OpenRaiser/Intern-Atlas huggingface dataset:
MEx: Multi-modal Exercises Dataset for Human Activity Recognition
Venue: arXiv cs.CV 2019
Year: 2019

MEx: Multi-modal Exercises Dataset is a multi-sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD). We select 7 exercises regularly recommended for MSD patients by physiotherapists and collected data with four sensors a pressure mat, a depth camera and two accelerometers. The dataset contains three data modalities; numerical time-series data, video data and pressure sensor data posing interesting research challenges when reasoning for HAR and Exercise Quality Assessment. This paper presents our evaluation of the dataset on number of standard classification algorithms for the HAR task by comparing different feature representation algorithms for each sensor. These results set a reference performance for each individual sensor that expose their strengths and weaknesses for the future tasks. In addition we visualise pressure mat data to explore the potential of the sensor to capture exercise performance quality. With the recent advancement in multi-modal fusion, we also believe MEx is a suitable dataset to benchmark not only HAR algorithms, but also fusion algorithms of heterogeneous data types in multiple application domains.
Hyperparameter-Free Losses for Model-Based Monocular Reconstruction
Venue: arXiv cs.CV 2019
Year: 2019

This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the object and the camera pose are jointly optimized in a sole term expression. This simplification reduces the optimization time and its complexity. Moreover, we propose a novel implicit regularization technique based on random virtual projections that does not require additional 2D or 3D annotations. Our experiments suggest that minimizing a shape reprojection error together with the proposed implicit regularization is especially suitable for applications that require precise alignment between geometry and image spaces, such as augmented reality. We evaluate our losses on a large scale dataset with 3D ground truth and publish our implementations to facilitate reproducibility and public benchmarking in this field.
Towards Secure and Decentralized Sharing of IoT Data
Venue: arXiv cs.DC 2019
Year: 2019

The Internet of Things (IoT) bears unprecedented security and scalability challenges due to the magnitude of data produced and exchanged by IoT devices and platforms. Some of those challenges are currently being addressed by coupling IoT applications with blockchains. However, current blockchain-backed IoT systems simply use the blockchain to store access control policies, thereby underutilizing the power of blockchain technology. In this paper, we propose a new framework named Sash that couples IoT platforms with blockchain that provides a number of advantages compared to state of the art. In Sash, the blockchain is used to store access control policies and take access control decisions. Therefore, both changes to policies and access requests are correctly enforced and publicly auditable. Further, we devise a ``data marketplace'' by leveraging the ability of blockchains to handle financial transaction and providing ``by design'' remuneration to data producers. Finally, we exploit a special flavor of identity-based encryption to cater for cryptography-enforced access control while minimizing the overhead to distribute decryption keys. We prototype Sash by using the FIWARE open source IoT platform and the Hyperledger Fabric framework as the blockchain back-end. We also evaluate the performance of our prototype and show that it incurs tolerable overhead in realistic deployment settings.
Adaptive Group-based Zero Knowledge Proof-Authentication Protocol (AGZKP-AP) in Vehicular Ad Hoc Networks
Venue: arXiv cs.CR 2019
Year: 2019

Vehicular Ad Hoc Networks (VANETs) are a particular subclass of mobile ad hoc networks that raise a number of security challenges, notably from the way users authenticate the network. Authentication technologies based on existing security policies and access control rules in such networks assume full trust on Roadside Unit (RSU) and authentication servers. The disclosure of authentication parameters enables user's trace-ability over the network. VANETs' trusted entities (e.g. RSU) can utilize such information to track a user traveling behavior, violating user privacy and anonymity. In this paper, we proposed a novel, light-weight, Adaptive Group-based Zero Knowledge Proof-Authentication Protocol (AGZKP-AP) for VANETs. The proposed authentication protocol is capable of offering various levels of users' privacy settings based on the type of services available on such networks. Our scheme is based on the Zero-Knowledge-Proof (ZKP) crypto approach with the support of trade-off options. Users have the option to make critical decisions on the level of privacy and the amount of resources usage they prefer such as short system response time versus the number of private information disclosures. Furthermore, AGZKP-AP is incorporated with a distributed privilege control and revoking mechanism that render user's private information to law enforcement in case of a traffic violation.
Intellectual and social similarity among scholarly journals: an exploratory comparison of the networks of editors, authors and co-citations
Venue: arXiv cs.SI 2019
Year: 2019

This paper explores, by using suitable quantitative techniques, to what extent the intellectual proximity among scholarly journals is also a proximity in terms of social communities gathered around the journals. Three fields are considered: statistics, economics and information and library sciences. Co-citation networks (CC) represent the intellectual proximity among journals. The academic communities around the journals are represented by considering the networks of journals generated by authors writing in more than one journal (interlocking authorship: IA), and the networks generated by scholars sitting in the editorial board of more than one journal (interlocking editorship: IE). For comparing the whole structure of the networks, the dissimilarity matrices are considered. The CC, IE and IA networks appear to be correlated for the three fields. The strongest correlations is between CC and IA for the three fields. Lower and similar correlations are obtained for CC and IE, and for IE and IA. The CC, IE and IA networks are then partitioned in communities. Information and library sciences is the field where communities are more easily detectable, while the most difficult field is economics. The degrees of association among the detected communities show that they are not independent. For all the fields, the strongest association is between CC and IA networks; the minimum level of association is between IE and CC. Overall, these results indicate that the intellectual proximity is also a proximity among authors and among editors of the journals. Thus, the three maps of editorial power, intellectual proximity and authors communities tell similar stories.
Universal Policies to Learn Them All
Venue: arXiv cs.MA 2019
Year: 2019

We explore a collaborative and cooperative multi-agent reinforcement learning setting where a team of reinforcement learning agents attempt to solve a single cooperative task in a multi-scenario setting. We propose a novel multi-agent reinforcement learning algorithm inspired by universal value function approximators that not only generalizes over state space but also over a set of different scenarios. Additionally, to prove our claim, we are introducing a challenging 2D multi-agent urban security environment where the learning agents are trying to protect a person from nearby bystanders in a variety of scenarios. Our study shows that state-of-the-art multi-agent reinforcement learning algorithms fail to generalize a single task over multiple scenarios while our proposed solution works equally well as scenario-dependent policies.
Precise system-wide concatic malware unpacking
Venue: arXiv cs.CR 2019
Year: 2019

Run time packing is a common approach malware use to obfuscate their payloads, and automatic unpacking is, therefore, highly relevant. The problem has received much attention, and so far, solutions based on dynamic analysis have been the most successful. Nevertheless, existing solutions lack in several areas, both conceptually and architecturally, because they focus on a limited part of the unpacking problem. These limitations significantly impact their applicability, and current unpackers have, therefore, experienced limited adoption. In this paper, we introduce a new tool, called Minerva, for effective automatic unpacking of malware samples. Minerva introduces a unified approach to precisely uncover execution waves in a packed malware sample and produce PE files that are well-suited for follow-up static analysis. At the core, Minerva deploys a novel information flow model of system-wide dynamically generated code, precise collection of API calls and a new approach for merging execution waves and API calls. Together, these novelties amplify the generality and precision of automatic unpacking and make the output of Minerva highly usable. We extensively evaluate Minerva against synthetic and real-world malware samples and show that our techniques significantly improve on several aspects compared to previous work.
That's Not Me! Designing Fictitious Profiles to Answer Security Questions
Venue: arXiv cs.CR 2019
Year: 2019

Although security questions are still widely adopted, they still have several limitations. Previous research found that using system-generated information to answer security questions could be more secure than users' own answers. However, using system-generated information has usability limitations. To improve usability, previous research proposed the design of system-generated fictitious profiles. The information from these profiles would be used to answer security questions. However, no research has studied the elements that could influence the design of fictitious profiles or systems that use them to answer security questions. To address this research gap, we conducted an empirical investigation through 20 structured interviews. Our main findings revealed that to improve the design of fictitious profiles, users should be given the option to configure the profiles to make them relatable, interesting and memorable. We also found that the security questions currently provided by websites would need to be enhanced to cater for fictitious profiles.
RandNet: deep learning with compressed measurements of images
Venue: arXiv cs.LG 2019
Year: 2019

Principal component analysis, dictionary learning, and auto-encoders are all unsupervised methods for learning representations from a large amount of training data. In all these methods, the higher the dimensions of the input data, the longer it takes to learn. We introduce a class of neural networks, termed RandNet, for learning representations using compressed random measurements of data of interest, such as images. RandNet extends the convolutional recurrent sparse auto-encoder architecture to dense networks and, more importantly, to the case when the input data are compressed random measurements of the original data. Compressing the input data makes it possible to fit a larger number of batches in memory during training. Moreover, in the case of sparse measurements,training is more efficient computationally. We demonstrate that, in unsupervised settings, RandNet performs dictionary learning using compressed data. In supervised settings, we show that RandNet can classify MNIST images with minimal loss in accuracy, despite being trained with random projections of the images that result in a 50% reduction in size. Overall, our results provide a general principled framework for training neural networks using compressed data.
Extending TensorFlow's Semantics with Pipelined Execution
Venue: arXiv cs.DC 2019
Year: 2019

TensorFlow is a popular cloud computing framework that targets machine learning applications. It separates the specification of application logic (in a dataflow graph) from the execution of the logic. TensorFlow's native runtime executes the application with low overhead across a diverse set of hardware including CPUs, GPUs, and ASICs. Although the underlying dataflow engine supporting these features could be applied to computations beyond machine learning, certain design decisions limit this broader application, such as the inability for an application to differentiate between data items across concurrent requests. This paper introduces Pipelined TensorFlow (PTF), a system that extends TensorFlow's semantics to provide support for a broader variety of application logic. In particular, PTF supports applications that concurrently process finite batches of data on a single instantiation. PTF adds these semantics by partitioning the dataflow graph into a pipeline of smaller graphs and tagging each data item with metadata. These smaller graphs are separated by gates: new data structures in PTF that buffer data items between graphs and interpret the metadata to apply the new semantics. PTF's pipeline architecture executes on an unmodified TensorFlow runtime, maintaining compatibility with many existing TensorFlow library functions. Our evaluation shows that the pipelining mechanism of PTF can increase the throughput of a bioinformatics application by 4$\times$ while only increasing its latency by 0.13$\times$. This results in a sustained genome alignment and sorting rate of 321 megabases/second, using the compute and I/O resources of 20 computers.
OpenMP parallelization of multiple precision Taylor series method
Venue: arXiv cs.MS 2019
Year: 2019

OpenMP parallelization of multiple precision Taylor series method is proposed. A very good parallel performance scalability and parallel efficiency inside one computation node of a CPU-cluster is observed. We explain the details of the parallelization on the classical example of the Lorentz equations. The same approach can be applied straightforwardly to a large class of chaotic dynamical systems.
Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer
Venue: arXiv cs.CL 2019
Year: 2019

Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. In this work we introduce the Generative Style Transformer (GST) - a new approach to rewriting sentences to a target style in the absence of parallel style corpora. GST leverages the power of both, large unsupervised pre-trained language models as well as the Transformer. GST is a part of a larger `Delete Retrieve Generate' framework, in which we also propose a novel method of deleting style attributes from the source sentence by exploiting the inner workings of the Transformer. Our models outperform state-of-art systems across 5 datasets on sentiment, gender and political slant transfer. We also propose the use of the GLEU metric as an automatic metric of evaluation of style transfer, which we found to compare better with human ratings than the predominantly used BLEU score.
Deep Closed-Form Subspace Clustering
Venue: arXiv cs.CV 2019
Year: 2019

We propose Deep Closed-Form Subspace Clustering (DCFSC), a new embarrassingly simple model for subspace clustering with learning non-linear mapping. Compared with the previous deep subspace clustering (DSC) techniques, our DCFSC does not have any parameters at all for the self-expressive layer. Instead, DCFSC utilizes the implicit data-driven self-expressive layer derived from closed-form shallow auto-encoder. Moreover, DCFSC also has no complicated optimization scheme, unlike the other subspace clustering methods. With its extreme simplicity, DCFSC has significant memory-related benefits over the existing DSC method, especially on the large dataset. Several experiments showed that our DCFSC model had enough potential to be a new reference model for subspace clustering on large-scale high-dimensional dataset.
Protecting Actuators in Safety-Critical IoT Systems from Control Spoofing Attacks
Venue: arXiv cs.CR 2019
Year: 2019

In this paper, we propose a framework called Contego-TEE to secure Internet-of-Things (IoT) edge devices with timing requirements from control spoofing attacks where an adversary sends malicious control signals to the actuators. We use a trusted computing base available in commodity processors (such as ARM TrustZone) and propose an invariant checking mechanism to ensure the security and safety of the physical system. A working prototype of Contego-TEE was developed using embedded Linux kernel. We demonstrate the feasibility of our approach for a robotic vehicle running on an ARM-based platform.
High Performance Visual Object Tracking with Unified Convolutional Networks
Venue: arXiv cs.RO 2019
Year: 2019

Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks. Nonetheless, the chosen CNN features are always pre-trained in different tasks and individual components in tracking systems are learned separately, thus the achieved tracking performance may be suboptimal. Besides, most of these trackers are not designed towards real-time applications because of their time-consuming feature extraction and complex optimization details. In this paper, we propose an end-to-end framework to learn the convolutional features and perform the tracking process simultaneously, namely, a unified convolutional tracker (UCT). Specifically, the UCT treats feature extractor and tracking process both as convolution operation and trains them jointly, which enables learned CNN features are tightly coupled with tracking process. During online tracking, an efficient model updating method is proposed by introducing peak-versus-noise ratio (PNR) criterion, and scale changes are handled efficiently by incorporating a scale branch into network. Experiments are performed on four challenging tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016. Our method achieves leading performance on these benchmarks while maintaining beyond real-time speed.
Graph Embedding Based Hybrid Social Recommendation System
Venue: arXiv cs.SI 2019
Year: 2019

Item recommendation tasks are a widely studied topic. Recent developments in deep learning and spectral methods paved a path towards efficient graph embedding techniques. But little research has been done on applying these graph embedding to social graphs for recommendation tasks. This paper focuses at performance of various embedding methods applied on social graphs for the task of item recommendation. Additionally, a hybrid model is proposed wherein chosen embedding models are combined together to give a collective output. We put forward the hypothesis that such a hybrid model would perform better than individual embedding for recommendation task. With recommendation using individual embedding as a baseline, performance for hybrid model for the same task is evaluated and compared. Standard metrics are used for qualitative comparison. It is found that the proposed hybrid model outperforms the baseline.
Local Graph Stability in Exponential Family Random Graph Models
Venue: arXiv cs.DM 2019
Year: 2019

Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Such forces often lead to a complex pattern of dependence among edges, with non-trivial large-scale structures emerging from relatively simple local mechanisms. While this provides a powerful tool for probing macro-micro connections, much remains to be understood about how local forces shape global outcomes. One simple question of this type is that of the conditions needed for social forces to stabilize a particular structure. We refer to this property as local stability and seek a general means of identifying the set of parameters under which a target graph is locally stable with respect to a set of alternatives. Here, we provide a complete characterization of the region of the parameter space inducing local stability, showing it to be the interior of a convex cone whose faces can be derived from the change-scores of the sufficient statistics vis-a-vis the alternative structures. As we show, local stability is a necessary but not sufficient condition for more general notions of stability, the latter of which can be explored more efficiently by using the ``stable cone'' within the parameter space as a starting point. In addition, we show how local stability can be used to determine whether a fitted model implies that an observed structure would be expected to arise primarily from the action of social forces, versus by merit of the model permitting a large number of high probability structures, of which the observed structure is one. We also use our approach to identify the dyads within a given structure that are the least stable, and hence predicted to have the highest probability of changing over time.
Low-Congestion Shortcut and Graph Parameters
Venue: arXiv cs.DC 2019
Year: 2019

The concept of low-congestion shortcuts is initiated by Ghaffari and Haeupler [SODA2016] for addressing the design of CONGEST algorithms running fast in restricted network topologies. Specifically, given a specific graph class $X$, an $f$-round algorithm of constructing shortcuts of quality $q$ for any instance in $X$ results in $\tilde{O}(q + f)$-round algorithms of solving several fundamental graph problems such as minimum spanning tree and minimum cut, for $X$. In this paper, we consider the relationship between the quality of low-congestion shortcuts and three major graph parameters, chordality, diameter, and clique-width. The main contribution of the paper is threefold: (1) We show an $O(1)$-round algorithm which constructs a low-congestion shortcut with quality $O(kD)$ for any $k$-chordal graph, and prove that the quality and running time of this construction is nearly optimal up to polylogarithmic factors. (2) We present two algorithms, each of which constructs a low-congestion shortcut with quality $\tilde{O}(n^{1/4})$ in $\tilde{O}(n^{1/4})$ rounds for graphs of $D=3$, and that with quality $\tilde{O}(n^{1/3})$ in $\tilde{O}(n^{1/3})$ rounds for graphs of $D=4$ respectively. These results obviously deduce two MST algorithms running in $\tilde{O}(n^{1/4})$ and $\tilde{O}(n^{1/3})$ rounds for $D=3$ and $4$ respectively, which almost close the long-standing complexity gap of the MST construction in small-diameter graphs originally posed by Lotker et al. [Distributed Computing 2006]. (3) We show that bounding clique-width does not help the construction of good shortcuts by presenting a network topology of clique-width six where the construction of MST is as expensive as the general case.