OpenRaiser/Intern-Atlas huggingface dataset:
Robust Interference Management for SISO Systems with Multiple Over-the-Air Computations
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we consider the over-the-air computation of sums. Specifically, we wish to compute $M\geq 2$ sums $s_m=\sum_{k\in\mathcal{D}m}x_k$ over a shared complex-valued MAC at once with minimal mean-squared error ($\mathsf{MSE}$). Finding appropriate Tx-Rx scaling factors balance between a low error in the computation of $s_n$ and the interference induced by it in the computation of other sums $s_m$, $m\neq n$. In this paper, we are interested in designing an optimal Tx-Rx scaling policy that minimizes the mean-squared error $\max_{m\in[1:M]}\mathsf{MSE}_m$ subject to a Tx power constraint with maximum power $P$. We show that an optimal design of the Tx-Rx scaling policy $\left(\bar{\mathbf{a}},\bar{\mathbf{b}}\right)$ involves optimizing (a) their phases and (b) their absolute values in order to (i) decompose the computation of $M$ sums into, respectively, $M_R$ and $M_I$ ($M=M_R+M_I$) calculations over real and imaginary part of the Rx signal and (ii) to minimize the computation over each part -- real and imaginary -- individually. The primary focus of this paper is on (b). We derive conditions (i) on the feasibility of the optimization problem and (ii) on the Tx-Rx scaling policy of a local minimum for $M_w=2$ computations over the real ($w=R$) or the imaginary ($w=I$) part. Extensive simulations over a single Rx chain for $M_w=2$ show that the level of interference in terms of $ΔD=|\mathcal{D}_2|-|\mathcal{D}_1|$ plays an important role on the ergodic worst-case $\mathsf{MSE}$. At very high $\mathsf{SNR}$, typically only the sensor with the weakest channel transmits with full power while all remaining sensors transmit with less to limit the interference. Interestingly, we observe that due to residual interference, the ergodic worst-case $\mathsf{MSE}$ is not vanishing; rather, it converges to $\frac{|\mathcal{D}_1||\mathcal{D}_2|}{K}$ as $\mathsf{SNR}\rightarrow\infty$.
Knowledge Refactoring for Inductive Program Synthesis
Venue: arXiv cs.AI 2020
Year: 2020

Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. We evaluate our approach on two program induction domains: real-world string transformations and building Lego structures. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.
DIET: Lightweight Language Understanding for Dialogue Systems
Venue: arXiv cs.CL 2020
Year: 2020

Large-scale pre-trained language models have shown impressive results on language understanding benchmarks like GLUE and SuperGLUE, improving considerably over other pre-training methods like distributed representations (GloVe) and purely supervised approaches. We introduce the Dual Intent and Entity Transformer (DIET) architecture, and study the effectiveness of different pre-trained representations on intent and entity prediction, two common dialogue language understanding tasks. DIET advances the state of the art on a complex multi-domain NLU dataset and achieves similarly high performance on other simpler datasets. Surprisingly, we show that there is no clear benefit to using large pre-trained models for this task, and in fact DIET improves upon the current state of the art even in a purely supervised setup without any pre-trained embeddings. Our best performing model outperforms fine-tuning BERT and is about six times faster to train.
Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels
Venue: arXiv cs.CL 2020
Year: 2020

Static knowledge graph has been incorporated extensively into sequence-to-sequence framework for text generation. While effectively representing structured context, static knowledge graph failed to represent knowledge evolution, which is required in modeling dynamic events. In this paper, an automatic commenting task is proposed for long novels, which involves understanding context of more than tens of thousands of words. To model the dynamic storyline, especially the transitions of the characters and their relations, Evolutionary Knowledge Graph(EKG) is proposed and learned within a multi-task framework. Given a specific passage to comment, sequential modeling is used to incorporate historical and future embedding for context representation. Further, a graph-to-sequence model is designed to utilize the EKG for comment generation. Extensive experimental results show that our EKG-based method is superior to several strong baselines on both automatic and human evaluations.
A review on mathematical strength and analysis of Enigma
Venue: arXiv cs.CR 2020
Year: 2020

In this review article, we discussed the Mathematics and mechanics behind the Enigma machine with an analysis of security strength. The German army used the Enigma machine during the second world war to encrypt communications. Due to its complexity, the encryption done by the Enigma Machine was assumed to be almost unbreakable. However, the Polish believed that people with good background and deep knowledge of science and mathematics would have a better chance to break the encryption done by Enigma. They appointed twenty mathematicians from Poznan University to work on this problem at the Polish Cipher Bureau. Three of those, Marian Rejewski, Jerzy Rozycki and Henryk Zygalski were able to exploit certain flaws in the encryption, and by using permutation group theory finally managed to decipher the Enigma messages. The mathematics discovered by them is presented here.
Secure and Energy-Efficient Key-Agreement Protocol for Multi-Server Architecture
Venue: arXiv cs.CR 2020
Year: 2020

Authentication schemes are practised globally to verify the legitimacy of users and servers for the exchange of data in different facilities. Generally, the server verifies a user to provide resources for different purposes. But due to the large network system, the authentication process has become complex and therefore, time-to-time different authentication protocols have been proposed for the multi-server architecture. However, most of the protocols are vulnerable to various security attacks and their performance is not efficient. In this paper, we propose a secure and energy-efficient remote user authentication protocol for multi-server systems. The results show that the proposed protocol is comparatively ~44% more efficient and needs ~38% less communication cost. We also demonstrate that with only two-factor authentication, the proposed protocol is more secure from the earlier related authentication schemes.
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition
Venue: arXiv cs.LG 2020
Year: 2020

We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$. We propose a model-free algorithm UCB-Advantage and prove that it achieves $\tilde{O}(\sqrt{H^2SAT})$ regret where $T = KH$ and $K$ is the number of episodes to play. Our regret bound improves upon the results of [Jin et al., 2018] and matches the best known model-based algorithms as well as the information theoretic lower bound up to logarithmic factors. We also show that UCB-Advantage achieves low local switching cost and applies to concurrent reinforcement learning, improving upon the recent results of [Bai et al., 2019].
Exploring Distributed Control with the NK Model
Venue: arXiv cs.NE 2020
Year: 2020

The NK model has been used widely to explore aspects of natural evolution and complex systems. This paper introduces a modified form of the NK model for exploring distributed control in complex systems such as organisations, social networks, collective robotics, etc. Initial results show how varying the size and underlying functional structure of a given system affects the performance of different distributed control structures and decision making, including within dynamically formed structures and those with differing numbers of control nodes.
Frequency-Weighted Robust Tensor Principal Component Analysis
Venue: arXiv cs.CV 2020
Year: 2020

Robust tensor principal component analysis (RTPCA) can separate the low-rank component and sparse component from multidimensional data, which has been used successfully in several image applications. Its performance varies with different kinds of tensor decompositions, and the tensor singular value decomposition (t-SVD) is a popularly selected one. The standard t-SVD takes the discrete Fourier transform to exploit the residual in the 3rd mode in the decomposition. When minimizing the tensor nuclear norm related to t-SVD, all the frontal slices in frequency domain are optimized equally. In this paper, we incorporate frequency component analysis into t-SVD to enhance the RTPCA performance. Specially, different frequency bands are unequally weighted with respect to the corresponding physical meanings, and the frequency-weighted tensor nuclear norm can be obtained. Accordingly we rigorously deduce the frequency-weighted tensor singular value threshold operator, and apply it for low rank approximation subproblem in RTPCA. The newly obtained frequency-weighted RTPCA can be solved by alternating direction method of multipliers, and it is the first time that frequency analysis is taken in tensor principal component analysis. Numerical experiments on synthetic 3D data, color image denoising and background modeling verify that the proposed method outperforms the state-of-the-art algorithms both in accuracy and computational complexity.
Fog-Based Detection for Random-Access IoT Networks with Per-Measurement Preambles
Venue: arXiv cs.IT 2020
Year: 2020

Internet of Things (IoT) systems may be deployed to monitor spatially distributed quantities of interests (QoIs), such as noise or pollution levels. This paper considers a fog-based IoT network, in which active IoT devices transmit measurements of the monitored QoIs to the local edge node (EN), while the ENs are connected to a cloud processor via limited-capacity fronthaul links. While the conventional approach uses preambles as metadata for reserving communication resources, here we consider assigning preambles directly to measurement levels across all devices. The resulting Type-Based Multiple Access (TBMA) protocol enables the efficient remote detection of the QoIs, rather than of the individual payloads. The performance of both edge and cloud-based detection or hypothesis testing is evaluated in terms of error exponents. Cloud-based hypothesis testing is shown theoretically and via numerical results to be advantageous when the inter-cell interference power and the fronthaul capacity are sufficiently large.
A Sub-linear Time Framework for Geometric Optimization with Outliers in High Dimensions
Venue: arXiv cs.CG 2020
Year: 2020

Many real-world problems can be formulated as geometric optimization problems in high dimensions, especially in the fields of machine learning and data mining. Moreover, we often need to take into account of outliers when optimizing the objective functions. However, the presence of outliers could make the problems to be much more challenging than their vanilla versions. In this paper, we study the fundamental minimum enclosing ball (MEB) with outliers problem first; partly inspired by the core-set method from Bădoiu and Clarkson, we propose a sub-linear time bi-criteria approximation algorithm based on two novel techniques, the Uniform-Adaptive Sampling method and Sandwich Lemma. To the best of our knowledge, our result is the first sub-linear time algorithm, which has the sample size ({\em i.e.,} the number of sampled points) independent of both the number of input points $n$ and dimensionality $d$, for MEB with outliers in high dimensions. Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, $k$-center clustering, and SVM with outliers, and therefore achieve the sub-linear time algorithms for these problems respectively.
Achieving Network Slice Communication Service Distribution across 5G Micro-Operator Multi-tenants
Venue: arXiv cs.NI 2020
Year: 2020

Network slicing is the means of logically isolating network capabilities from a one size fits all to a set of different slices where each slice is responsible for specific network requirements. In the same light, the micro-operator concept is targeted at local deployment of 5G for vertical specific services. When a network slice instance (NSI) is created across the access and core networks, it is important to understand how the communication service from the created slice instance will be distributed to different tenants and their end-users. In this vein, we propose a network slice communication service distribution technique for local 5G micro-operator deployment scenarios. This is achieved by expanding or leveraging the communication service management function (CSMF) defined by 3GPP into a multi-tenant manager and a communication services orchestrator. We further describe how the communication service orchestrator will address all the possible multitenant-slice situations that can exist during the distribution of a network slice instance to multiple tenants. The generic technique introduced in this paper will proffer a researchable solution for not only network slice communication service distribution across different micro-operator tenants but pave the way to support future use cases especially when the allocated slice is responsible for multiple tenants or when a tenant requests multiple NSIs
In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19
Venue: arXiv cs.SI 2020
Year: 2020

During the COVID-19 pandemic, "Chinese Virus" emerged as a controversial term for coronavirus. To some, it may seem like a neutral term referring to the physical origin of the virus. To many others, however, the term is in fact attaching ethnicity to the virus. While both arguments appear reasonable, quantitative analysis of the term's real-world usage is lacking to shed light on the issues behind the controversy. In this paper, we attempt to fill this gap. To model the substantive difference of tweets with controversial terms and those with non-controversial terms, we apply topic modeling and LIWC-based sentiment analysis. To test whether "Chinese Virus" and "COVID-19" are interchangeable, we formulate it as a classification task, mask out these terms, and classify them using the state-of-the-art transformer models. Our experiments consistently show that the term "Chinese Virus" is associated with different substantive topics and sentiment compared with "COVID-19" and that the two terms are easily distinguishable by looking at their context.
Digital tools against COVID-19: Framing the ethical challenges and how to address them
Venue: arXiv cs.CY 2020
Year: 2020

Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilizing digital tools for public health. To help scientists and policymakers navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications currently in use. Namely: proximity and contact tracing, symptom monitoring, quarantine control, and flow modeling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, in recognition of the need for practical guidance, we propose a navigation aid for policymakers made up of ten steps for the ethical use of digital public health tools.
ParaCNN: Visual Paragraph Generation via Adversarial Twin Contextual CNNs
Venue: arXiv cs.CV 2020
Year: 2020

Image description generation plays an important role in many real-world applications, such as image retrieval, automatic navigation, and disabled people support. A well-developed task of image description generation is image captioning, which usually generates a short captioning sentence and thus neglects many of fine-grained properties, e.g., the information of subtle objects and their relationships. In this paper, we study the visual paragraph generation, which can describe the image with a long paragraph containing rich details. Previous research often generates the paragraph via a hierarchical Recurrent Neural Network (RNN)-like model, which has complex memorising, forgetting and coupling mechanism. Instead, we propose a novel pure CNN model, ParaCNN, to generate visual paragraph using hierarchical CNN architecture with contextual information between sentences within one paragraph. The ParaCNN can generate an arbitrary length of a paragraph, which is more applicable in many real-world applications. Furthermore, to enable the ParaCNN to model paragraph comprehensively, we also propose an adversarial twin net training scheme. During training, we force the forwarding network's hidden features to be close to that of the backwards network by using adversarial training. During testing, we only use the forwarding network, which already includes the knowledge of the backwards network, to generate a paragraph. We conduct extensive experiments on the Stanford Visual Paragraph dataset and achieve state-of-the-art performance.
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
Venue: arXiv cs.RO 2020
Year: 2020

We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space. Using the CARLA simulator, we develop a parking lot environment and collect a dataset of human parking maneuvers. We then study the impact of model complexity and feature information by comparing a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment improves long term predictions.
Bit Commitment for Lottery and Auction on Quantum Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

This paper propose a protocol for lottery and a protocol for auction on quantum Blockchain. Our protocol of lottery satisfies randomness, unpredictability, unforgeability, verifiability, decentralization and unconditional security. Our protocol of auction satisfies bid privacy, posterior privacy, bids' binding, decentralization and unconditional security. Except quantum Block-chain, the main technique involved in both protocols is quantum bit commitment. Since both quantum blockchain and quantum bit commitment can be realized by the current technology, our protocols are practically feasible.
A fully distributed motion coordination strategy for multi-robot systems with local information
Venue: arXiv cs.RO 2020
Year: 2020

This paper investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace. Based on the realistic assumptions that each robot is subject to both velocity and input constraints and can have only local view and local information, a fully distributed multi-robot motion coordination strategy is proposed. Building on top of a cell decomposition, a conflict detection algorithm is presented first. Then, a rule is proposed to assign dynamically a planning order to each pair of neighboring robots, which is deadlock-free. Finally, a two-step motion planning process that combines fixed-path planning and trajectory planning is designed. The effectiveness of the resulting solution is verified by a simulation example.
Evolving Dyadic Strategies for a Cooperative Physical Task
Venue: arXiv cs.NE 2020
Year: 2020

Many cooperative physical tasks require that individuals play specialized roles (e.g., leader-follower). Humans are adept cooperators, negotiating these roles and transitions between roles innately. Yet how roles are delegated and reassigned is not well understood. Using a genetic algorithm, we evolve simulated agents to explore a space of feasible role-switching policies. Applying these switching policies in a cooperative manual task, agents process visual and haptic cues to decide when to switch roles. We then analyze the evolved virtual population for attributes typically associated with cooperation: load sharing and temporal coordination. We find that the best performing dyads exhibit high temporal coordination (anti-synchrony). And in turn, anti-synchrony is correlated to symmetry between the parameters of the cooperative agents. These simulations furnish hypotheses as to how human cooperators might mediate roles in dyadic tasks.
On the Potential of Social Media Data in Urban Planning: Findings from the Beer Street in Curitiba, Brazil
Venue: arXiv cs.SI 2020
Year: 2020

Social media makes available vast amounts of data for various types of analyses. Cities have the opportunity to explore this new data source to study urban dynamics and complement traditional data used for urban planning. We investigate Untappd social media data in the context of urban planning in Curitiba, Brazil. We analyze the project to create a Craft Beer Street, recently announced by the municipality to promote local beers in Curitiba, in order to study the potential of exploring social media data to support the planning of this project. Our results indicate that social media data could have helped to guide the decision of the Beer Street creation and can potentially become a strategic urban planning tool.
Problems and Solutions of Service Architecture in Small and Medium Enterprise Communities
Venue: arXiv cs.SE 2020
Year: 2020

Lack of resources is a challenge for small and medium enterprises (SMEs) in implementing an IT-based system to facilitate more efficient business decisions and expanding the market. A community system based on service-oriented architecture (SOA) can help SMEs alleviate this problem. This paper explores and analyses the frameworks proposed by previous studies in the context of inter-enterprise SOA for SMEs. Several problems being the background of the system implementation are identified. Afterward, the offered solutions are presented, including the system architecture, technology adoption, specific elements, and collaboration model. The study also discusses the system architecture patterns of the reviewed studies as well as the collaboration organizational structures.