate be maintained during optimization? This paper proposes a new technique to address these issues. Traffic is allocated to candidate solutions using a multi-armed bandit algorithm, using more traffic on those evaluations that are most useful. In a best-arm identification mode, the best candidate can be identified reliably at the end of evolution, and in a campaign mode, the overall conversion rate can be optimized throughout the entire evolution process. Multi-armed bandit algorithms thus improve performance and reliability of machine discovery in noisy real-world environments.
Enhanced Optimization with Composite Objectives and Novelty Selection
Venue: arXiv cs.NE 2018
Year: 2018

An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. This paper proposes a solution: The original objectives are replaced by their linear combinations, thus focusing the search on the most useful tradeoffs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. In the highly deceptive problem of discovering minimal sorting networks, this approach finds better solutions, and finds them faster and more consistently than standard methods. It is therefore a promising approach to solving deceptive problems through multi-objective optimization.
Efficient Determination of Equivalence for Encrypted Data
Venue: arXiv cs.CR 2018
Year: 2018

Secure computation of equivalence has fundamental application in many different areas, including healthcare. We study this problem in the context of matching an individual identity to link medical records across systems. We develop an efficient solution for equivalence based on existing work that can evaluate the greater than relation. We implement the approach and demonstrate its effectiveness on data, as well as demonstrate how it meets regulatory criteria for risk.