OpenRaiser/Intern-Atlas huggingface dataset:
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Venue: arXiv cs.AI 2019
Year: 2019

Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.
Selfish Mining in Ethereum
Venue: arXiv cs.CR 2019
Year: 2019

We study selfish mining in Ethereum. The problem is combinatorially more complex than in Bitcoin because of major differences in the reward system and a different difficulty adjustment formula. Equivalent strategies in Bitcoin do have different profitabilities in Ethereum. The attacker can either broadcast his fork one block by one, or keep them secret as long as possible and publish them all at once at the end of an attack cycle. The first strategy is damaging for substantial hashrates, and we show that the second strategy is even worse. This confirms what we already proved for Bitcoin: Selfish mining is most of all an attack on the difficulty adjustment formula. We show that the current reward for signaling uncle blocks is a weak incentive for the attacker to signal blocks. We compute the profitabilities of different strategies and find out that for a large parameter space values, strategies that do not signal blocks are the best ones. We compute closed-form formulas for the apparent hashrates for these strategies and compare them. We use a direct combinatorics analysis with Dyck words to find these closed-form formulas.
Analyzing Defense Strategies Against Mobile Information Leakages: A Game-Theoretic Approach
Venue: arXiv cs.GT 2019
Year: 2019

Abuse of zero-permission sensors on-board mobile and wearable devices to infer users' personal context and information is a well-known privacy threat that has received significant attention. Efforts towards protection mechanisms that prevent or limit the success of such threats, however, have been ad-hoc so far and have primarily focused on designing threat-specific customized defense mechanisms. Such approaches are not very practical, as evident from their limited adoption within major mobile/wearable operating systems. In the end, it is clear that all privacy threats that take advantage of unrestricted access to zero-permission sensors can be prevented if access to these sensors is regulated. However, due to the dynamic nature of sensor usage and requirements of different mobile applications, design of such access control mechanisms is not trivial. To effectively design an automated mobile defense mechanism that can dynamically measure the threat level of different sensor access requests from different applications and appropriately block suspicious requests, the problem of zero-permission sensor access needs to be first formally defined and analyzed. This paper accomplishes the above objective by employing game theory, specifically, signaling games, to analytically model the sensor access scenario for mobile applications, including, formalizing sensor access strategies of mobile applications and defense strategies of the on-board defense mechanism and the associated costs and benefits. Within the confines of a formal and practical game model, the paper then outlines conditions under which equilibria can be achieved between entities (applications and defense mechanism) with conflicting goals. The game model is further analyzed using numerical simulations, and also extended in the form of a repeated signaling game.
The Level Weighted Structural Similarity Loss: A Step Away from the MSE
Venue: arXiv cs.CV 2019
Year: 2019

The Mean Square Error (MSE) has shown its strength when applied in deep generative models such as Auto-Encoders to model reconstruction loss. However, in image domain especially, the limitation of MSE is obvious: it assumes pixel independence and ignores spatial relationships of samples. This contradicts most architectures of Auto-Encoders which use convolutional layers to extract spatial dependent features. We base on the structural similarity metric (SSIM) and propose a novel level weighted structural similarity (LWSSIM) loss for convolutional Auto-Encoders. Experiments on common datasets on various Auto-Encoder variants show that our loss is able to outperform the MSE loss and the Vanilla SSIM loss. We also provide reasons why our model is able to succeed in cases where the standard SSIM loss fails.
Characterizing Pairs Collaboration in a Mobile-equipped Shared-Wall Display Supported Collaborative Setup
Venue: arXiv cs.HC 2019
Year: 2019

Recent advancements in mobile devices encourage researchers to utilize them in collaborative environments as a medium to interact with large shared wall-displays. In this paper, we focus on a semi-controlled user study that we conducted to measure the collaborative coupling ratio between partners working in pairs in a collaborative setup equipped with a shared tiled-wall display and multiple mobile devices. We invited 36 participants in 18 pairs to take part in our experiment in order to analyze how they communicate and collaborate with each other during the experiment. We observed their collaborative coupling by measuring how often they verbally and visually communicated. Further, we found frequently used collaborative physical position patterns by observing the pairs' physical arrangements and standing positions. Moreover, we combined these factors to gain a clearer understanding of coupling in our setup, taking into account the mobility factor offered by the mobile devices. Results of the study show interesting findings about the coupling factors between the partners mainly due to the flexibility offered by including mobile devices in our collaborative setup.
Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers
Venue: arXiv cs.IT 2019
Year: 2019

Distributed implementations of gradient-based methods, wherein a server distributes gradient computations across worker machines, suffer from slow running machines, called 'stragglers'. Gradient coding is a coding-theoretic framework to mitigate stragglers by enabling the server to recover the gradient sum in the presence of stragglers. 'Approximate gradient codes' are variants of gradient codes that reduce computation and storage overhead per worker by allowing the server to approximately reconstruct the gradient sum. In this work, our goal is to construct approximate gradient codes that are resilient to stragglers selected by a computationally unbounded adversary. Our motivation for constructing codes to mitigate adversarial stragglers stems from the challenge of tackling stragglers in massive-scale elastic and serverless systems, wherein it is difficult to statistically model stragglers. Towards this end, we propose a class of approximate gradient codes based on balanced incomplete block designs (BIBDs). We show that the approximation error for these codes depends only on the number of stragglers, and thus, adversarial straggler selection has no advantage over random selection. In addition, the proposed codes admit computationally efficient decoding at the server. Next, to characterize fundamental limits of adversarial straggling, we consider the notion of 'adversarial threshold' -- the smallest number of workers that an adversary must straggle to inflict certain approximation error. We compute a lower bound on the adversarial threshold, and show that codes based on symmetric BIBDs maximize this lower bound among a wide class of codes, making them excellent candidates for mitigating adversarial stragglers.
A Scale-Consistent Approach for Recommender Systems
Venue: arXiv cs.SI 2019
Year: 2019

In this paper we propose and develop a relatively simple and efficient approach for estimating unknown elements of a user-rating matrix in the context of a recommender system (RS). The critical theoretical property of the method is its consistency with respect to arbitrary units implicitly adopted by different users to construct their quantitative ratings of products. It is argued that this property is needed for robust performance accuracy across a broad spectrum of RS application domains.
Unveiling the Hyper-Rayleigh Regime of the Fluctuating Two-Ray Fading Model
Venue: arXiv cs.IT 2019
Year: 2019

The recently proposed Fluctuating Two-Ray (FTR) model is gaining momentum as a reference fading model in scenarios where two dominant specular waves are present. Despite the numerous research works devoted to the performance analysis under FTR fading, little attention has been paid to effectively understanding the interplay between the fading model parameters and the fading severity. According to a new scale defined in this work, which measures the hyper-Rayleigh character of a fading channel in terms of the Amount of Fading, the outage probability and the average capacity, we see that the FTR fading model exhibits a full hyper-Rayleigh behavior. However, the Two-Wave with Diffuse Power fading model from which the former is derived has only strong hyper-Rayleigh behavior, which constitutes an interesting new insight. We also identify that the random fluctuations in the dominant specular waves are ultimately responsible for the full hyper-Rayleigh behavior of this class of fading channels.
Permutation Code Equivalence is not Harder than Graph Isomorphism when Hulls are Trivial
Venue: arXiv cs.DS 2019
Year: 2019

The paper deals with the problem of deciding if two finite-dimensional linear subspaces over an arbitrary field are identical up to a permutation of the coordinates. This problem is referred to as the permutation code equivalence. We show that given access to a subroutine that decides if two weighted undirected graphs are isomorphic, one may deterministically decide the permutation code equivalence provided that the underlying vector spaces intersect trivially with their orthogonal complement with respect to an arbitrary inner product. Such a class of vector spaces is usually called linear codes with trivial hulls. The reduction is efficient because it essentially boils down to computing the inverse of a square matrix of order the length of the involved codes. Experimental results obtained with randomly drawn binary codes having trivial hulls show that permutation code equivalence can be decided in a few minutes for lengths up to 50,000.
A Performance Evaluation Tool for Drone Communications in 4G Cellular Networks
Venue: arXiv cs.NI 2019
Year: 2019

We introduce a measurement tool for the performance evaluation of wireless communications with drones over cellular networks. The Android software records various LTE parameters, evaluates the TCP and UDP throughput, and tracks the GPS position. Example measurement results are presented.
ON-OFF Privacy with Correlated Requests
Venue: arXiv cs.IT 2019
Year: 2019

We introduce the ON-OFF privacy problem. At each time, the user is interested in the latest message of one of N online sources chosen at random, and his privacy status can be ON or OFF for each request. Only when privacy is ON the user wants to hide the source he is interested in. The problem is to design ON-OFF privacy schemes with maximum download rate that allow the user to obtain privately his requested messages. In many realistic scenarios, the user's requests are correlated since they depend on his personal attributes such as age, gender, political views, or geographical location. Hence, even when privacy is OFF, he cannot simply reveal his request since this will leak information about his requests when privacy was ON. We study the case when the users's requests can be modeled by a Markov chain and N=2 sources. In this case, we propose an ON-OFF privacy scheme and prove its optimality.
On the Convergence Rates of Learning-based Signature Generation Schemes to Contain Self-propagating Malware
Venue: arXiv cs.CR 2019
Year: 2019

In this paper, we investigate the importance of a defense system's learning rates to fight against the self-propagating class of malware such as worms and bots. To this end, we introduce a new propagation model based on the interactions between an adversary (and its agents) who wishes to construct a zombie army of a specific size, and a defender taking advantage of standard security tools and technologies such as honeypots (HPs) and intrusion detection and prevention systems (IDPSes) in the network environment. As time goes on, the defender can incrementally learn from the collected/observed attack samples (e.g., malware payloads), and therefore being able to generate attack signatures. The generated signatures then are used for filtering next attack traffic and thus containing the attacker's progress in its malware propagation mission. Using simulation and numerical analysis, we evaluate the efficacy of signature generation algorithms and in general any learning-based scheme in bringing an adversary's maneuvering in the environment to a halt as an adversarial containment strategy.
On a conditional inequality in Kolmogorov complexity and its applications in communication complexity
Venue: arXiv cs.CC 2019
Year: 2019

Romashchenko and Zimand~\cite{rom-zim:c:mutualinfo} have shown that if we partition the set of pairs $(x,y)$ of $n$-bit strings into combinatorial rectangles, then $I(x:y) \geq I(x:y \mid t(x,y)) - O(\log n)$, where $I$ denotes mutual information in the Kolmogorov complexity sense, and $t(x,y)$ is the rectangle containing $(x,y)$. We observe that this inequality can be extended to coverings with rectangles which may overlap. The new inequality essentially states that in case of a covering with combinatorial rectangles, $I(x:y) \geq I(x:y \mid t(x,y)) - \log ρ- O(\log n)$, where $t(x,y)$ is any rectangle containing $(x,y)$ and $ρ$ is the thickness of the covering, which is the maximum number of rectangles that overlap. We discuss applications to communication complexity of protocols that are nondeterministic, or randomized, or Arthur-Merlin, and also to the information complexity of interactive protocols.
Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks
Venue: arXiv cs.CV 2019
Year: 2019

The great performances of deep learning are undeniable, with impressive results over a wide range of tasks. However, the output confidence of these models is usually not well-calibrated, which can be an issue for applications where confidence on the decisions is central to providing trust and reliability (e.g., autonomous driving or medical diagnosis). For models using softmax at the last layer, Temperature Scaling (TS) is a state-of-the-art calibration method, with low time and memory complexity as well as demonstrated effectiveness. TS relies on a T parameter to rescale and calibrate values of the softmax layer, whose parameter value is computed from a labelled dataset. We are proposing an Unsupervised Temperature Scaling (UTS) approach, which does not depend on labelled samples to calibrate the model, which allows, for example, the use of a part of a test samples to calibrate the pre-trained model before going into inference mode. We provide theoretical justifications for UTS and assess its effectiveness on a wide range of deep models and datasets. We also demonstrate calibration results of UTS on skin lesion detection, a problem where a well-calibrated output can play an important role for accurate decision-making.
Dropping Pixels for Adversarial Robustness
Venue: arXiv cs.LG 2019
Year: 2019

Deep neural networks are vulnerable against adversarial examples. In this paper, we propose to train and test the networks with randomly subsampled images with high drop rates. We show that this approach significantly improves robustness against adversarial examples in all cases of bounded L0, L2 and L_inf perturbations, while reducing the standard accuracy by a small value. We argue that subsampling pixels can be thought to provide a set of robust features for the input image and, thus, improves robustness without performing adversarial training.
Emotional Contribution Analysis of Online Reviews
Venue: arXiv cs.IR 2019
Year: 2019

In response to the constant increase in population and tourism worldwide, there is a need for the development of cross-language market research tools that are more cost and time effective than surveys or interviews. Focusing on the Chinese tourism boom and the hotel industry in Japan, we extracted the most influential keywords in emotional judgement from Chinese online reviews of Japanese hotels in the portal site Ctrip. Using an entropy based mathematical model and a machine learning algorithm, we determined the words that most closely represent the demands and emotions of this customer base.
The Podium Mechanism: Improving on the Laplace and Staircase Mechanisms
Venue: arXiv cs.CR 2019
Year: 2019

The Podium mechanism guarantees ($ε, 0$)-differential privacy by sampling noise from a \emph{finite} mixture of three uniform distributions. By carefully constructing such a mixture distribution, we trivially guarantee privacy properties, while minimizing the variance of the noise added to our continuous outcome. Our gains in variance control are due to the "truncated" nature of the Podium mechanism where support for the noise distribution is maintained as close as possible to the sensitivity of our data collection, unlike the \emph{infinite} support that characterizes both the Laplace and Staircase mechanisms. In a high-privacy regime ($ε< 1$), the Podium mechanism outperforms the other two by 50-70\% in terms of the noise variance reduction, while in a low privacy regime ($ε\to \infty$), it asymptotically approaches the Staircase mechanism.
Bending models of lipid bilayer membranes: spontaneous curvature and area-difference elasticity
Venue: arXiv cs.CE 2019
Year: 2019

We preset a computational study of bending models for the curvature elasticity of lipid bilayer membranes that are relevant for simulations of vesicles and red blood cells. We compute bending energy and forces on triangulated meshes and evaluate and extend four well established schemes for their approximation: Kantor and Nelson 1987, Phys. Rev. A 36, 4020, Jülicher 1996, J. Phys. II France 6, 1797, Gompper and Kroll 1996, J. Phys. I France 6, 1305, and Meyer et. al. 2003 in Visualization and Mathematics III, Springer, p35, termed A, B, C, D. We present a comparative study of these four schemes on the minimal bending model and propose extensions for schemes B, C and D. These extensions incorporate the reference state and non-local energy to account for the spontaneous curvature, bilayer coupling, and area-difference elasticity models. Our results indicate that the proposed extensions enhance the models to account for shape transformation including budding/vesiculation as well as for non-axisymmetric shapes. We find that the extended scheme B is superior to the rest in terms of accuracy, and robustness as well as simplicity of implementation. We demonstrate the capabilities of this scheme on several benchmark problems including the budding-vesiculating process and the reproduction of the phase diagram of vesicles.
Profi-Load: An FPGA-Based Solution for Generating Network Load in Profinet Communication
Venue: arXiv cs.NI 2019
Year: 2019

Industrial automation has received a considerable attention in the last few years with the rise of Internet of Things (IoT). Specifically, industrial communication network technology such as Profinet has proved to be a major game changer for such automation. However, industrial automation devices often have to exhibit robustness to dynamically changing network conditions and thus, demand a rigorous testing environment to avoid any safety-critical failures. Hence, in this paper, we have proposed an FPGA-based novel framework called Profi-Load to generate Profinet traffic with specific intensities for a specified duration of time. The proposed Profi-Load intends to facilitate the performance testing of the industrial automated devices under various network conditions. By using the advantage of inherent hardware parallelism and re-configurable features of FPGA, Profi-Load is able to generate Profinet traffic efficiently. Moreover, it can be reconfigured on the fly as per the specific requirements. We have developed our proposed Profi-Load framework by employing the Xilinx-based NetJury device which belongs to Zynq-7000 FPGA family. A series of experiments have been conducted to evaluate the effectiveness of Profi-Load and it has been observed that Profi-Load is able to generate precise load at a constant rate for stringent timing requirements. Furthermore, a suitable Human Machine Interface (HMI) has also been developed for quick access to our framework. The HMI at the client side can directly communicate with the NetJury device and parameters such as, required load amount, number of packet(s) to be sent or desired time duration can be selected using the HMI.
Warshall's algorithm -- survey and applications
Venue: arXiv cs.DM 2019
Year: 2019

The survey presents the well-known Warshall's algorithm, a generalization and some interesting applications of this.
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
Venue: arXiv cs.LG 2019
Year: 2019

We introduce a novel loss function for training deep learning architectures to perform classification. It consists in minimizing the smoothness of label signals on similarity graphs built at the output of the architecture. Equivalently, it can be seen as maximizing the distances between the network function images of training inputs from distinct classes. As such, only distances between pairs of examples in distinct classes are taken into account in the process, and the training does not prevent inputs from the same class to be mapped to distant locations in the output domain. We show that this loss leads to similar performance in classification as architectures trained using the classical cross-entropy, while offering interesting degrees of freedom and properties. We also demonstrate the interest of the proposed loss to increase robustness of trained architectures to deviations of the inputs.