OpenRaiser/Intern-Atlas huggingface dataset:
Using fuzzy bits and neural networks to partially invert few rounds of some cryptographic hash functions
Venue: arXiv cs.CR 2019
Year: 2019

We consider fuzzy, or continuous, bits, which take values in [0;1] and (-1;1] instead of {0;1}, and operations on them (NOT, XOR etc.) and on their sequences (ADD), to obtain the generalization of cryptographic hash functions, CHFs, for the messages consisting of fuzzy bits, so that CHFs become smooth and non-constant functions of each bit of the message. We then train the neural networks to predict the message that has a given hash, where the loss function for the hash of predicted message and given true hash is backpropagatable. The results of the trainings for the standard CHFs - MD5, SHA1, SHA2-256, and SHA3/Keccak - with small number of (optionally weakened) rounds are presented and compared.
Deep Neural Networks Predicting Oil Movement in a Development Unit
Venue: arXiv cs.LG 2019
Year: 2019

We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it "Metamodel") is based on a projection of the system dynamics into a latent variable space, using Variational Autoencoder model, where Recurrent Neural Network predicts the dynamics. We show that being trained on multiple results of the conventional reservoir modelling, the Metamodel does not compromise the accuracy of the reservoir dynamics reconstruction in a significant way. It allows forecasting not only the flow rates from the wells, but also the dynamics of pressure and fluid saturations within the reservoir. The results open a new perspective in the optimization of oilfield development as the scenario screening could be accelerated sufficiently.
Cyber Security Awareness Campaigns: Why do they fail to change behaviour?
Venue: arXiv cs.CR 2019
Year: 2019

The present paper focuses on Cyber Security Awareness Campaigns, and aims to identify key factors regarding security which may lead them to failing to appropriately change people's behaviour. Past and current efforts to improve information-security practices and promote a sustainable society have not had the desired impact. It is important therefore to critically reflect on the challenges involved in improving information-security behaviours for citizens, consumers and employees. In particular, our work considers these challenges from a Psychology perspective, as we believe that understanding how people perceive risks is critical to creating effective awareness campaigns. Changing behaviour requires more than providing information about risks and reactive behaviours - firstly, people must be able to understand and apply the advice, and secondly, they must be motivated and willing to do so - and the latter requires changes to attitudes and intentions. These antecedents of behaviour change are identified in several psychological models of behaviour. We review the suitability of persuasion techniques, including the widely used 'fear appeals'. From this range of literature, we extract essential components for an awareness campaign as well as factors which can lead to a campaign's success or failure. Finally, we present examples of existing awareness campaigns in different cultures (the UK and Africa) and reflect on these.
Low-Cost Transfer Learning of Face Tasks
Venue: arXiv cs.CV 2019
Year: 2019

Do we know what the different filters of a face network represent? Can we use this filter information to train other tasks without transfer learning? For instance, can age, head pose, emotion and other face related tasks be learned from face recognition network without transfer learning? Understanding the role of these filters allows us to transfer knowledge across tasks and take advantage of large data sets in related tasks. Given a pretrained network, we can infer which tasks the network generalizes for and the best way to transfer the information to a new task.
Multi-layer Game-Theoretical Analysis of Wireless Markets with Market Segmentation
Venue: arXiv cs.NI 2019
Year: 2019

New larger and more diverse wireless markets have emerged. Modelling them can be challenging due to various business and network aspects. Existing models of wireless markets are either microscopic, focusing on a specific technical aspect (e.g., network topology) at a fine scale or macroscopic modelling wireless markets at a large-scale, e.g., considering homogeneous populations. In contrast to these approaches, this work develops a multi-layer game-theoretical framework, which allows providers to model users at multiple-level of detail by considering a different number of user sub-populations. It also models the mobility, traffic demand, and networks of providers. A population game using Logit dynamics models the user selection of the dataplan and provider, capturing the diversity in customer profile and relaxing the assumption about the user rationality. It analytically computes the equilibriums of users and providers and numerically evaluates the performance of the market as a function of the traffic demand, the number of available dataplans, and the knowledge about customer population. Significant benefits in revenue can be achieved by a provider when it integrates more detailed information about the user population. The number of disconnected users also decreases. Moreover the availability of several dataplans further enhances the gains. The stronger the provider, the more prominent the benefits. However the benefits diminish when all the providers model the customer population at the same degree of detail due to an increased competition. The analysis highlights the strategies of the providers depending on their capacity, level of knowledge about the customer population, and traffic conditions. It illustrates how a provider changes its strategy under different conditions, focusing potentially on different customer segments and also the pressure introduced by specific customer types.
The 2nd Workshop on Hacking and Making at Time-Bounded Events
Venue: arXiv cs.CY 2019
Year: 2019

In hackathons, small teams work together over a specified period of time to complete a project of interest. Such time-bounded hackathon-style events have become increasingly popular across different domains in recent years. Collegiate hackathons, just one of the many variants of hackathons, that are supported by the largest hackathon league (https://mlh.io/) alone attract over 65,000 participants among more than 200 events each year. Variously known as data dives, codefests, hack-days, sprints, edit-a-thons, mapathons, and so on, such events vary depending on different audiences and with divergent aims: for example, whether teams know each other beforehand, whether the event is structured as a competition with prizes, whether the event is open or requires membership or invitations, and whether the desired outcome is primarily a product innovation, learning a new skill, forming a community around a cause, solving a technical problem that requires intensive focus by a group, or just having fun. Taken together, hackathons offer new opportunities and challenges for collaboration by affording explicit, predictable, time-bounded spaces for collaborative work and engaging with new audiences. With the goal of discussing opportunities and challenges surrounding hackathons of different kinds, this one-day workshop brought together researchers, experienced event organizers, and practitioners to share and discuss their practical experiences. Empirical insights from studying these events may help position the CHI community to better study, plan and design hackathon-style events as socio-technical systems that support new modes of production and collaboration.
Improving In-Network Computing in IoT Through Degeneracy
Venue: arXiv cs.DC 2019
Year: 2019

We present a novel way of considering in-network computing (INC), using ideas from statistical physics. We define degeneracy for INC as the multiplicity of possible options available within the network to perform the same function with a given macroscopic property (e.g. delay). We present an efficient algorithm to determine all these alternatives. Our results show that by exploiting the set of possible degenerate alternatives, we can significantly improve the successful computation rate of a symmetric function, while still being able to satisfy requirements such as delay or energy consumption.
BS-assisted Task Offloading for D2D Networks with Presence of User Mobility
Venue: arXiv cs.DC 2019
Year: 2019

Task offloading is a key component in mobile edge computing. Offloading a task to a remote server takes communication and networking resources. An alternative is device-todevice (D2D) offloading, where a task of a device is offloaded to some device having computational resource available. The latter requires that the devices are within the range of each other, first for task collection, and later for result gathering. Hence, in mobility scenarios, the performance of D2D offloading will suffer if the contact rates between the devices are low. We enhance the setup to base station (BS) assisted D2D offloading, namely, a BS can act as a relay for task distribution or result collection. However, this would imply additional consumption of wireless resource. The associated cost and the improvement in completion time of task offloading compose a fundamental trade-off. For the resulting optimization problem, we mathematically prove the complexity, and propose an algorithm using Lagrangian duality. The simulation results demonstrate not only that the algorithm has close-to-optimal performance, but also provide structural insights of the optimal trade-off.
How Compact?: Assessing Compactness of Representations through Layer-Wise Pruning
Venue: arXiv cs.LG 2019
Year: 2019

Various forms of representations may arise in the many layers embedded in deep neural networks (DNNs). Of these, where can we find the most compact representation? We propose to use a pruning framework to answer this question: How compact can each layer be compressed, without losing performance? Most of the existing DNN compression methods do not consider the relative compressibility of the individual layers. They uniformly apply a single target sparsity to all layers or adapt layer sparsity using heuristics and additional training. We propose a principled method that automatically determines the sparsity of individual layers derived from the importance of each layer. To do this, we consider a metric to measure the importance of each layer based on the layer-wise capacity. Given the trained model and the total target sparsity, we first evaluate the importance of each layer from the model. From the evaluated importance, we compute the layer-wise sparsity of each layer. The proposed method can be applied to any DNN architecture and can be combined with any pruning method that takes the total target sparsity as a parameter. To validate the proposed method, we carried out an image classification task with two types of DNN architectures on two benchmark datasets and used three pruning methods for compression. In case of VGG-16 model with weight pruning on the ImageNet dataset, we achieved up to 75% (17.5% on average) better top-5 accuracy than the baseline under the same total target sparsity. Furthermore, we analyzed where the maximum compression can occur in the network. This kind of analysis can help us identify the most compact representation within a deep neural network.
Codes for Updating Linear Functions over Small Fields
Venue: arXiv cs.IT 2019
Year: 2019

We consider a point-to-point communication scenario where the receiver maintains a specific linear function of a message vector over a finite field. When the value of the message vector undergoes a sparse update, the transmitter broadcasts a coded version of the modified message while the receiver uses this codeword and the current value of the linear function to update its contents. It is assumed that the transmitter has access to the modified message but is unaware of the exact difference vector between the original and modified messages. Under the assumption that the difference vector is sparse and that its Hamming weight is at the most a known constant, the objective is to design a linear code with as small a codelength as possible that allows successful update of the linear function at the receiver. This problem is motivated by applications to distributed data storage systems. Recently, Prakash and Medard derived a lower bound on the codelength, which is independent of the size of the underlying finite field, and provided constructions that achieve this bound if the size of the finite field is sufficiently large. However, this requirement on the field size can be prohibitive for even moderate values of the system parameters. In this paper, we provide a field-size aware analysis of the function update problem, including a tighter lower bound on the codelength, and design codes that trade-off the codelength for a smaller field size requirement. We first characterize the family of function update problems where linear coding can provide reduction in codelength compared to a naive transmission scheme. We then provide field-size dependent bounds on the optimal codelength, and construct coding schemes when the receiver maintains linear functions of striped message vector. Finally, we show that every function update problem is equivalent to a generalized index coding problem.
Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset
Venue: arXiv cs.CV 2019
Year: 2019

Human Activity Recognition (HAR) is a key building block of many emerging applications such as intelligent mobility, sports analytics, ambient-assisted living and human-robot interaction. With robust HAR, systems will become more human-aware, leading towards much safer and empathetic autonomous systems. While human pose detection has made significant progress with the dawn of deep convolutional neural networks (CNNs), the state-of-the-art research has almost exclusively focused on a single sensing modality, especially video. However, in safety critical applications it is imperative to utilize multiple sensor modalities for robust operation. To exploit the benefits of state-of-the-art machine learning techniques for HAR, it is extremely important to have multimodal datasets. In this paper, we present a novel, multi-modal sensor dataset that encompasses nine indoor activities, performed by 16 participants, and captured by four types of sensors that are commonly used in indoor applications and autonomous vehicles. This multimodal dataset is the first of its kind to be made openly available and can be exploited for many applications that require HAR, including sports analytics, healthcare assistance and indoor intelligent mobility. We propose a novel data preprocessing algorithm to enable adaptive feature extraction from the dataset to be utilized by different machine learning algorithms. Through rigorous experimental evaluations, this paper reviews the performance of machine learning approaches to posture recognition, and analyses the robustness of the algorithms. When performing HAR with the RGB-Depth data from our new dataset, machine learning algorithms such as a deep neural network reached a mean accuracy of up to 96.8% for classification across all stationary and dynamic activities
Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection
Venue: arXiv cs.AI 2019
Year: 2019

Its constant technological evolution characterizes the contemporary world, and every day the processes, once manual, become computerized. Data are stored in the cyberspace, and as a consequence, one must increase the concern with the security of this environment. Cyber-attacks are represented by a growing worldwide scale and are characterized as one of the significant challenges of the century. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in cybernetic data attacks, focusing on the SQL Injection attack. The tests were performed with real bases of SQL Injection attacks on government computers, using fuzzy neural networks. According to the results obtained, the feasibility of constructing a system based on fuzzy rules, with the classification accuracy of cybernetic invasions within the margin of the standard deviation (compared to the state-of-the-art model in solving this type of problem) is real. The model helps countries prepare to protect their data networks and information systems, as well as create opportunities for expert systems to automate the identification of attacks in cyberspace.
Deep Learning for Human Affect Recognition: Insights and New Developments
Venue: arXiv cs.LG 2019
Year: 2019

Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications.
Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
Venue: arXiv cs.SI 2019
Year: 2019

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods. We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes (seed nodes) to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm.
A witness function based construction of discriminative models using Hermite polynomials
Venue: arXiv cs.LG 2019
Year: 2019

In machine learning, we are given a dataset of the form $\{(\mathbf{x}_j,y_j)\}_{j=1}^M$, drawn as i.i.d. samples from an unknown probability distribution $μ$; the marginal distribution for the $\mathbf{x}_j$'s being $μ^*$. We propose that rather than using a positive kernel such as the Gaussian for estimation of these measures, using a non-positive kernel that preserves a large number of moments of these measures yields an optimal approximation. We use multi-variate Hermite polynomials for this purpose, and prove optimal and local approximation results in a supremum norm in a probabilistic sense. Together with a permutation test developed with the same kernel, we prove that the kernel estimator serves as a `witness function' in classification problems. Thus, if the value of this estimator at a point $\mathbf{x}$ exceeds a certain threshold, then the point is reliably in a certain class. This approach can be used to modify pretrained algorithms, such as neural networks or nonlinear dimension reduction techniques, to identify in-class vs out-of-class regions for the purposes of generative models, classification uncertainty, or finding robust centroids. This fact is demonstrated in a number of real world data sets including MNIST, CIFAR10, Science News documents, and LaLonde data sets.
Minimizing The Age of Information: NOMA or OMA?
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we examine the potentials of Non- Orthogonal Multiple Access (NOMA), currently rivaling Orthogonal Multiple Access (OMA) in 3rd Generation Partnership Project (3GPP) standardization for future 5G networks Machine Type Communications (MTC), in the framework of minimizing the average Age of Information (AoI). By leveraging the notion of Stochastic Hybrid Systems (SHS), we find the total average AoI of the network in simple NOMA and conventional OMA environments. Armed with this, we provide a comparison between the two schemes in terms of average AoI. Interestingly, it will be shown that even when NOMA achieves better spectral efficiency in comparison to OMA, this does not necessarily translates into a lower average AoI in the network.
System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic
Venue: arXiv cs.NI 2019
Year: 2019

While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition period - the hybrid vehicular traffic - is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.