OpenRaiser/Intern-Atlas huggingface dataset:
Genealogy tree: understanding academic lineage of authors via algorithmic and visual analysis
Venue: arXiv cs.DL 2018
Year: 2018

Ancestry and genealogy tree are proven tools to determine the lineage of any person and establish dependencies among individuals. Genealogy tree can be exploited further to gain information about the researcher and his scholastic lineage which is of paramount importance in today's world of computer technology. this insight into academic genealogy could be a way of helping PHD students achieve academic socialization within the discipline, by making explicit connections that may be influential. Awareness of his scientific heritage gives the user a broader perspective of his own research project. This paper also highlights and investigates how this academic network is exploited by certain researchers using various visualization tools. It was observed during this work that the credibility and influence factor is determined by the various citations obtained by an author and to improve their rankings in various forms, they tend to collaborate in their academic circle and boost their citation count. A recent trend among researchers is to form communities based on their academic relationships and rely on copious citations for their mutual benefit. Tracing the genealogical relationships can be helpful in detecting such communities and also create a more quality aware metric using a lineage independent model for computation of author level metrics.
Towards Quantifying Sampling Bias in Network Inference
Venue: arXiv cs.SI 2018
Year: 2018

Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labelled sample? How should the network be sampled, and what effect does it have on inference error? How does the structure of the network impact the sampling strategy? We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labelled, regardless of the sampling strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. These findings suggest that the impact of network structure on relational classification is more complex than previously thought.
Optimal Placement of Relay Nodes in Wireless Sensor Network Using Artificial Bee Colony Algorithm
Venue: arXiv cs.NI 2018
Year: 2018

Deploying sensor nodes randomly most of the time generates initial communication hole even in highly dense networks. These communication holes cannot be totally eliminated even when the deployment is done in a structured manner. In either case, the resulting inter-node distances may degrade the performance of the network. This paper proposes an enhanced deployment algorithm based on Artificial Bees Colony (ABC). The ABC-based deployment is guaranteed to extend the lifetime by optimizing the network parameters and constraining the total number of deployed relays. Simulations validate the effectiveness of the proposed strategy under different cases of problem complexity. Results show that the proposed approach improves the network lifetime considerably when compared to solutions reported in the literature such as Shortest Path 3-D grid Deployment (SP3D) algorithm. Keywords: Artificial Bee Colony, Wiener index, optimization, relay nodes, Laplacian matrix, connected graph, vertex, edge, average distance, Laplacian matrix, Shortest Path 3-D grid Deployment, ABC, SP3D, RNs/CHs, ILDCC, SPRN, O3DwLC, algorithm, approach, single objective, multi objective, eigenvalue, First Phase Relay Nodes, NP-Hard, Deployment, proposed, second phase relay nodes, Ideal Media Access Control, cluster head, Minimum Spanning Tree, non-deterministic polynomial-time hard, two-layer hierarchical structure, Optimized 3-D deployment with Lifetime Constraints, flux, Wireless Sensor Network, size, Lifetime, Network load, number of nodes using packets, Connectivity, two layered, protocols, collision and interference.
Categorical Mixture Models on VGGNet activations
Venue: arXiv cs.CV 2018
Year: 2018

In this project, I use unsupervised learning techniques in order to cluster a set of yelp restaurant photos under meaningful topics. In order to do this, I extract layer activations from a pre-trained implementation of the popular VGGNet convolutional neural network. First, I explore using LDA with the activations of convolutional layers as features. Secondly, I explore using the object-recognition powers of VGGNet trained on ImageNet in order to extract meaningful objects from the photos, and then perform LDA to group the photos under topic-archetypes. I find that this second approach finds meaningful archetypes, which match the human intuition for photo topics such as restaurant, food, and drinks. Furthermore, these clusters align well and distinctly with the actual yelp photo labels.
Decision-making processes in the Cognitive Theory of True Conditions
Venue: arXiv cs.AI 2018
Year: 2018

The Cognitive Theory of True Conditions (CTTC) is a proposal to design the implementation of cognitive abilities and to describe the model-theoretic semantics of symbolic cognitive architectures. The CTTC is formulated mathematically using the multi-optional many-sorted past present future(MMPPF) structures. This article discussed how decision-making processes are described in the CTTC.
Scaling Structured Multigrid to 500K+ Cores through Coarse-Grid Redistribution
Venue: arXiv cs.MS 2018
Year: 2018

The efficient solution of sparse, linear systems resulting from the discretization of partial differential equations is crucial to the performance of many physics-based simulations. The algorithmic optimality of multilevel approaches for common discretizations makes them a good candidate for an efficient parallel solver. Yet, modern architectures for high-performance computing systems continue to challenge the parallel scalability of multilevel solvers. While algebraic multigrid methods are robust for solving a variety of problems, the increasing importance of data locality and cost of data movement in modern architectures motivates the need to carefully exploit structure in the problem. Robust logically structured variational multigrid methods, such as Black Box Multigrid (BoxMG), maintain structure throughout the multigrid hierarchy. This avoids indirection and increased coarse-grid communication costs typical in parallel algebraic multigrid. Nevertheless, the parallel scalability of structured multigrid is challenged by coarse-grid problems where the overhead in communication dominates computation. In this paper, an algorithm is introduced for redistributing coarse-grid problems through incremental agglomeration. Guided by a predictive performance model, this algorithm provides robust redistribution decisions for structured multilevel solvers. A two-dimensional diffusion problem is used to demonstrate the significant gain in performance of this algorithm over the previous approach that used agglomeration to one processor. In addition, the parallel scalability of this approach is demonstrated on two large-scale computing systems, with solves on up to 500K+ cores.
Towards a Data-driven IoT Software Architecture for Smart City Utilities
Venue: arXiv cs.DC 2018
Year: 2018

The Internet of Things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart Cities are practical manifestation of IoT, with the goal of efficient, reliable and safe delivery of city utilities like water, power and transport to residents, through their intelligent management. A data-driven IoT Software Platform is essential for realizing manageable and sustainable Smart Utilities, and for novel applications to be developed upon them. Here, we propose such a service-oriented software architecture to address two key operational activities in a Smart Utility -- the IoT fabric for resource management, and the data and application platform for decision making. Our design uses open web standards and evolving network protocols, Cloud and edge resources, and streaming Big Data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus-scale IoT testbed at the Indian Institute of Science (IISc), Bangalore, and present our experiences. Our architecture is scalable to a township or city, while also generalizable to other Smart Utility domains. Our experiences serves as a template for other similar efforts, particularly in emerging markets, and highlights the gaps and opportunities for a data-driven IoT Software architecture for smart cities.
MDS matrices over small fields: A proof of the GM-MDS conjecture
Venue: arXiv cs.DM 2018
Year: 2018

An MDS matrix is a matrix whose minors all have full rank. A question arising in coding theory is what zero patterns can MDS matrices have. There is a natural combinatorial characterization (called the MDS condition) which is necessary over any field, as well as sufficient over very large fields by a probabilistic argument. Dau et al. (ISIT 2014) conjectured that the MDS condition is sufficient over small fields as well, where the construction of the matrix is algebraic instead of probabilistic. This is known as the GM-MDS conjecture. Concretely, if a $k \times n$ zero pattern satisfies the MDS condition, then they conjecture that there exists an MDS matrix with this zero pattern over any field of size $|\mathbb{F}| \ge n+k-1$. In recent years, this conjecture was proven in several special cases. In this work, we resolve the conjecture.
Improving Aviation Safety using Synthetic Vision System integrated with Eye-tracking Devices
Venue: arXiv cs.HC 2018
Year: 2018

By collecting the data of eyeball movement of pilots, it is possible to monitor pilot's operation in the future flight in order to detect potential accidents. In this paper, we designed a novel SVS system that is integrated with an eye tracking device, and is able to achieve the following functions:1) A novel method that is able to learn from the eyeball movements of pilots and preload or render the terrain data in various resolutions, in order to improve the quality of terrain display by comprehending the interested regions of the pilot. 2) A warning mechanism that may detect the risky operation via analyzing the aviation information from the SVS and the eyeball movement from the eye tracking device, in order to prevent the maloperations or human factor accidents. The user study and experiments show that the proposed SVS-Eyetracking system works efficiently and is capable of avoiding potential risked caused by fatigue in the flight simulation.
Quasiperiods of biinfinite Sturmian words
Venue: arXiv cs.DM 2018
Year: 2018

We study the notion of quasiperiodicity, in the sense of "coverability", for biinfinite words. All previous work about quasiperiodicity focused on right infinite words, but the passage to the biinfinite case could help to prove stronger results about quasiperiods of Sturmian words. We demonstrate this by showing that all biinfinite Sturmian words have infinitely many quasiperiods, which is not quite (but almost) true in the right infinite case, and giving a characterization of those quasiperiods. The main difference between right infinite and the biinfinite words is that, in the latter case, we might have several quasiperiods of the same length. This is not possible with right infinite words because a quasiperiod has to be a prefix of the word. We study in depth the relations between quasiperiods of the same length in a given biinfinite quasiperiodic word. This study gives enough information to allow to determine the set of quasiperiods of an arbitrary word.
A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture
Venue: arXiv cs.LG 2018
Year: 2018

Optical motion capture systems have become a widely used technology in various fields, such as augmented reality, robotics, movie production, etc. Such systems use a large number of cameras to triangulate the position of optical markers.The marker positions are estimated with high accuracy. However, especially when tracking articulated bodies, a fraction of the markers in each timestep is missing from the reconstruction. In this paper, we propose to use a neural network approach to learn how human motion is temporally and spatially correlated, and reconstruct missing markers positions through this model. We experiment with two different models, one LSTM-based and one time-window-based. Both methods produce state-of-the-art results, while working online, as opposed to most of the alternative methods, which require the complete sequence to be known. The implementation is publicly available at https://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction .
The joint optimization of critical interdependent infrastructure of an electricity-water-gas system
Venue: arXiv cs.SY 2018
Year: 2018

Electricity, water, and gas systems are critical infrastructures that are sustaining our daily lives. This paper studies the joint operation of these systems through a proposed optimization model and explores the advantage of considering the system of systems. Individual and joint optimizations are studied and compared. The numerical results show that the total electricity cost for these three systems can be reduced by 9% via joint optimization. Because the water system and gas system intrinsically include the storages in their systems, the power system can use these storages as the regulation capacity to shift load from peak hours to off-peak hours. Since the saving on the power generation cost surpasses the incremental cost in the operation and maintenance (O&M), the overall economic performance is improved by the joint optimization.
Co-processor-based Behavior Monitoring: Application to the Detection of Attacks Against the System Management Mode
Venue: arXiv cs.CR 2018
Year: 2018

Highly privileged software, such as firmware, is an attractive target for attackers. Thus, BIOS vendors use cryptographic signatures to ensure firmware integrity at boot time. Nevertheless, such protection does not prevent an attacker from exploiting vulnerabilities at runtime. To detect such attacks, we propose an event-based behavior monitoring approach that relies on an isolated co-processor. We instrument the code executed on the main CPU to send information about its behavior to the monitor. This information helps to resolve the semantic gap issue. Our approach does not depend on a specific model of the behavior nor on a specific target. We apply this approach to detect attacks targeting the System Management Mode (SMM), a highly privileged x86 execution mode executing firmware code at runtime. We model the behavior of SMM using invariants of its control-flow and relevant CPU registers (CR3 and SMBASE). We instrument two open-source firmware implementations: EDK II and coreboot. We evaluate the ability of our approach to detect state-of-the-art attacks and its runtime execution overhead by simulating an x86 system coupled with an ARM Cortex A5 co-processor. The results show that our solution detects intrusions from the state of the art, without any false positives, while remaining acceptable in terms of performance overhead in the context of the SMM (i.e., less than the 150 $μ$s threshold defined by Intel).
Genetic Algorithm Assisted Hybrid Beamforming for Wireless Fronthaul
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes a genetic algorithm assisted hybrid signal to leakage plus noise ratio (SLNR) beamforming design for wireless fronthaul scenario. The digital precoder of the proposed hybrid SLNR beamforming is expressed in closed-form. Highly limited phase resolution (one-bit resolution) is assumed at the phase shifters at the analog precoder. The analog precoders maximizing the approximated sum rate are presented. Genetic algorithms are used to search for optimal solutions of one-bit analog precoders. In contrast to common assumptions on perfect knowledge of the true channel matrix at the transmitter, the proposed method relies only on the distorted channel matrix after the analog precoder. Performance of the proposed hybrid SLNR beamforming with limited phase resolution at the analog precoder can achieve performance close to digital beamforming in single cell wireless fronthaul scenarios. It is also shown that hybrid beamforming can result in undesired beams causing intercell interference in multicell wireless fronthaul scenarios.
Interference Management via Space and Frequency Domain Resource Partitioning
Venue: arXiv cs.IT 2018
Year: 2018

One key requirement for the fifth-Generation (5G) mobile system is the enhancement of cell edge user performance to ensure that every user is supported with consistent experience anywhere in the network. An active interference design to improve anywhere performance, particularly in the low SINR regime, can be achieved by applying a recently proposed new type of modulation scheme, frequency quadrature-amplitude modulation (FQAM), which could change the distribution of interference and therefore improve channel capacity. In this paper, a resource partitioning scheme to support FQAM in interference intensive scenarios is proposed. The proposed scheme slices radio resources into orthogonal parts for QAM and FQAM, respectively, along two different resource dimensions, namely, space and frequency. This can be achieved by incorporating advanced beamforming algorithms, e.g., full-dimension (FD-MIMO), and performing a frequency-based split of FQAM resources to effectively improve the data rate of the edge users experiencing heavy interference. Two resource partitioning mechanisms are proposed followed by complexity analysis. Evaluation results on both space and frequency-based FQAM interference management are presented, showing significant performance improvements when compared to the regular QAM modulation.
On the Verification of Logically Decorated Graph Transformations
Venue: arXiv cs.LO 2018
Year: 2018

We address the problem of reasoning on graph transformations featuring actions such as \emph{addition} and \emph{deletion} of nodes and edges, node \emph{merging} and \emph{cloning}, node or edge \emph{labelling} and edge \emph{redirection}. First, we introduce the considered graph rewrite systems which are parameterized by a given logic $\mathcal{L}$. Formulas of $\mathcal{L}$ are used to label graph nodes and edges. In a second step, we tackle the problem of formal verification of the considered rewrite systems by using a Hoare-like weakest precondition calculus. It acts on triples of the form $\{\texttt{Pre}\}(\texttt{R},\texttt{strategy}) \{\texttt{Post}\}$ where \texttt{Pre} and \texttt{Post} are conditions specified in the given logic $\mathcal{L}$, \texttt{R} is a graph rewrite system and \texttt{strategy} is an expression stating how rules in \texttt{R} are to be performed. We prove that the calculus we introduce is sound. Moreover, we show how the proposed framework can be instantiated successfully with different logics. We investigate first-order logic and several of its decidable fragments with a particular focus on different dialects of description logic (DL). We also show, by using bisimulation relations, that some DL fragments cannot be used due to their lack of expressive power.
Facebook (A)Live? Are live social broadcasts really broadcasts?
Venue: arXiv cs.SI 2018
Year: 2018

The era of live-broadcast is back but with two major changes. First, unlike traditional TV broadcasts, content is now streamed over the Internet enabling it to reach a wider audience. Second, due to various user-generated content platforms it has become possible for anyone to get involved, streaming their own content to the world. This emerging trend of going live usually happens via social platforms, where users perform live social broadcasts predominantly from their mobile devices, allowing their friends (and the general public) to engage with the stream in real-time. With the growing popularity of such platforms, the burden on the current Internet infrastructure is therefore expected to multiply. With this in mind, we explore one such prominent platform - Facebook Live. We gather 3TB of data, representing one month of global activity and explore the characteristics of live social broadcast. From this, we derive simple yet effective principles which can decrease the network burden. We then dissect global and hyper-local properties of the video while on-air, by capturing the geography of the broadcasters or the users who produce the video and the viewers or the users who interact with it. Finally, we study the social engagement while the video is live and distinguish the key aspects when the same video goes on-demand. A common theme throughout the paper is that, despite its name, many attributes of Facebook Live deviate from both the concepts of live and broadcast.
Path Planning and Navigation Inside Off-World Lava Tubes and Caves
Venue: arXiv cs.RO 2018
Year: 2018

Detailed surface images of the Moon and Mars reveal hundreds of cave-like openings. These cave-like openings are theorized to be remnants of lava-tubes and their interior maybe in pristine conditions. These locations may have well preserved geological records of the Moon and Mars, including evidence of past water flow and habitability. Exploration of these caves using wheeled rovers remains a daunting challenge. These caves are likely to have entrances with caved-in ceilings much like the lava-tubes of Arizona and New Mexico. Thus, the entrances are nearly impossible to traverse even for experienced human hikers. Our approach is to utilize the SphereX robot, a 3 kg, 30 cm diameter robot with computer hardware and sensors of a smartphone attached to rocket thrusters. Each SphereX robot can hop, roll or fly short distances in low gravity, airless or low-pressure environments. Several SphereX robots maybe deployed to minimize single-point failure and exploit cooperative behaviors to traverse the cave. There are some important challenges for navigation and path planning in these cave environments. Localization systems such as GPS are not available nor are they easy to install due to the signal blockage from the rocks. These caves are too dark and too large for conventional sensor such as cameras and miniature laser sensors to perform detailed mapping and navigation. In this paper, we identify new techniques to map these caves by performing localized, cooperative mapping and navigation.