OpenRaiser/Intern-Atlas huggingface dataset:
Consumer Demand Modeling During COVID-19 Pandemic
Venue: arXiv cs.AI 2021
Year: 2021

The current pandemic has introduced substantial uncertainty to traditional methods for demand planning. These uncertainties stem from the disease progression, government interventions, economy and consumer behavior. While most of the emerging literature on the pandemic has focused on disease progression, a few have focused on consequent regulations and their impact on individual behavior. The contributions of this paper include a quantitative behavior model of fear of COVID-19, impact of government interventions on consumer behavior, and impact of consumer behavior on consumer choice and hence demand for goods. It brings together multiple models for disease progression, consumer behavior and demand estimation-thus bridging the gap between disease progression and consumer demand. We use panel regression to understand the drivers of demand during the pandemic and Bayesian inference to simplify the regulation landscape that can help build scenarios for resilient demand planning. We illustrate this resilient demand planning model using a specific example of gas retailing. We find that demand is sensitive to fear of COVID-19: as the number of COVID-19 cases increase over the previous week, the demand for gas decreases -- though this dissipates over time. Further, government regulations restrict access to different services, thereby reducing mobility, which in itself reduces demand.
Formalizing the Four-layer Metamodeling Stack -- Potential and Benefits
Venue: arXiv cs.SE 2021
Year: 2021

Enterprise modeling deals with the increasing complexity of processes and systems by operationalizing model content and by linking complementary models and languages, thus amplifying the model-value beyond mere comprehensible pictures. To enable this amplification and turn models into computer-processable structures a comprehensive formalization is needed. This paper presents a generic formalism based on typed first-order logic and provides a perspective on the potential and benefits arising for a variety of research issues in conceptual modeling. We define modeling languages as formal languages with a signature $Σ$ - comprising object types, relation types, and attributes through types and function symbols - and a set of constraints. Three cases studies are included to show the effectiveness of the approach. Applying the formalism to the next level in the hierarchy of models we create M2FOL, a formal modeling language for metamodels. We show that M2FOL is self-describing and therefore complete the formalization of the full four-layer metamodeling stack. On the basis of our generic formalism applicable to arbitrary modeling languages we examine three current research topics - language interleaving & consistency, operations on models, and automatic translation of formalizations to platform-specific code - and how to approach them with the proposed formalism. This shows that the rich knowledge stack on formal languages in logic offers new tools for old problems.
Multiple-Output Channel Simulation and Lossy Compression of Probability Distributions
Venue: arXiv cs.IT 2021
Year: 2021

We consider a variant of the channel simulation problem with a single input and multiple outputs, where Alice observes a probability distribution $P$ from a set of prescribed probability distributions $\mathbb{\mathcal{P}}$, and sends a prefix-free codeword $W$ to Bob to allow him to generate $n$ i.i.d. random variables $X_{1},X_{2,}...,X_{n}$ which follow the distribution $P$. This can also be regarded as a lossy compression setting for probability distributions. This paper describes encoding schemes for three cases of $P$: $P$ is a distribution over positive integers, $P$ is a continuous distribution over $[0,1]$ with a non-increasing pdf, and $P$ is a continuous distribution over $[0,\infty)$ with a non-increasing pdf. We show that the growth rate of the expected codeword length is sub-linear in $n$ when a power law bound is satisfied. An application of multiple-outputs channel simulation is the compression of probability distributions.
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Venue: arXiv cs.IR 2021
Year: 2021

Deep learning recommendation systems at scale have provided remarkable gains through increasing model capacity (i.e. wider and deeper neural networks), but it comes at significant training cost and infrastructure cost. Model pruning is an effective technique to reduce computation overhead for deep neural networks by removing redundant parameters. However, modern recommendation systems are still thirsty for model capacity due to the demand for handling big data. Thus, pruning a recommendation model at scale results in a smaller model capacity and consequently lower accuracy. To reduce computation cost without sacrificing model capacity, we propose a dynamic training scheme, namely alternate model growth and pruning, to alternatively construct and prune weights in the course of training. Our method leverages structured sparsification to reduce computational cost without hurting the model capacity at the end of offline training so that a full-size model is available in the recurring training stage to learn new data in real-time. To the best of our knowledge, this is the first work to provide in-depth experiments and discussion of applying structural dynamics to recommendation systems at scale to reduce training cost. The proposed method is validated with an open-source deep-learning recommendation model (DLRM) and state-of-the-art industrial-scale production models.
Tuple Interpretations for Higher-Order Rewriting
Venue: arXiv cs.SC 2021
Year: 2021

We develop a class of algebraic interpretations for many-sorted and higher-order term rewriting systems that takes type information into account. Specifically, base-type terms are mapped to \emph{tuples} of natural numbers and higher-order terms to functions between those tuples. Tuples may carry information relevant to the type; for instance, a term of type $\mathsf{nat}$ may be associated to a pair $(\mathsf{cost}, \mathsf{size})$ representing its evaluation cost and size. This class of interpretations results in a more fine-grained notion of complexity than runtime or derivational complexity, which makes it particularly useful to obtain complexity bounds for higher-order rewriting systems. We show that rewriting systems compatible with tuple interpretations admit finite bounds on derivation height. Furthermore, we demonstrate how to mechanically construct tuple interpretations and how to orient $β$ and $η$ reductions within our technique. Finally, we relate our method to runtime complexity and prove that specific interpretation shapes imply certain runtime complexity bounds.
Modeling Social Readers: Novel Tools for Addressing Reception from Online Book Reviews
Venue: arXiv cs.CL 2021
Year: 2021

Readers' responses to literature have received scant attention in computational literary studies. The rise of social media offers an opportunity to capture a segment of these responses while data-driven analysis of these responses can provide new critical insight into how people "read". Posts discussing an individual book on Goodreads, a social media platform that hosts user discussions of popular literature, are referred to as "reviews", and consist of plot summaries, opinions, quotes, or some mixture of these. Since these reviews are written by readers, computationally modeling them allows one to discover the overall non-professional discussion space about a work, including an aggregated summary of the work's plot, an implicit ranking of the importance of events, and the readers' impressions of main characters. We develop a pipeline of interlocking computational tools to extract a representation of this reader generated shared narrative model. Using a corpus of reviews of five popular novels, we discover the readers' distillation of the main storylines in a novel, their understanding of the relative importance of characters, as well as the readers' varying impressions of these characters. In so doing, we make three important contributions to the study of infinite vocabulary networks: (i) an automatically derived narrative network that includes meta-actants; (ii) a new sequencing algorithm, REV2SEQ, that generates a consensus sequence of events based on partial trajectories aggregated from the reviews; and (iii) a new "impressions" algorithm, SENT2IMP, that provides finer, non-trivial and multi-modal insight into readers' opinions of characters.
Network Coding in Photonic-land: Three Commandments for Future-proof Optical Core Networks
Venue: arXiv cs.NI 2021
Year: 2021

The digital transformation has been underway, creating digital shadows of (almost) all physical entities and moving them to the Internet. The era of Internet of Everything has therefore started to come into play, giving rise to unprecedented traffic growths. In this context, optical core networks forming the backbone of Internet infrastructure have been under critical issues of reaching the capacity limit of conventional fiber, a phenomenon widely referred as capacity crunch. For many years, the many-fold increases in fiber capacity is thanks to exploiting physical dimensions for multiplexing optical signals such as wavelength, polarization, time and lately space-division multiplexing using multi-core fibers and such route seems to come to an end as almost all known ways have been exploited. This necessitates for a departure from traditional approaches to use the fiber capacity more efficiently and thereby improve economics of scale. This paper lays out a new perspective to integrate network coding (NC) functions into optical networks to achieve greater capacity efficiency by upgrading intermediate nodes functionalities. In addition to the review of recent proposals on new research problems enabled by NC operation in optical networks, we also report state-of-the-art findings in the literature in an effort to renew the interest of NC in optical networks and discuss three critical points for pushing forward its applicability and practicality including i) NC as a new dimension for multiplexing optical signals ii) algorithmic aspects of NC-enabled optical networks design iii) NC as an entirely fresh way for securing optical signals at physical layers
EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia
Venue: arXiv cs.LG 2021
Year: 2021

Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multiple areas, including development of biomarkers for cancer, disease subtype identification, or gene-drug interactions among others. In this paper we introduce EBIC.JL - an implementation of one of the most accurate biclustering algorithms in Julia, a modern highly parallelizable programming language for data science. We show that the new version maintains comparable accuracy to its predecessor EBIC while converging faster for the majority of the problems. We hope that this open source software in a high-level programming language will foster research in this promising field of bioinformatics and expedite development of new biclustering methods for big data.
ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders
Venue: arXiv cs.CL 2021
Year: 2021

Pre-trained text encoders have drawn sustaining attention in natural language processing (NLP) and shown their capability in obtaining promising results in different tasks. Recent studies illustrated that external self-supervised signals (or knowledge extracted by unsupervised learning, such as n-grams) are beneficial to provide useful semantic evidence for understanding languages such as Chinese, so as to improve the performance on various downstream tasks accordingly. To further enhance the encoders, in this paper, we propose to pre-train n-gram-enhanced encoders with a large volume of data and advanced techniques for training. Moreover, we try to extend the encoder to different languages as well as different domains, where it is confirmed that the same architecture is applicable to these varying circumstances and new state-of-the-art performance is observed from a long list of NLP tasks across languages and domains.
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs
Venue: arXiv cs.CV 2021
Year: 2021

Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance. Some of the challenges in change detection include background fluctuations, illumination variation, weather changes, intermittent object motion, shadow, fast/slow object motion, camera motion, heterogeneous object shapes and real-time processing. Traditionally, this problem has been solved using hand-crafted features and background modelling techniques. In recent years, deep learning frameworks have been successfully adopted for robust change detection. This article aims to provide an empirical review of the state-of-the-art deep learning methods for change detection. More specifically, we present a detailed analysis of the technical characteristics of different model designs and experimental frameworks. We provide model design based categorization of the existing approaches, including the 2D-CNN, 3D-CNN, ConvLSTM, multi-scale features, residual connections, autoencoders and GAN based methods. Moreover, an empirical analysis of the evaluation settings adopted by the existing deep learning methods is presented. To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods. Finally, we point out the research needs, future directions and draw our own conclusions.
Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey
Venue: arXiv cs.CR 2021
Year: 2021

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.
Secure Virtual Mobile Small Cells: A Stepping Stone Towards 6G
Venue: arXiv cs.NI 2021
Year: 2021

As 5th Generation research reaches the twilight, the research community must go beyond 5G and look towards the 2030 connectivity landscape, namely 6G. In this context, this work takes a step towards the 6G vision by proposing a next generation communication platform, which aims to extend the rigid coverage area of fixed deployment networks by considering virtual mobile small cells (MSC) that are created on demand. Relying on emerging computing paradigms such as NFV (Network Function Virtualization) and SDN (Software Defined Networking), these cells can harness radio and networking capability locally reducing protocol signaling latency and overhead. These MSCs constitute an intelligent pool of networking resources that can collaborate to form a wireless network of MSCs providing a communication platform for localized, ubiquitous and reliable connectivity. The technology enablers for implementing the MSC concept are also addressed in terms of virtualization, lightweight wireless security, and energy efficient RF. The benefits of the MSC architecture towards reliable and efficient cell offloading are demonstrated as a use-case.
QSOC: Quantum Service-Oriented Computing
Venue: arXiv cs.SE 2021
Year: 2021

Quantum computing is quickly turning from a promise to a reality, witnessing the launch of several cloud-based, general-purpose offerings, and IDEs. Unfortunately, however, existing solutions typically implicitly assume intimate knowledge about quantum computing concepts and operators. This paper introduces Quantum Service-Oriented Computing (QSOC), including a model-driven methodology to allow enterprise DevOps teams to compose, configure and operate enterprise applications without intimate knowledge on the underlying quantum infrastructure, advocating knowledge reuse, separation of concerns, resource optimization, and mixed quantum- & conventional QSOC applications.
Hardness-Preserving Reductions via Cuckoo Hashing
Venue: arXiv cs.CR 2021
Year: 2021

The focus of this work is \emph{hardness-preserving} transformations of somewhat limited pseudorandom functions families (PRFs) into ones with more versatile characteristics. Consider the problem of \emph{domain extension} of pseudorandom functions: given a PRF that takes as input elements of some domain $U$, we would like to come up with a PRF over a larger domain. Can we do it with little work and without significantly impacting the security of the system? One approach is to first hash the larger domain into the smaller one and then apply the original PRF. Such a reduction, however, is vulnerable to a "birthday attack": after $\sqrt{\size{U}}$ queries to the resulting PRF, a collision (\ie two distinct inputs having the same hash value) is very likely to occur. As a consequence, the resulting PRF is \emph{insecure} against an attacker making this number of queries. In this work we show how to go beyond the aforementioned birthday attack barrier by replacing the above simple hashing approach with a variant of \textit{cuckoo hashing}, a hashing paradigm that resolves collisions in a table by using two hash functions and two tables, cleverly assigning each element to one of the two tables. We use this approach to obtain: (i) a domain extension method that requires {\em just two calls} to the original PRF, can withstand as many queries as the original domain size, and has a distinguishing probability that is exponentially small in the amount of non-cryptographic work; and (ii) a {\em security-preserving} reduction from non-adaptive to adaptive PRFs.
A Power and Area Efficient Lepton Hardware Encoder with Hash-based Memory Optimization
Venue: arXiv cs.MM 2021
Year: 2021

Although it has been surpassed by many subsequent coding standards, JPEG occupies a large share of the storage load of the current data hosting service. To reduce the storage costs, DropBox proposed a lossless secondary compression algorithm, Lepton, to further improve the compression rate of JPEG images. However, the bloated probability models defined by Lepton severely restrict its throughput and energy efficiency. To solve this problem, we construct an efficient access probability-based hash function for the probability models, and then propose a hardware-friendly memory optimization method by combining the proposed hash function and the N-way Set-Associative unit. After that, we design a highly parameterized hardware structure for the probability models and finally implement a power and area efficient Lepton hardware encoder. To the best of our knowledge, this is the first hardware implementation of Lepton. The synthesis result shows that the proposed hardware structure reduces the total area of the probability models by 70.97%. Compared with DropBox's software solution, the throughput and the energy efficiency of the proposed Lepton hardware encoder are increased by 55.25 and 4899 times respectively. In terms of manufacturing cost, the proposed Lepton hardware encoder is also significantly lower than the general-purpose CPU used by DropBox.
Learning Traffic Speed Dynamics from Visualizations
Venue: arXiv cs.LG 2021
Year: 2021

Space-time visualizations of macroscopic or microscopic traffic variables is a qualitative tool used by traffic engineers to understand and analyze different aspects of road traffic dynamics. We present a deep learning method to learn the macroscopic traffic speed dynamics from these space-time visualizations, and demonstrate its application in the framework of traffic state estimation. Compared to existing estimation approaches, our approach allows a finer estimation resolution, eliminates the dependence on the initial conditions, and is agnostic to external factors such as traffic demand, road inhomogeneities and driving behaviors. Our model respects causality in traffic dynamics, which improves the robustness of estimation. We present the high-resolution traffic speed fields estimated for several freeway sections using the data obtained from the Next Generation Simulation Program (NGSIM) and German Highway (HighD) datasets. We further demonstrate the quality and utility of the estimation by inferring vehicle trajectories from the estimated speed fields, and discuss the benefits of deep neural network models in approximating the traffic dynamics.
Enhancing Generalizability of Predictive Models with Synergy of Data and Physics
Venue: arXiv cs.LG 2021
Year: 2021

Wind farm needs prediction models for predictive maintenance. There is a need to predict values of non-observable parameters beyond ranges reflected in available data. A prediction model developed for one machine many not perform well in another similar machine. This is usually due to lack of generalizability of data-driven models. To increase generalizability of predictive models, this research integrates the data mining with first-principle knowledge. Physics-based principles are combined with machine learning algorithms through feature engineering, strong rules and divide-and-conquer. The proposed synergy concept is illustrated with the wind turbine blade icing prediction and achieves significant prediction accuracy across different turbines. The proposed process is widely accepted by wind energy predictive maintenance practitioners because of its simplicity and efficiency. Furthermore, this paper demonstrates the importance of embedding physical principles within the machine learning process, and also highlight an important point that the need for more complex machine learning algorithms in industrial big data mining is often much less than it is in other applications, making it essential to incorporate physics and follow Less is More philosophy.