OpenRaiser/Intern-Atlas huggingface dataset:
Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.
A Unified Architecture for Data-Driven Metadata Tagging of Building Automation Systems
Venue: arXiv cs.CY 2020
Year: 2020

This article presents a Unified Architecture for automated point tagging of Building Automation System data, based on a combination of data-driven approaches. Advanced energy analytics applications-including fault detection and diagnostics and supervisory control-have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical Building Automation System implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: 1. commercial retail buildings and 2. office buildings from the National Renewable Energy Laboratory campus. The proposed methodology correctly applied 85-90 percent and 70-75 percent of the tags in each of these test scenarios, respectively.
$F$, $B$, Alpha Matting
Venue: arXiv cs.CV 2020
Year: 2020

Cutting out an object and estimating its opacity mask, known as image matting, is a key task in many image editing applications. Deep learning approaches have made significant progress by adapting the encoder-decoder architecture of segmentation networks. However, most of the existing networks only predict the alpha matte and post-processing methods must then be used to recover the original foreground and background colours in the transparent regions. Recently, two methods have shown improved results by also estimating the foreground colours, but at a significant computational and memory cost. In this paper, we propose a low-cost modification to alpha matting networks to also predict the foreground and background colours. We study variations of the training regime and explore a wide range of existing and novel loss functions for the joint prediction. Our method achieves the state of the art performance on the Adobe Composition-1k dataset for alpha matte and composite colour quality. It is also the current best performing method on the alphamatting.com online evaluation.
A Novel Online Action Detection Framework from Untrimmed Video Streams
Venue: arXiv cs.CV 2020
Year: 2020

Online temporal action localization from an untrimmed video stream is a challenging problem in computer vision. It is challenging because of i) in an untrimmed video stream, more than one action instance may appear, including background scenes, and ii) in online settings, only past and current information is available. Therefore, temporal priors, such as the average action duration of training data, which have been exploited by previous action detection methods, are not suitable for this task because of the high intra-class variation in human actions. We propose a novel online action detection framework that considers actions as a set of temporally ordered subclasses and leverages a future frame generation network to cope with the limited information issue associated with the problem outlined above. Additionally, we augment our data by varying the lengths of videos to allow the proposed method to learn about the high intra-class variation in human actions. We evaluate our method using two benchmark datasets, THUMOS'14 and ActivityNet, for an online temporal action localization scenario and demonstrate that the performance is comparable to state-of-the-art methods that have been proposed for offline settings.
Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
Venue: arXiv cs.CV 2020
Year: 2020

Recently, deep learning approaches have been extensively investigated to reconstruct images from accelerated magnetic resonance image (MRI) acquisition. Although these approaches provide significant performance gain compared to compressed sensing MRI (CS-MRI), it is not clear how to choose a suitable network architecture to balance the trade-off between network complexity and performance. Recently, it was shown that an encoder-decoder convolutional neural network (CNN) can be interpreted as a piecewise linear basis-like representation, whose specific representation is determined by the ReLU activation patterns for a given input image. Thus, the expressivity or the representation power is determined by the number of piecewise linear regions. As an extension of this geometric understanding, this paper proposes a systematic geometric approach using bootstrapping and subnetwork aggregation using an attention module to increase the expressivity of the underlying neural network. Our method can be implemented in both k-space domain and image domain that can be trained in an end-to-end manner. Experimental results show that the proposed schemes significantly improve reconstruction performance with negligible complexity increases.
TraLFM: Latent Factor Modeling of Traffic Trajectory Data
Venue: arXiv cs.SI 2020
Year: 2020

The widespread use of positioning devices (e.g., GPS) has given rise to a vast body of human movement data, often in the form of trajectories. Understanding human mobility patterns could benefit many location-based applications. In this paper, we propose a novel generative model called TraLFM via latent factor modeling to mine human mobility patterns underlying traffic trajectories. TraLFM is based on three key observations: (1) human mobility patterns are reflected by the sequences of locations in the trajectories; (2) human mobility patterns vary with people; and (3) human mobility patterns tend to be cyclical and change over time. Thus, TraLFM models the joint action of sequential, personal and temporal factors in a unified way, and brings a new perspective to many applications such as latent factor analysis and next location prediction. We perform thorough empirical studies on two real datasets, and the experimental results confirm that TraLFM outperforms the state-of-the-art methods significantly in these applications.
Capacitive storage in mycelium substrate
Venue: arXiv cs.ET 2020
Year: 2020

The emerging field of living technologies aims to create new functional hybrid materials in which living systems interface with artificial ones. Combining research into living technologies with emerging developments in computing architecture has enabled the generation of organic electronics from plants and slime mould. Here, we expand on this work by studying capacitive properties of a substrate colonised by mycelium of grey oyster fungi, Pleurotus ostreatus. Capacitors play a fundamental role in traditional analogue and digital electronic systems and have a range of uses including sensing, energy storage and filter circuits. Mycelium has the potential to be used as an organic replacement for traditional capacitor technology. Here, were show that the capacitance of mycelium is in the order of hundreds of pico-Farads. We also demonstrate that the charge density of the mycelium `dielectric' decays rapidly with increasing distance from the source probes. This is important as it indicates that small cells of mycelium could be used as a charge carrier or storage medium, when employed as part of an array with reasonable density.
On the Use of Quantum Entanglement in Secure Communications: A Survey
Venue: arXiv cs.CR 2020
Year: 2020

Quantum computing and quantum communications are exciting new frontiers in computing and communications. Indeed, the massive investments made by the governments of the US, China, and EU in these new technologies are not a secret and are based on the expected potential of these technologies to revolutionize communications, computing, and security. In addition to several field trials and hero experiments, a number of companies such as Google and IBM are actively working in these areas and some have already reported impressive demonstrations in the past few years. While there is some skepticism about whether quantum cryptography will eventually replace classical cryptography, the advent of quantum computing could necessitate the use of quantum cryptography as the ultimate frontier of secure communications. This is because, with the amazing speeds demonstrated with quantum computers, breaking cryptographic keys might no longer be a daunting task in the next decade or so. Hence, quantum cryptography as the ultimate frontier in secure communications might not be such a far-fetched idea. It is well known that Heisenberg's Uncertainty Principle is essentially a "negative result" in Physics and Quantum Mechanics. It turns out that Heisenberg's Uncertainty Principle, one of the most interesting results in Quantum Mechanics, could be the theoretical basis and the main scientific principle behind the ultimate frontier in quantum cryptography or secure communications in conjunction with Quantum Entanglement.
An Exploratory Study of Bot Commits
Venue: arXiv cs.SE 2020
Year: 2020

Background: Bots help automate many of the tasks performed by software developers and are widely used to commit code in various social coding platforms. At present, it is not clear what types of activities these bots perform and understanding it may help design better bots, and find application areas which might benefit from bot adoption. Aim: We aim to categorize the Bot Commits by the type of change (files added, deleted, or modified), find the more commonly changed file types, and identify the groups of file types that tend to get updated together. Method: 12,326,137 commits made by 461 popular bots (that made at least 1000 commits) were examined to identify the frequency and the type of files added/ deleted/ modified by the commits, and association rule mining was used to identify the types of files modified together. Result: Majority of the bot commits modify an existing file, a few of them add new files, while deletion of a file is very rare. Commits involving more than one type of operation are even rarer. Files containing data, configuration, and documentation are most frequently updated, while HTML is the most common type in terms of the number of files added, deleted, and modified. Files of the type "Markdown", "Ignore List", "YAML", "JSON" were the types that are updated together with other types of files most frequently. Conclusion: We observe that majority of bot commits involve single file modifications, and bots primarily work with data, configuration, and documentation files. A better understanding if this is a limitation of the bots and, if overcome, would lead to different kinds of bots remains an open question.
Cross Lingual Cross Corpus Speech Emotion Recognition
Venue: arXiv cs.SD 2020
Year: 2020

The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting. These systems do not perform as well when applied in a cross-corpus and cross-language scenario. This paper presents results for speech emotion recognition for 4 languages in both single corpus and cross corpus setting. Additionally, since multi-task learning (MTL) with gender, naturalness and arousal as auxiliary tasks has shown to enhance the generalisation capabilities of the emotion models, this paper introduces language ID as another auxiliary task in MTL framework to explore the role of spoken language on emotion recognition which has not been studied yet.
Applying r-spatiogram in object tracking for occlusion handling
Venue: arXiv cs.CV 2020
Year: 2020

Object tracking is one of the most important problems in computer vision. The aim of video tracking is to extract the trajectories of a target or object of interest, i.e. accurately locate a moving target in a video sequence and discriminate target from non-targets in the feature space of the sequence. So, feature descriptors can have significant effects on such discrimination. In this paper, we use the basic idea of many trackers which consists of three main components of the reference model, i.e., object modeling, object detection and localization, and model updating. However, there are major improvements in our system. Our forth component, occlusion handling, utilizes the r-spatiogram to detect the best target candidate. While spatiogram contains some moments upon the coordinates of the pixels, r-spatiogram computes region-based compactness on the distribution of the given feature in the image that captures richer features to represent the objects. The proposed research develops an efficient and robust way to keep tracking the object throughout video sequences in the presence of significant appearance variations and severe occlusions. The proposed method is evaluated on the Princeton RGBD tracking dataset considering sequences with different challenges and the obtained results demonstrate the effectiveness of the proposed method.
A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency
Venue: arXiv cs.SI 2020
Year: 2020

A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new baseline correlation approach, which not only exhibits a decent prediction accuracy, but also reduces the computation burden and enables a fast decision making without the knowledge of historical data. Polynomial regression, classification modelling and lexicon-based sentiment analysis are performed using R. The obtained TSS predicts the future stock market trend in advance by 15 time samples (30 working hours) with an accuracy of 67.22% using the proposed baseline criterion without referring to historical TSS or market data. Specifically, TSS's prediction performance of an upward market is found far better than that of a downward market. Under the logistic regression and linear discriminant analysis, the accuracy of TSS in predicting the upward trend of the future market achieves 97.87%.
Counting Bounded Tree Depth Homomorphisms
Venue: arXiv cs.LO 2020
Year: 2020

We prove that graphs G, G' satisfy the same sentences of first-order logic with counting of quantifier rank at most k if and only if they are homomorphism-indistinguishable over the class of all graphs of tree depth at most k. Here G, G' are homomorphism-indistinguishable over a class C of graphs if for each graph F in C, the number of homomorphisms from F to G equals the number of homomorphisms from F to G'.
The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
Venue: arXiv cs.SI 2020
Year: 2020

It remains unknown whether personalized recommendations increase or decrease the diversity of content people consume. We present results from a randomized field experiment on Spotify testing the effect of personalized recommendations on consumption diversity. In the experiment, both control and treatment users were given podcast recommendations, with the sole aim of increasing podcast consumption. Treatment users' recommendations were personalized based on their music listening history, whereas control users were recommended popular podcasts among users in their demographic group. We find that, on average, the treatment increased podcast streams by 28.90%. However, the treatment also decreased the average individual-level diversity of podcast streams by 11.51%, and increased the aggregate diversity of podcast streams by 5.96%, indicating that personalized recommendations have the potential to create patterns of consumption that are homogenous within and diverse across users, a pattern reflecting Balkanization. Our results provide evidence of an "engagement-diversity trade-off" when recommendations are optimized solely to drive consumption: while personalized recommendations increase user engagement, they also affect the diversity of consumed content. This shift in consumption diversity can affect user retention and lifetime value, and impact the optimal strategy for content producers. We also observe evidence that our treatment affected streams from sections of Spotify's app not directly affected by the experiment, suggesting that exposure to personalized recommendations can affect the content that users consume organically. We believe these findings highlight the need for academics and practitioners to continue investing in personalization methods that explicitly take into account the diversity of content recommended.
Optimal Image Smoothing and Its Applications in Anomaly Detection in Remote Sensing
Venue: arXiv cs.CV 2020
Year: 2020

This paper is focused on deriving an optimal image smoother. The optimization is done through the minimization of the norm of the Laplace operator in the image coordinate system. Discretizing the Laplace operator and using the method of Euler-Lagrange result in a weighted average scheme for the optimal smoother. Satellite imagery can be smoothed by this optimal smoother. It is also very fast and can be used for detecting the anomalies in the image. A real anomaly detecting problem is considered for the Qom region in Iran. Satellite image in different bands are smoothed. Comparing the smoothed and original images in different bands, the maps of anomalies are presented. Comparison between the derived method and the existing methods reveals that it is more efficient in detecting anomalies in the region.
Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method
Venue: arXiv cs.SD 2020
Year: 2020

With the rapidly growing number of security-sensitive systems that use voice as the primary input, it becomes increasingly important to address these systems' potential vulnerability to replay attacks. Previous efforts to address this concern have focused primarily on single-channel audio. In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.
Detection of Pitt-Hopkins Syndrome based on morphological facial features
Venue: arXiv cs.CV 2020
Year: 2020

This work describes an automatic methodology to discriminate between individuals with the genetic disorder Pitt-Hopkins syndrome (PTHS), and healthy individuals. As input data, the methodology accepts unconstrained frontal facial photographs, from which faces are located with Histograms of Oriented Gradients features descriptors. Pre-processing steps of the methodology consist of colour normalisation, scaling down, rotation, and cropping in order to produce a series of images of faces with consistent dimensions. Sixty eight facial landmarks are automatically located on each face through a cascade of regression functions learnt via gradient boosting to estimate the shape from an initial approximation. The intensities of a sparse set of pixels indexed relative to this initial estimate are used to determine the landmarks. A set of carefully selected geometric features, for example, relative width of the mouth, or angle of the nose, are extracted from the landmarks. The features are used to investigate the statistical differences between the two populations of PTHS and healthy controls. The methodology was tested on 71 individuals with PTHS and 55 healthy controls. Two geometric features related to the nose and mouth showed statistical difference between the two populations.
On the Analysis of Parallel Real-Time Tasks with Spin Locks
Venue: arXiv cs.DC 2020
Year: 2020

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. Although the design and analysis of locking protocols have been intensively studied for sequential real-time tasks, there has been little work on this topic for parallel real-time tasks. In this paper, we study the analysis of parallel real-time tasks using spin locks to protect accesses to shared resources in three commonly used request serving orders (unordered, FIFO-order and priority-order). A remarkable feature making our analysis method more accurate is to systematically analyze the blocking time which may delay a task's finishing time, where the impact to the total workload and the longest path length is jointly considered, rather than analyzing them separately and counting all blocking time as the workload that delays a task's finishing time, as commonly assumed in the state-of-the-art.
Worst-Case Optimal Covering of Rectangles by Disks
Venue: arXiv cs.CG 2020
Year: 2020

We provide the solution for a fundamental problem of geometric optimization by giving a complete characterization of worst-case optimal disk coverings of rectangles: For any $λ\geq 1$, the critical covering area $A^*(λ)$ is the minimum value for which any set of disks with total area at least $A^*(λ)$ can cover a rectangle of dimensions $λ\times 1$. We show that there is a threshold value $λ_2 = \sqrt{\sqrt{7}/2 - 1/4} \approx 1.035797\ldots$, such that for $λ<λ_2$ the critical covering area $A^*(λ)$ is $A^*(λ)=3π\left(\frac{λ^2}{16} +\frac{5}{32} + \frac{9}{256λ^2}\right)$, and for $λ\geq λ_2$, the critical area is $A^*(λ)=π(λ^2+2)/4$; these values are tight. For the special case $λ=1$, i.e., for covering a unit square, the critical covering area is $\frac{195π}{256}\approx 2.39301\ldots$. The proof uses a careful combination of manual and automatic analysis, demonstrating the power of the employed interval arithmetic technique.