OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Stable Communities in Link Streams at Multiple Temporal Scales
Venue: arXiv cs.SI 2019
Year: 2019

Link streams model interactions over time in a wide range of fields. Under this model, the challenge is to mine efficiently both temporal and topological structures. Community detection and change point detection are one of the most powerful tools to analyze such evolving interactions. In this paper, we build on both to detect stable community structures by identifying change points within meaningful communities. Unlike existing dynamic community detection algorithms, the proposed method is able to discover stable communities efficiently at multiple temporal scales. We test the effectiveness of our method on synthetic networks, and on high-resolution time-varying networks of contacts drawn from real social networks.
The stable marriage problem with ties and restricted edges
Venue: arXiv cs.DM 2019
Year: 2019

In the stable marriage problem, a set of men and a set of women are given, each of whom has a strictly ordered preference list over the acceptable agents in the opposite class. A matching is called stable if it is not blocked by any pair of agents, who mutually prefer each other to their respective partner. Ties in the preferences allow for three different definitions for a stable matching: weak, strong and super-stability. Besides this, acceptable pairs in the instance can be restricted in their ability of blocking a matching or being part of it, which again generates three categories of restrictions on acceptable pairs. Forced pairs must be in a stable matching, forbidden pairs must not appear in it, and lastly, free pairs cannot block any matching. Our computational complexity study targets the existence of a stable solution for each of the three stability definitions, in the presence of each of the three types of restricted pairs. We solve all cases that were still open. As a byproduct, we also derive that the maximum size weakly stable matching problem is hard even in very dense graphs, which may be of independent interest.
Switchable Normalization for Learning-to-Normalize Deep Representation
Venue: arXiv cs.CV 2019
Year: 2019

We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks. Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g. 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, MegaFace, and Kinetics. Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer? (b) What impacts the choices of normalizers? (c) Do different tasks and datasets prefer different normalizers? We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN has been released at https://github.com/switchablenorms.
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts
Venue: arXiv cs.AI 2019
Year: 2019

This paper tackles the problem of learning a questioner in the goal-oriented visual dialog task. Several previous works adopt model-free reinforcement learning. Most pretrain the model from a finite set of human-generated data. We argue that using limited demonstrations to kick-start the questioner is insufficient due to the large policy search space. Inspired by a recently proposed information theoretic approach, we develop two analytic experts to serve as a source of high-quality demonstrations for imitation learning. We then take advantage of reinforcement learning to refine the model towards the goal-oriented objective. Experimental results on the GuessWhat?! dataset show that our method has the combined merits of imitation and reinforcement learning, achieving the state-of-the-art performance.
Synchronizing Geospatial Information for Personalized Health Monitoring
Venue: arXiv cs.HC 2019
Year: 2019

The health effects of air pollution have been subject to intense study in recent decades. Exposure to pollutants such as airborne particulate matter and ozone has been associated with increases in morbidity and mortality, especially with regards to respiratory and cardiovascular diseases. Unfortunately, individuals do not have readily accessible methods by which to track their exposure to pollution. This paper proposes how pollution parameters like CO, NO2, O3, PM2.5, PM10 and SO2 can be monitored for respiratory and cardiovascular personalized health during outdoor exercise events. Using location tracked activities, we synchronize them to public data sets of pollution sensors. For improved accuracy in estimation, we use heart rate data to understand breathing volume mapped with the local air quality sensors via constant GPS tracking.
Semi Automatic Construction of ShEx and SHACL Schemas
Venue: arXiv cs.DB 2019
Year: 2019

We present a method for the construction of SHACL or ShEx constraints for an existing RDF dataset. It has two components that are used conjointly: an algorithm for automatic schema construction, and an interactive workflow for editing the schema. The schema construction algorithm takes as input sets of sample nodes and constructs a shape constraint for every sample set. It can be parametrized by a schema pattern that defines structural requirements for the schema to be constructed. Schema patterns are also used to feed the algorithm with relevant information about the dataset coming from a domain expert or from some ontology. The interactive workflow provides useful information about the dataset, shows validation results w.r.t. the schema under construction, and offers schema editing operations that combined with the schema construction algorithm allow to build a complex ShEx or SHACL schema.
On the Feasibility of Infrastructure Assistance to Autonomous UAV Systems
Venue: arXiv cs.NI 2019
Year: 2019

Infrastructure assistance has been proposed as a viable solution to improve the capabilities of commercial Unmanned Aerial Vehicles (UAV), especially toward fully autonomous operations. The airborne nature of these devices imposes constrains limiting the onboard available energy supply and computing power. The assistance of the surrounding communication and computing infrastructure can mitigate such limitations by extending the communication range and taking over the execution of compute-intense tasks. However, autonomous operations impose specific, and rather extreme in some cases, demands to the infrastructure. Focusing on flight assistance and task offloading to edge servers, this paper presents an in-depth evaluation of the ability of the communication infrastructure to support the necessary flow of information from the UAV to the infrastructure. The study is based on our recently proposed FlyNetSim, an open-source UAV-network simulator accurately modeling both UAV and network operations.
Uncalibrated Deflectometry with a Mobile Device on Extended Specular Surfaces
Venue: arXiv cs.CV 2019
Year: 2019

We introduce a system and methods for the three-dimensional measurement of extended specular surfaces with high surface normal variations. Our system consists only of a mobile hand held device and exploits screen and front camera for Deflectometry-based surface measurements. We demonstrate high quality measurements without the need for an offline calibration procedure. In addition, we develop a multi-view technique to compensate for the small screen of a mobile device so that large surfaces can be densely reconstructed in their entirety. This work is a first step towards developing a self-calibrating Deflectometry procedure capable of taking 3D surface measurements of specular objects in the wild and accessible to users with little to no technical imaging experience.
Private Proximity Retrieval Codes
Venue: arXiv cs.IT 2019
Year: 2019

A \emph{private proximity retrieval} (\emph{PPR}) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance $r$ from the user's record $x$. The user's \emph{privacy} at each server is given by the fraction of the record $x$ that is kept private. In this paper, this research is initiated and protocols that offer trade-offs between privacy and computational complexity and storage are studied. In particular, we assume that each server stores a copy of the database and study the required minimum number of servers by our protocol which provides a given privacy level. Each server gets a query in the protocol and the set of queries forms a code. We study the family of codes generated by the set of queries and in particular the minimum number of codewords in such a code which is the minimum number of servers required for the protocol. These codes are closely related to a family of codes known as \emph{covering designs}. We introduce several lower bounds on the sizes of such codes as well as several constructions. This work focuses on the case when the records are binary vectors together with the Hamming distance. Other metrics such as the Johnson metric are also investigated.
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
Venue: arXiv cs.HC 2019
Year: 2019

Automation of tasks can have critical consequences when humans lose agency over decision processes. Deep learning models are particularly susceptible since current black-box approaches lack explainable reasoning. We argue that both the visual interface and model structure of deep learning systems need to take into account interaction design. We propose a framework of collaborative semantic inference (CSI) for the co-design of interactions and models to enable visual collaboration between humans and algorithms. The approach exposes the intermediate reasoning process of models which allows semantic interactions with the visual metaphors of a problem, which means that a user can both understand and control parts of the model reasoning process. We demonstrate the feasibility of CSI with a co-designed case study of a document summarization system.
Machine learning approach to remove ion interference effect in agricultural nutrient solutions
Venue: arXiv cs.LG 2019
Year: 2019

High concentration agricultural facilities such as vertical farms or plant factories consider hydroponic techniques as optimal solutions. Although closed-system dramatically reduces water consumption and pollution issues, it has ion-ratio related problem. As the root absorbs individual ions with different rate, ion rate in a nutrient solution should be adjusted periodically. But traditional method only considers pH and electrical conductivity to adjust the nutrient solution, leading to ion imbalance and accumulation of excessive salts. To avoid those problems, some researchers have proposed ion-balancing methods which measure and control each ion concentration. However, those approaches do not overcome the innate limitations of ISEs, especially ion interference effect. An anion sensor is affected by other anions, and the error grows larger in higher concentration solution. A machine learning approach to modify ISE data distorted by ion interference effect is proposed in this paper. As measurement of TDS value is relatively robust than any other signals, we applied TDS as key parameter to build a readjustment function to remove the artifact. Once a readjustment model is established, application on ISE data can be done in real time. Readjusted data with proposed model showed about 91.6 ~ 98.3% accuracies. This method will enable the fields to apply recent methods in feasible status.
A Self-Stabilizing Minimal k-Grouping Algorithm
Venue: arXiv cs.DC 2019
Year: 2019

We consider the minimal k-grouping problem: given a graph G=(V,E) and a constant k, partition G into subgraphs of diameter no greater than k, such that the union of any two subgraphs has diameter greater than k. We give a silent self-stabilizing asynchronous distributed algorithm for this problem in the composite atomicity model of computation, assuming the network has unique process identifiers. Our algorithm works under the weakly-fair daemon. The time complexity (i.e., the number of rounds to reach a legitimate configuration) of our algorithm is O(nD/k) where n is the number of processes in the network and \diam is the diameter of the network. The space complexity of each process is O((n +n_{false})log n) where n_{false} is the number of false identifiers, i.e., identifiers that do not match the identifier of any process, but which are stored in the local memory of at least one process at the initial configuration. Our algorithm guarantees that the number of groups is at most $2n/k+1$ after convergence. We also give a novel composition technique to concatenate a silent algorithm repeatedly, which we call loop composition.
Performance Comparison of Quasi-Delay-Insensitive Asynchronous Adders
Venue: arXiv cs.AR 2019
Year: 2019

In this technical note, we provide a comparison of the design metrics of various quasi-delay-insensitive (QDI) asynchronous adders, where the adders correspond to diverse architectures. QDI adders are robust, and the objective of this technical note is to point to those QDI adders which are suitable for low power/energy and less area. This information could be valuable for a resource-constrained low power VLSI design scenario. Non-QDI adders are excluded from the comparison since they are not robust although they may have optimized design metrics. All the QDI adders were realized using a 32/28nm CMOS process.
A Compact Light Field Camera for Real-Time Depth Estimation
Venue: arXiv cs.CV 2019
Year: 2019

Depth cameras are utilized in many applications. Recently light field approaches are increasingly being used for depth computation. While these approaches demonstrate the technical feasibility, they can not be brought into real-world application, since they have both a high computation time as well as a large design. Exactly these two drawbacks are overcome in this paper. For the first time, we present a depth camera based on the light field principle, which provides real-time depth information as well as a compact design.
Towards an Holistic Definition of Requirements Debt
Venue: arXiv cs.SE 2019
Year: 2019

When not appropriately managed, technical debt is considered to have negative effects on the long term success of a software project. However, how the debt metaphor applies to requirements engineering in general, and to requirements engineering activities in particular, is not well understood. Grounded in the existing literature, we present a holistic definition of requirements debt which include debt incurred during the identification, formalization, and implementation of requirements. We outline future assessment to validate and further refine our proposed definition. This conceptualization is the first step towards a requirements debt monitoring framework to support stakeholders decisions, such as when to incur and eventually pay back requirements debt, and at what costs
Fast Deterministic Constructions of Linear-Size Spanners and Skeletons
Venue: arXiv cs.DC 2019
Year: 2019

In the distributed setting, the only existing constructions of \textit{sparse skeletons}, (i.e., subgraphs with $O(n)$ edges) either use randomization or large messages, or require $Ω(D)$ time, where $D$ is the hop-diameter of the input graph $G$. We devise the first deterministic distributed algorithm in the CONGEST model (i.e., uses small messages) for constructing linear-size skeletons in time $2^{O(\sqrt{{\log n}\cdot{\log{\log n}}})}$. We can also compute a linear-size spanner with stretch $polylog(n)$ in low deterministic polynomial time, i.e., $O(n^ρ)$ for an arbitrarily small constant $ρ>0$, in the CONGEST model. Yet another algorithm that we devise runs in $O({\log n})^{κ-1}$ time, for a parameter $κ=1,2,\dots,$ and constructs an $O({\log n})^{κ-1}$ spanner with $O(n^{1+1/κ})$ edges. All our distributed algorithms are lightweight from the computational perspective, i.e., none of them employs any heavy computations.
Modelling Dynamic Interactions between Relevance Dimensions
Venue: arXiv cs.IR 2019
Year: 2019

Relevance is an underlying concept in the field of Information Science and Retrieval. It is a cognitive notion consisting of several different criteria or dimensions. Theoretical models of relevance allude to interdependence between these dimensions, where their interaction and fusion leads to the final inference of relevance. We study the interaction between the relevance dimensions using the mathematical framework of Quantum Theory. It is considered a generalised framework to model decision making under uncertainty, involving multiple perspectives and influenced by context. Specifically, we conduct a user study by constructing the cognitive analogue of a famous experiment in Quantum Physics. The data is used to construct a complex-valued vector space model of the user's cognitive state, which is used to explain incompatibility and interference between relevance dimensions. The implications of our findings to inform the design of Information Retrieval systems are also discussed.
Logical reduction of metarules
Venue: arXiv cs.LG 2019
Year: 2019

Many forms of inductive logic programming (ILP) use \emph{metarules}, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called \emph{derivation reduction}, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperforms subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.
Overview of Guidance, Navigation and Control System of the TeamIndus lunar lander
Venue: arXiv cs.RO 2019
Year: 2019

TeamIndus' lunar logistics vision includes multiple lunar missions to meet requirements of science, commercial and efforts towards global exploration. The first mission is slated for launch in 2020. The prime objective is to demonstrate autonomous precision lunar landing, and Surface Exploration Rover to collect data on the vicinity of the landing site. TeamIndus has developed various technologies towards lowering the access barrier to the lunar surface. This paper shall provide an overview of design of lander GNC system. The design of the GNC system has been described after concluding studies on sensor and actuator configurations. Frugal design approach is followed in the selection of GNC hardware. The paper describes the constraints for the orbital maneuvers and the lunar descent strategy. Various aspects of the GNC design of autonomous lunar descent maneuver: timeline of events, guidance, inertial and optical terrain-relative navigation schemes are described. The GNC software description focuses on system architecture, modes of operation, and core elements of the GNC software. The GNC algorithms have been tested using Monte-Carlo simulations and Processor-in-Loop runs. The paper concludes with a summary of key risk-mitigation studies for soft landing.
IDEAL-CITIES: A Trustworthy and Sustainable Framework for Circular Smart Cities
Venue: arXiv cs.HC 2019
Year: 2019

Reflecting upon the sustainability challenges cities will be facing in the near future and the recent technological developments allowing cities to become "smart", we introduce IDEAL-CITIES; a framework aiming to provide an architecture for cyber-physical systems to deliver a data-driven Circular Economy model in a city context. In the IDEAL-CITIES ecosystem, the city's finite resources as well as citizens will form the pool of intelligent assets in order to contribute to high utilization through crowdsourcing and real-time decision making and planning. We describe two use cases as a vehicle to demonstrate how a smart city can serve the Circular Economy paradigm.
Model Checking Data Flows in Concurrent Network Updates (Full Version)
Venue: arXiv cs.LO 2019
Year: 2019

We present a model checking approach for the verification of data flow correctness in networks during concurrent updates of the network configuration. This verification problem is of great importance for software-defined networking (SDN), where errors can lead to packet loss, black holes, and security violations. Our approach is based on a specification of temporal properties of individual data flows, such as the requirement that the flow is free of cycles. We check whether these properties are simultaneously satisfied for all active data flows while the network configuration is updated. To represent the behavior of the concurrent network controllers and the resulting evolutions of the configurations, we introduce an extension of Petri nets with a transit relation, which characterizes the data flow caused by each transition of the Petri net. For safe Petri nets with transits, we reduce the verification of temporal flow properties to a circuit model checking problem that can be solved with effective verification techniques like IC3, interpolation, and bounded model checking. We report on encouraging experiments with a prototype implementation based on the hardware model checker ABC.
DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks
Venue: arXiv cs.CL 2019
Year: 2019

Variants dropout methods have been designed for the fully-connected layer, convolutional layer and recurrent layer in neural networks, and shown to be effective to avoid overfitting. As an appealing alternative to recurrent and convolutional layers, the fully-connected self-attention layer surprisingly lacks a specific dropout method. This paper explores the possibility of regularizing the attention weights in Transformers to prevent different contextualized feature vectors from co-adaption. Experiments on a wide range of tasks show that DropAttention can improve performance and reduce overfitting.