OpenRaiser/Intern-Atlas huggingface dataset:
Estimating Kullback-Leibler Divergence Using Kernel Machines
Venue: arXiv cs.LG 2019
Year: 2019

Recently, a method called the Mutual Information Neural Estimator (MINE) that uses neural networks has been proposed to estimate mutual information and more generally the Kullback-Leibler (KL) divergence between two distributions. The method uses the Donsker-Varadhan representation to arrive at the estimate of the KL divergence and is better than the existing estimators in terms of scalability and flexibility. The output of MINE algorithm is not guaranteed to be a consistent estimator. We propose a new estimator that instead of searching among functions characterized by neural networks searches the functions in a Reproducing Kernel Hilbert Space. We prove that the proposed estimator is consistent. We carry out simulations and show that when the datasets are small the proposed estimator is more reliable than the MINE estimator and when the datasets are large the performance of the two methods are close.
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling
Venue: arXiv cs.RO 2019
Year: 2019

Coordination recognition and subtle pattern prediction of future trajectories play a significant role when modeling interactive behaviors of multiple agents. Due to the essential property of uncertainty in the future evolution, deterministic predictors are not sufficiently safe and robust. In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task. We illustrate two types of representation of the coordination variable: categorized and real-valued, and compare their effects and advantages based on empirical studies. We also bring the ideas of Bayesian deep learning into deep generative models to generate diversified prediction hypotheses. The proposed system is tested on multiple driving datasets in various traffic scenarios, which achieves better performance than baseline approaches in terms of a set of evaluation metrics. The results also show that using categorized coordination can better capture multi-modality and generate more diversified samples than the real-valued coordination, while the latter can generate prediction hypotheses with smaller errors with a sacrifice of sample diversity. Moreover, employing neural networks with weight uncertainty is able to generate samples with larger variance and diversity.
Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture
Venue: arXiv cs.LG 2019
Year: 2019

Using raw sensor data to model and train networks for Human Activity Recognition can be used in many different applications, from fitness tracking to safety monitoring applications. These models can be easily extended to be trained with different data sources for increased accuracies or an extension of classifications for different prediction classes. This paper goes into the discussion on the available dataset provided by WISDM and the unique features of each class for the different axes. Furthermore, the design of a Long Short Term Memory (LSTM) architecture model is outlined for the application of human activity recognition. An accuracy of above 94% and a loss of less than 30% has been reached in the first 500 epochs of training.
A knowledge-based intelligent system for control of dirt recognition process in the smart washing machines
Venue: arXiv cs.AI 2019
Year: 2019

In this paper, we propose an intelligence approach based on fuzzy logic to modeling human intelligence in washing clothes. At first, an intelligent feedback loop is designed for perception-based sensing of dirt inspired by human color understanding. Then, when color stains leak out of some colored clothes the human probabilistic decision making is computationally modeled to detect this stain leakage and thus the problem of recognizing dirt from stain can be considered in the washing process. Finally, we discuss the fuzzy control of washing clothes and design and simulate a smart controller based on the fuzzy intelligence feedback loop.
Alternative Techniques for Mapping Paths to HLAI
Venue: arXiv cs.AI 2019
Year: 2019

The only systematic mapping of the HLAI technical landscape was conducted at a workshop in 2009 [Adams et al., 2012]. However, the results from it were not what organizers had hoped for [Goertzel 2014, 2016], merely just a series of milestones, up to 50% of which could be argued to have been completed already. We consider two more recent articles outlining paths to human-like intelligence [Mikolov et al., 2016; Lake et al., 2017]. These offer technical and more refined assessments of the requirements for HLAI rather than just milestones. While useful, they also have limitations. To address these limitations we propose the use of alternative techniques for an updated systematic mapping of the paths to HLAI. The newly proposed alternative techniques can model complex paths of future technologies using intricate directed graphs. Specifically, there are two classes of alternative techniques that we consider: scenario mapping methods and techniques for eliciting expert opinion through digital platforms and crowdsourcing. We assess the viability and utility of both the previous and alternative techniques, finding that the proposed alternative techniques could be very beneficial in advancing the existing body of knowledge on the plausible frameworks for creating HLAI. In conclusion, we encourage discussion and debate to initiate efforts to use these proposed techniques for mapping paths to HLAI.
Inside Job: Diagnosing Bluetooth Lower Layers Using Off-the-Shelf Devices
Venue: arXiv cs.NI 2019
Year: 2019

Bluetooth is among the dominant standards for wireless short-range communication with multi-billion Bluetooth devices shipped each year. Basic Bluetooth analysis inside consumer hardware such as smartphones can be accomplished observing the Host Controller Interface (HCI) between the operating system's driver and the Bluetooth chip. However, the HCI does not provide insights to tasks running inside a Bluetooth chip or Link Layer (LL) packets exchanged over the air. As of today, consumer hardware internal behavior can only be observed with external, and often expensive tools, that need to be present during initial device pairing. In this paper, we leverage standard smartphones for on-device Bluetooth analysis and reverse engineer a diagnostic protocol that resides inside Broadcom chips. Diagnostic features include sniffing lower layers such as LL for Classic Bluetooth and Bluetooth Low Energy (BLE), transmission and reception statistics, test mode, and memory peek and poke.
HeTM: Transactional Memory for Heterogeneous Systems
Venue: arXiv cs.DC 2019
Year: 2019

Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak performance and energy efficiency levels. Unfortunately, though, developing applications that can take full advantage of the potential of heterogeneous systems is a notoriously hard task. This work takes a step towards reducing the complexity of programming heterogeneous systems by introducing the abstraction of Heterogeneous Transactional Memory (HeTM). HeTM provides programmers with the illusion of a single memory region, shared among the CPUs and the (discrete) GPU(s) of a heterogeneous system, with support for atomic transactions. Besides introducing the abstract semantics and programming model of HeTM, we present the design and evaluation of a concrete implementation of the proposed abstraction, which we named Speculative HeTM (SHeTM). SHeTM makes use of a novel design that leverages on speculative techniques and aims at hiding the inherently large communication latency between CPUs and discrete GPUs and at minimizing inter-device synchronization overhead. SHeTM is based on a modular and extensible design that allows for easily integrating alternative TM implementations on the CPU's and GPU's sides, which allows the flexibility to adopt, on either side, the TM implementation (e.g., in hardware or software) that best fits the applications' workload and the architectural characteristics of the processing unit. We demonstrate the efficiency of the SHeTM via an extensive quantitative study based both on synthetic benchmarks and on a porting of a popular object caching system.
DS-VIO: Robust and Efficient Stereo Visual Inertial Odometry based on Dual Stage EKF
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a dual stage EKF (Extended Kalman Filter)-based algorithm for the real-time and robust stereo VIO (visual inertial odometry). The first stage of this EKF-based algorithm performs the fusion of accelerometer and gyroscope while the second performs the fusion of stereo camera and IMU. Due to the sufficient complementary characteristics between accelerometer and gyroscope as well as stereo camera and IMU, the dual stage EKF-based algorithm can achieve a high precision of odometry estimations. At the same time, because of the low dimension of state vector in this algorithm, its computational efficiency is comparable to previous filter-based approaches. We call our approach DS-VIO (dual stage EKFbased stereo visual inertial odometry) and evaluate our DSVIO algorithm by comparing it with the state-of-art approaches including OKVIS, ROVIO, VINS-MONO and S-MSCKF on the EuRoC dataset. Results show that our algorithm can achieve comparable or even better performances in terms of the RMS error
Threshold-based Selective Cooperative-NOMA
Venue: arXiv cs.IT 2019
Year: 2019

In this letter, we propose threshold-based selective cooperative-NOMA (TBS-C-NOMA) to increase the data reliability of conventional cooperative-NOMA (C-NOMA) networks. In TBS-C-NOMA, the intra-cell user forwards the symbols of cell-edge user after successive interference canceler (SIC) only if the signal-to-interference plus noise ratio (SINR) is greater than the pre-determined threshold value. Hence, the data reliability of the cell-edge user is increased by eliminating the effect of the error propagation. We derive closed-form end-to-end exact bit error probability (BEP) of proposed system for various modulation constellations. Then, the optimum threshold value is analyzed in order to minimize BEP. The obtained expressions are validated via simulations and it is revealed that TBS-C-NOMA outperforms C-NOMA and full diversity order is achieved.
Conditioning of restricted Fourier matrices and super-resolution of MUSIC
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies stable recovery of a collection of point sources from its noisy $M+1$ low-frequency Fourier coefficients. We focus on the super-resolution regime where the minimum separation of the point sources is below $1/M$. We propose a separated clumps model where point sources are clustered in far apart sets, and prove an accurate lower bound of the Fourier matrix with nodes restricted to the source locations. This estimate gives rise to a theoretical analysis on the super-resolution limit of the MUSIC algorithm.
Bug Searching in Smart Contract
Venue: arXiv cs.SE 2019
Year: 2019

With the frantic development of smart contracts on the Ethereum platform, its market value has also climbed. In 2016, people were shocked by the loss of nearly $50 million in cryptocurrencies from the DAO reentrancy attack. Due to the tremendous amount of money flowing in smart contracts, its security has attracted much attention of researchers. In this paper, we investigated several common smart contract vulnerabilities and analyzed their possible scenarios and how they may be exploited. Furthermore, we survey the smart contract vulnerability detection tools for the Ethereum platform in recent years. We found that these tools have similar prototypes in software vulnerability detection technology. Moreover, for the features of public distribution systems such as Ethereum, we present the new challenges that these software vulnerability detection technologies face.
Log Diameter Rounds Algorithms for $2$-Vertex and $2$-Edge Connectivity
Venue: arXiv cs.DS 2019
Year: 2019

Many modern parallel systems, such as MapReduce, Hadoop and Spark, can be modeled well by the MPC model. The MPC model captures well coarse-grained computation on large data --- data is distributed to processors, each of which has a sublinear (in the input data) amount of memory and we alternate between rounds of computation and rounds of communication, where each machine can communicate an amount of data as large as the size of its memory. This model is stronger than the classical PRAM model, and it is an intriguing question to design algorithms whose running time is smaller than in the PRAM model. In this paper, we study two fundamental problems, $2$-edge connectivity and $2$-vertex connectivity (biconnectivity). PRAM algorithms which run in $O(\log n)$ time have been known for many years. We give algorithms using roughly log diameter rounds in the MPC model. Our main results are, for an $n$-vertex, $m$-edge graph of diameter $D$ and bi-diameter $D'$, 1) a $O(\log D\log\log_{m/n} n)$ parallel time $2$-edge connectivity algorithm, 2) a $O(\log D\log^2\log_{m/n}n+\log D'\log\log_{m/n}n)$ parallel time biconnectivity algorithm, where the bi-diameter $D'$ is the largest cycle length over all the vertex pairs in the same biconnected component. Our results are fully scalable, meaning that the memory per processor can be $O(n^δ)$ for arbitrary constant $δ>0$, and the total memory used is linear in the problem size. Our $2$-edge connectivity algorithm achieves the same parallel time as the connectivity algorithm of Andoni et al. (FOCS 2018). We also show an $Ω(\log D')$ conditional lower bound for the biconnectivity problem.
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
Venue: arXiv cs.CV 2019
Year: 2019

We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. We demonstrate our approach's practicality with an augmented reality smartphone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms.
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Venue: arXiv cs.CV 2019
Year: 2019

Many systems for autonomous vehicles' navigation rely on lane detection. Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions. On the other hand, free space detection algorithms only detect navigable areas, without information about lanes. State-of-the-art algorithms use CNNs for both tasks, with significant consumption of computing resources. We propose a novel approach that estimates the free space inside each lane, with a single CNN. Additionally, adding only a small requirement concerning GPU RAM, we infer the road type, that will be useful for path planning. To achieve this result, we train a multi-task CNN. Then, we further elaborate the output of the network, to extract polygons that can be effectively used in navigation control. Finally, we provide a computationally efficient implementation, based on ROS, that can be executed in real time. Our code and trained models are available online.
Content Delivery Latency of Caching Strategies for Information-Centric IoT
Venue: arXiv cs.NI 2019
Year: 2019

In-network caching is a central aspect of Information-Centric Networking (ICN). It enables the rapid distribution of content across the network, alleviating strain on content producers and reducing content delivery latencies. ICN has emerged as a promising candidate for use in the Internet of Things (IoT). However, IoT devices operate under severe constraints, most notably limited memory. This means that nodes cannot indiscriminately cache all content; instead, there is a need for a caching strategy that decides what content to cache. Furthermore, many applications in the IoT space are timesensitive; therefore, finding a caching strategy that minimises the latency between content request and delivery is desirable. In this paper, we evaluate a number of ICN caching strategies in regards to latency and hop count reduction using IoT devices in a physical testbed. We find that the topology of the network, and thus the routing algorithm used to generate forwarding information, has a significant impact on the performance of a given caching strategy. To the best of our knowledge, this is the first study that focuses on latency effects in ICN-IoT caching while using real IoT hardware, and the first to explicitly discuss the link between routing algorithm, network topology, and caching effects.
Physicist's Journeys Through the AI World - A Topical Review. There is no royal road to unsupervised learning
Venue: arXiv cs.LG 2019
Year: 2019

Artificial Intelligence (AI), defined in its most simple form, is a technological tool that makes machines intelligent. Since learning is at the core of intelligence, machine learning poses itself as a core sub-field of AI. Then there comes a subclass of machine learning, known as deep learning, to address the limitations of their predecessors. AI has generally acquired its prominence over the past few years due to its considerable progress in various fields. AI has vastly invaded the realm of research. This has led physicists to attentively direct their research towards implementing AI tools. Their central aim has been to gain better understanding and enrich their intuition. This review article is meant to supplement the previously presented efforts to bridge the gap between AI and physics, and take a serious step forward to filter out the "Babelian" clashes brought about from such gabs. This necessitates first to have fundamental knowledge about common AI tools. To this end, the review's primary focus shall be on deep learning models called artificial neural networks. They are deep learning models which train themselves through different learning processes. It discusses also the concept of Markov decision processes. Finally, shortcut to the main goal, the review thoroughly examines how these neural networks are capable to construct a physical theory describing some observations without applying any previous physical knowledge.
HRC-SoS: Human Robot Collaboration Experimentation Platform as System of Systems
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents an experimentation platform for human robot collaboration as a system of systems as well as proposes a conceptual framework describing the aspects of Human Robot Collaboration. These aspects are Awareness, Intelligence and Compliance of the system. Based on this framework case studies describing experiment setups performed using this platform are discussed. Each experiment highlights the use of the subsystems such as the digital twin, motion capture system, human-physiological monitoring system, data collection system and robot control and interface systems. A highlight of this paper showcases a subsystem with the ability to monitor human physiological feedback during a human robot collaboration task.
CompEngine: a self-organizing, living library of time-series data
Venue: arXiv cs.DB 2019
Year: 2019

Modern biomedical applications often involve time-series data, from high-throughput phenotyping of model organisms, through to individual disease diagnosis and treatment using biomedical data streams. Data and tools for time-series analysis are developed and applied across the sciences and in industry, but meaningful cross-disciplinary interactions are limited by the challenge of identifying fruitful connections. Here we introduce the web platform, CompEngine, a self-organizing, living library of time-series data that lowers the barrier to forming meaningful interdisciplinary connections between time series. Using a canonical feature-based representation, CompEngine places all time series in a common space, regardless of their origin, allowing users to upload their data and immediately explore interdisciplinary connections to other data with similar properties, and be alerted when similar data is uploaded in the future. In contrast to conventional databases, which are organized by assigned metadata, CompEngine incentivizes data sharing by automatically connecting experimental and theoretical scientists across disciplines based on the empirical structure of their data. CompEngine's growing library of interdisciplinary time-series data also facilitates comprehensively characterization of algorithm performance across diverse types of data, and can be used to empirically motivate the development of new time-series analysis algorithms.
Carrier Aggregation in Multi-Beam High Throughput Satellite Systems
Venue: arXiv cs.IT 2019
Year: 2019

Carrier Aggregation (CA) is an integral part of current terrestrial networks. Its ability to enhance the peak data rate, to efficiently utilize the limited available spectrum resources and to satisfy the demand for data-hungry applications has drawn large attention from different wireless network communities. Given the benefits of CA in the terrestrial wireless environment, it is of great interest to analyze and evaluate the potential impact of CA in the satellite domain. In this paper, we study CA in multibeam high throughput satellite systems. We consider both inter-transponder and intra-transponder CA at the satellite payload level of the communication stack, and we address the problem of carrier-user assignment assuming that multiple users can be multiplexed in each carrier. The transmission parameters of different carriers are generated considering the transmission characteristics of carriers in different transponders. In particular, we propose a flexible carrier allocation approach for a CA-enabled multibeam satellite system targeting a proportionally fair user demand satisfaction. Simulation results and analysis shed some light on this rather unexplored scenario and demonstrate the feasibility of the CA in satellite communication systems.
On the Impact of Memory Allocation on High-Performance Query Processing
Venue: arXiv cs.DB 2019
Year: 2019

Somewhat surprisingly, the behavior of analytical query engines is crucially affected by the dynamic memory allocator used. Memory allocators highly influence performance, scalability, memory efficiency and memory fairness to other processes. In this work, we provide the first comprehensive experimental analysis on the impact of memory allocation for high-performance query engines. We test five state-of-the-art dynamic memory allocators and discuss their strengths and weaknesses within our DBMS. The right allocator can increase the performance of TPC-DS (SF 100) by 2.7x on a 4-socket Intel Xeon server.