OpenRaiser/Intern-Atlas huggingface dataset:
Private 5G: The Future of Industrial Wireless
Venue: arXiv cs.NI 2020
Year: 2020

High-performance wireless communication is crucial in digital transformation of industrial systems which is driven by Industry 4.0 and the Industrial Internet initiatives. Among the candidate industrial wireless technologies, 5G (cellular/mobile) holds significant potential. Operation of private (non-public) 5G networks in industrial environments is promising to fully unleash this potential. This article provides a technical overview of private 5G networks. It introduces the concept and functional architecture of private 5G while highlighting the key benefits and industrial use-cases. It explores spectrum opportunities for private 5G networks. It also discusses design aspects of private 5G along with the key challenges. Finally, it explores the emerging standardization and open innovation ecosystem for private 5G.
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start
Venue: arXiv cs.IR 2020
Year: 2020

E-commerce platforms provide their customers with ranked lists of recommended items matching the customers' preferences. Merchants on e-commerce platforms would like their items to appear as high as possible in the top-N of these ranked lists. In this paper, we demonstrate how unscrupulous merchants can create item images that artificially promote their products, improving their rankings. Recommender systems that use images to address the cold start problem are vulnerable to this security risk. We describe a new type of attack, Adversarial Item Promotion (AIP), that strikes directly at the core of Top-N recommenders: the ranking mechanism itself. Existing work on adversarial images in recommender systems investigates the implications of conventional attacks, which target deep learning classifiers. In contrast, our AIP attacks are embedding attacks that seek to push features representations in a way that fools the ranker (not a classifier) and directly lead to item promotion. We introduce three AIP attacks insider attack, expert attack, and semantic attack, which are defined with respect to three successively more realistic attack models. Our experiments evaluate the danger of these attacks when mounted against three representative visually-aware recommender algorithms in a framework that uses images to address cold start. We also evaluate potential defenses, including adversarial training and find that common, currently-existing, techniques do not eliminate the danger of AIP attacks. In sum, we show that using images to address cold start opens recommender systems to potential threats with clear practical implications.
Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems
Venue: arXiv cs.HC 2020
Year: 2020

Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system- and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported state-of-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.
Designing Differentially Private Estimators in High Dimensions
Venue: arXiv cs.LG 2020
Year: 2020

We study differentially private mean estimation in a high-dimensional setting. Existing differential privacy techniques applied to large dimensions lead to computationally intractable problems or estimators with excessive privacy loss. Recent work in high-dimensional robust statistics has identified computationally tractable mean estimation algorithms with asymptotic dimension-independent error guarantees. We incorporate these results to develop a strict bound on the global sensitivity of the robust mean estimator. This yields a computationally tractable algorithm for differentially private mean estimation in high dimensions with dimension-independent privacy loss. Finally, we show on synthetic data that our algorithm significantly outperforms classic differential privacy methods, overcoming barriers to high-dimensional differential privacy.
Continual Learning of Predictive Models in Video Sequences via Variational Autoencoders
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience, an initial Variational Autoencoder, together with a set of fully connected neural networks are utilized to respectively learn the appearance of video frames and their dynamics at the latent space level. By employing an adapted Markov Jump Particle Filter, the proposed method recognizes new situations and integrates them as predictive models avoiding catastrophic forgetting of previously learned tasks. For evaluating the proposed method, this article uses video sequences from a vehicle that performs different tasks in a controlled environment.
The Typology of Polysemy: A Multilingual Distributional Framework
Venue: arXiv cs.CL 2020
Year: 2020

Lexical semantic typology has identified important cross-linguistic generalizations about the variation and commonalities in polysemy patterns---how languages package up meanings into words. Recent computational research has enabled investigation of lexical semantics at a much larger scale, but little work has explored lexical typology across semantic domains, nor the factors that influence cross-linguistic similarities. We present a novel computational framework that quantifies semantic affinity, the cross-linguistic similarity of lexical semantics for a concept. Our approach defines a common multilingual semantic space that enables a direct comparison of the lexical expression of concepts across languages. We validate our framework against empirical findings on lexical semantic typology at both the concept and domain levels. Our results reveal an intricate interaction between semantic domains and extra-linguistic factors, beyond language phylogeny, that co-shape the typology of polysemy across languages.
Detecting and Understanding Real-World Differential Performance Bugs in Machine Learning Libraries
Venue: arXiv cs.LG 2020
Year: 2020

Programming errors that degrade the performance of systems are widespread, yet there is little tool support for analyzing these bugs. We present a method based on differential performance analysis---we find inputs for which the performance varies widely, despite having the same size. To ensure that the differences in the performance are robust (i.e. hold also for large inputs), we compare the performance of not only single inputs, but of classes of inputs, where each class has similar inputs parameterized by their size. Thus, each class is represented by a performance function from the input size to performance. Importantly, we also provide an explanation for why the performance differs in a form that can be readily used to fix a performance bug. The two main phases in our method are discovery with fuzzing and explanation with decision tree classifiers, each of which is supported by clustering. First, we propose an evolutionary fuzzing algorithm to generate inputs. For this fuzzing task, the unique challenge is that we not only need the input class with the worst performance, but rather a set of classes exhibiting differential performance. We use clustering to merge similar input classes which significantly improves the efficiency of our fuzzer. Second, we explain the differential performance in terms of program inputs and internals. We adapt discriminant learning approaches with clustering and decision trees to localize suspicious code regions. We applied our techniques to a set of applications. On a set of micro-benchmarks, we show that our approach outperforms state-of-the-art fuzzers in finding inputs to characterize the differential performance. On a set of case-studies, we discover and explain multiple performance bugs in popular machine learning frameworks. Four of these bugs, reported first in this paper, have since been fixed by the developers.
Open-Set Recognition with Gaussian Mixture Variational Autoencoders
Venue: arXiv cs.LG 2020
Year: 2020

In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability to distinguish unknown classes. In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. With this, our Gaussian mixture variational autoencoder (GMVAE) achieves more accurate and robust open-set classification results, with an average F1 improvement of 29.5%, through extensive experiments aided by analytical results.
Proximity-based Networking: Small world overlays optimized with particle swarm optimization
Venue: arXiv cs.NI 2020
Year: 2020

Information dissemination is a fundamental and frequently occurring problem in large, dynamic, distributed systems. In order to solve this, there has been an increased interest in creating efficient overlay networks that can maintain decentralized peer-to-peer networks. Within these overlay networks nodes take the patterns of small world networks, whose connections are based on proximity. These small-world systems can be incredibly useful in the dissemination and lookup of information within an internet network. The data can be efficiently transferred and routing with minimal information loss through forward error correct (FEC) and the User Datagram Protocol (UDP). We propose a networking scheme that incorporates geographic location in chord for the organization of peers within each node's partitioned key space. When we combine this with a proximity-based neighborhood set {based on the small world structure} we can mimic the efficient of solutions designed to solve traditional small-world problems, with the additional benefit of resilience and fault-tolerance. Furthermore, the routing and address book can be updated based on the neighborhood requirements. The flexibility of our proposed schemes enables a variety of swarm models, and agents. This enables our network to as an underlying networking model that can be applied to file-sharing, streaming, and synchronization of networks.
Interpretable Time-series Classification on Few-shot Samples
Venue: arXiv cs.LG 2020
Year: 2020

Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series classification algorithms fail to tackle the few-shot scenario. Existing few-shot learning methods are proposed to tackle image or text data, and most of them are neural-based models that lack interpretability. This paper proposes an interpretable neural-based framework, namely \textit{Dual Prototypical Shapelet Networks (DPSN)} for few-shot time-series classification, which not only trains a neural network-based model but also interprets the model from dual granularity: 1) global overview using representative time series samples, and 2) local highlights using discriminative shapelets. In particular, the generated dual prototypical shapelets consist of representative samples that can mostly demonstrate the overall shapes of all samples in the class and discriminative partial-length shapelets that can be used to distinguish different classes. We have derived 18 few-shot TSC datasets from public benchmark datasets and evaluated the proposed method by comparing with baselines. The DPSN framework outperforms state-of-the-art time-series classification methods, especially when training with limited amounts of data. Several case studies have been given to demonstrate the interpret ability of our model.
SD-FFR: Software Defined Fast Failure Recovery Mechanism in the Automatic Warehouse
Venue: arXiv cs.NI 2020
Year: 2020

Due to the rapid development of IoT technology, automatic guided vehicles (AGVs) interact with an industrial control system (ICS) through the wireless network to support the freight distribution in the automated warehouse. However, the message exchange among AGVs and the ICS would experience large packet loss or long transmission delay, in the presence of wireless network link failure and link congestion. Therefore, the performance of warehouse automation would be degraded. In this paper, we propose the Software Defined Fast Failure Recovery(SD-FFR) mechanism, aiming to improve network reliability and avoid link congestion caused by load unbalance. With SD-FFR, when link failure occurs, the SDN controller actively detects the link failure within milliseconds, and then reroutes the affected traffic flows accordingly. The proposed SD-FFR mechanism also takes load balance into consideration in selecting routing paths when a new traffic flow joins or link failure occurs in the network. The evaluation results show that network performance can be time efficiently improved.
Hierarchical forecast reconciliation with machine learning
Venue: arXiv cs.LG 2020
Year: 2020

Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down methods, focus on a particular aggregation level to anchor the forecasts. During the past decades, these have been replaced by a variety of linear combination approaches that exploit information from the complete hierarchy to produce more accurate forecasts. However, the performance of these combination methods depends on the particularities of the examined series and their relationships. This paper proposes a novel hierarchical forecasting approach based on machine learning that deals with these limitations in three important ways. First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches. Second, it structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence. Finally, due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns.
Reaping the Informational Surplus in Bayesian Persuasion
Venue: arXiv cs.GT 2020
Year: 2020

The Bayesian persuasion model studies communication between an informed sender and a receiver with a payoff-relevant action, emphasizing the ability of a sender to extract maximal surplus from his informational advantage. In this paper we study a setting with multiple senders, but in which the receiver interacts with only one sender of his choice: senders commit to signals and the receiver then chooses, at the interim stage, with which sender to interact. Our main result is that whenever senders are even slightly uncertain about each other's preferences, the receiver receives all the informational surplus in all equilibria of this game.
The Art of CPU-Pinning: Evaluating and Improving the Performance of Virtualization and Containerization Platforms
Venue: arXiv cs.DC 2020
Year: 2020

Cloud providers offer a variety of execution platforms in form of bare-metal, VM, and containers. However, due to the pros and cons of each execution platform, choosing the appropriate platform for a specific cloud-based application has become a challenge for solution architects. The possibility to combine these platforms (e.g. deploying containers within VMs) offers new capacities that makes the challenge even further complicated. However, there is a little study in the literature on the pros and cons of deploying different application types on various execution platforms. In particular, evaluation of diverse hardware configurations and different CPU provisioning methods, such as CPU pinning, have not been sufficiently studied in the literature. In this work, the performance overhead of container, VM, and bare-metal execution platforms are measured and analyzed for four categories of real-world applications, namely video processing, parallel processing (MPI), web processing, and No-SQL, respectively representing CPU intensive, parallel processing, and two IO intensive processes. Our analyses reveal a set of interesting and sometimes counterintuitive findings that can be used as best practices by the solution architects to efficiently deploy cloud-based applications. Here are some notable mentions: (A) Under specific circumstances, containers can impose a higher overhead than VMs; (B) Containers on top of VMs can mitigate the overhead of VMs for certain applications; (C) Containers with a large number of cores impose a lower overhead than those with a few cores.
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation
Venue: arXiv cs.CV 2020
Year: 2020

We propose a novel objective for penalizing geometric inconsistencies to improve the depth and pose estimation performance of monocular camera images. Our objective is designed using the Wasserstein distance between two point clouds, estimated from images with different camera poses. The Wasserstein distance can impose a soft and symmetric coupling between two point clouds, which suitably maintains geometric constraints and results in a differentiable objective. By adding our objective to the those of other state-of-the-art methods, we can effectively penalize geometric inconsistencies and obtain highly accurate depth and pose estimations. Our proposed method is evaluated using the KITTI dataset.
An ExpTime Upper Bound for $\mathcal{ALC}$ with Integers (Extended Version)
Venue: arXiv cs.AI 2020
Year: 2020

Concrete domains, especially those that allow to compare features with numeric values, have long been recognized as a very desirable extension of description logics (DLs), and significant efforts have been invested into adding them to usual DLs while keeping the complexity of reasoning in check. For expressive DLs and in the presence of general TBoxes, for standard reasoning tasks like consistency, the most general decidability results are for the so-called $ω$-admissible domains, which are required to be dense. Supporting non-dense domains for features that range over integers or natural numbers remained largely open, despite often being singled out as a highly desirable extension. The decidability of some extensions of $\mathcal{ALC}$ with non-dense domains has been shown, but existing results rely on powerful machinery that does not allow to infer any elementary bounds on the complexity of the problem. In this paper, we study an extension of $\mathcal{ALC}$ with a rich integer domain that allows for comparisons (between features, and between features and constants coded in unary), and prove that consistency can be solved using automata-theoretic techniques in single exponential time, and thus has no higher worst-case complexity than standard $\mathcal{ALC}$. Our upper bounds apply to some extensions of DLs with concrete domains known from the literature, support general TBoxes, and allow for comparing values along paths of ordinary (not necessarily functional) roles.
KCN: Knowledge Centric Networking
Venue: arXiv cs.NI 2020
Year: 2020

The advent of multi-domain and multi-requirement digital services requires an underlying network ecosystem able to understand service-specific contexts. In this work, we propose Knowledge Centric Networking (KCN), a paradigm in which knowledge is positioned at the center of the networking landscape. KCN enables approaches by which in-network knowledge generation and distribution can be used to support advanced network control intelligence that is essential to handle complexity and uncertainty in these emerging digital services. In this paper, we introduce the principles of KCN and present an architecture for its realization that enables in-network knowledge creation, distribution, storage and processing, both within a single domain/player and across potentially heterogeneous domains/players.
Being published successfully or getting arXived? The importance of social capital and interdisciplinary collaboration for getting printed in a high impact journal in Physics
Venue: arXiv cs.DL 2020
Year: 2020

The structure of collaboration is known to be of great importance for the success of scientific endeavors. In particular, various types of social capital employed in co-authored work and projects bridging disciplinary boundaries have attracted researchers' interest. Almost all previous studies, however, use samples with an inherent survivor bias, i.e., they focus on papers that have already been published. In contrast, our article examines the chances for getting a working paper published by using a unique dataset of 245,000 papers uploaded to arXiv. ArXiv is a popular preprint platform in Physics which allows us to construct a co-authorship network from which we can derive different types of social capital and interdisciplinary teamwork. To emphasize the 'normal case' of community-specific standards of excellence, we assess publications in Physics' high impact journals as success. Utilizing multilevel event history models, our results reveal that already a moderate number of persistent collaborations spanning at least two years is the most important social antecedent of getting a manuscript published successfully. In contrast, inter- and subdisciplinary collaborations decrease the probability of publishing in an eminent journal in Physics, which can only partially be mitigated by scientists' social capital.