OpenRaiser/Intern-Atlas huggingface dataset:
Urban flows prediction from spatial-temporal data using machine learning: A survey
Venue: arXiv cs.LG 2019
Year: 2019

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics
Venue: arXiv cs.LG 2019
Year: 2019

We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns. Reconstructing and forecasting the evolution of hormonal dynamics is a challenging task, but a critical one to improve general understanding of the menstrual cycle and personalized detection of potential health issues. Our goal is to infer and forecast individual hormone daily levels over time, while accommodating pragmatic and minimally invasive measurement settings. To that end, our approach combines the power of probabilistic generative models (i.e., multi-task Gaussian processes) with the flexibility of neural networks (i.e., a dilated convolutional architecture) to learn complex temporal mappings. To attain accurate hormone level reconstruction with as little data as possible, we propose a sampling mechanism for optimal reconstruction accuracy with limited sampling budget. Our results show the validity of our proposed hormonal dynamic modeling framework, as it provides accurate predictive performance across different realistic sampling budgets and outperforms baselines methods.
DTN7: An Open-Source Disruption-tolerant Networking Implementation of Bundle Protocol 7
Venue: arXiv cs.NI 2019
Year: 2019

In disruption-tolerant networking (DTN), data is transmitted in a store-carry-forward fashion from network node to network node. In this paper, we present an open source DTN implementation, called DTN7, of the recently released Bundle Protocol Version 7 (draft version 13). DTN7 is written in Go and provides features like memory safety and concurrent execution. With its modular design and interchangeable components, DTN7 facilitates DTN research and application development. Furthermore, we present results of a comparative experimental evaluation of DTN7 and other DTN systems including Serval, IBR-DTN, and Forban. Our results indicate that DTN7 is a flexible and efficient open-source multi-platform implementation of the most recent Bundle Protocol Version 7.
TxForest: A DSL for Concurrent Filestores
Venue: arXiv cs.PL 2019
Year: 2019

Many systems use ad hoc collections of files and directories to store persistent data. For consumers of this data, the process of properly parsing, using, and updating these filestores using conventional APIs is cumbersome and error-prone. Making matters worse, most filestores are too big to fit in memory, so applications must process the data incrementally while managing concurrent accesses by multiple users. This paper presents Transactional Forest (TxForest), which builds on earlier work on Forest to provide a simpler, more powerful API for managing filestores, including a mechanism for managing concurrent accesses using serializable transactions. Under the hood, TxForest implements an optimistic concurrency control scheme using Huet's zippers to track the data associated with filestores. We formalize TxForest in a core calculus, develop a proof of serializability, and describe our OCaml prototype, which we have used to build several practical applications.
Authentication and Hand-Over Algorithms for IoT Group
Venue: arXiv cs.CR 2019
Year: 2019

Current advancements in mobility of devices and also Internet of Things (IoT) have replaced the central networks by distributed infrastructure. The more a network is distributed, the more the security of infrastructure and the communication is getting complex. The members in a distributed network create different groups according to their coverage area or their requirements. Mobility nature of the members brings a problem called hand-over of members between groups. Current authentication methods are not applicable due to the lack of resources in the devices.A lightweight authentication method and an easy and fast hand-over process are the current need for the distributed networks. Shamir Secret Sharing algorithm is used for the authentication process in the studies before, but still secure group authentication algorithm and hand-over process are challenges in the group authentication. In this study, a new method is proposed to provide a secure group authentication and hand-over process between groups based on Lagrange's Interpolation.
Artificial Intelligence Approaches
Venue: arXiv cs.AI 2019
Year: 2019

Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions.
Successive Cancellation List Decoding of Product Codes with Reed-Muller Component Codes
Venue: arXiv cs.IT 2019
Year: 2019

This letter proposes successive cancellation list (SCL) decoding of product codes with Reed--Muller (RM) component codes. SCL decoding relies on a product code description based on the $2\times 2$ Hadamard kernel, which enables interpreting the code as an RM subcode. The focus is on a class of product codes considered in wireless communication systems, based on single parity-check and extended Hamming component codes. For short product codes, it is shown that SCL decoding with a moderate list size performs as well as (and, sometimes, outperforms) belief propagation (BP) decoding. Furthermore, by concatenating a short product code with a high-rate outer code, SCL decoding outperforms BP decoding by up to $1.4$ dB.
A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots
Venue: arXiv cs.AI 2019
Year: 2019

The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games---and use the game of `Noughts & Crosses' with two variants as a case study. Its minimum requirements for learning to perceive and interact are based on a few hundred example images, a few example multimodal dialogues and physical demonstrations of robot manipulation, and automatic simulations. In addition, we propose novel algorithms for robust visual game tracking and for competitive policy learning with high winning rates, which substantially outperform DQN-based baselines. While an automatic evaluation shows evidence that the proposed approach can be easily extended to new games with competitive robot behaviours, a human evaluation with 130 humans playing with the Pepper robot confirms that highly accurate visual perception is required for successful game play.
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
Venue: arXiv cs.LG 2019
Year: 2019

The architecture of Transformer is based entirely on self-attention, and has been shown to outperform models that employ recurrence on sequence transduction tasks such as machine translation. The superior performance of Transformer has been attributed to propagating signals over shorter distances, between positions in the input and the output, compared to the recurrent architectures. We establish connections between the dynamics in Transformer and recurrent networks to argue that several factors including gradient flow along an ensemble of multiple weakly dependent paths play a paramount role in the success of Transformer. We then leverage the dynamics to introduce {\em Multiresolution Transformer Networks} as the first architecture that exploits hierarchical structure in data via self-attention. Our models significantly outperform state-of-the-art recurrent and hierarchical recurrent models on two real-world datasets for query suggestion, namely, \aol and \amazon. In particular, on AOL data, our model registers at least 20\% improvement on each precision score, and over 25\% improvement on the BLEU score with respect to the best performing recurrent model. We thus provide strong evidence that recurrence is not essential for modeling hierarchical structure.
Interval-Permutation Segment Graphs
Venue: arXiv cs.DM 2019
Year: 2019

In this work, we introduce the \emph{interval permutation segment (IP-SEG)} model that naturally generalizes the geometric intersection models of interval and permutation graphs. We study properties of two graph classes that arise from the IP-SEG model and present a family of forbidden subgraphs for these classes. In addition, we present polynomial algorithms for the clique and independent set problems on these classes, when the model is given as part of the input.
On the (In)security of Bluetooth Low Energy One-Way Secure Connections Only Mode
Venue: arXiv cs.CR 2019
Year: 2019

To defeat security threats such as man-in-the-middle (MITM) attacks, Bluetooth Low Energy (BLE) 4.2 and 5.x introduce the Secure Connections Only mode, under which a BLE device accepts only secure paring protocols including Passkey Entry and Numeric Comparison from an initiator, e.g., an Android mobile. However, the BLE specification does not explicitly require the Secure Connection Only mode of the initiator. Taking the Android's BLE programming framework for example, we found that it cannot enforce secure pairing, invalidating the security protection provided by the Secure Connection Only mode. The same problem applies to Apple iOS too. Specifically, we examine the life cycle of a BLE pairing process in Android and identify four severe design flaws. These design flaws can be exploited by attackers to perform downgrading attacks, forcing the BLE pairing protocols to run in the insecure mode without the users' awareness. To validate our findings, we selected and tested 18 popular BLE commercial products and our experimental results proved that downgrading attacks and MITM attacks were all possible to these products. All 3501 BLE apps from Androzoo are also subject to these attacks. For defense, we have designed and implemented a prototype of the Secure Connection Only mode on Android 8 through the Android Open Source Project (AOSP). We have reported the identified BLE pairing vulnerabilities to Bluetooth Special Interest Group (SIG), Google, Apple, Texas Instruments (TI) and all of them are actively addressing this issue. Google rated the reported security flaw a High Severity.
Streaming and Batch Algorithms for Truss Decomposition
Venue: arXiv cs.SI 2019
Year: 2019

Truss decomposition is a method used to analyze large sparse graphs in order to identify successively better connected subgraphs. Since in many domains the underlying graph changes over time, its associated truss decomposition needs to be updated as well. This work focuses on the problem of incrementally updating an existing truss decomposition and makes the following three significant contributions. First, it presents a theory that identifies how the truss decomposition can change as new edges get added. Second, it develops an efficient incremental algorithm that incorporates various optimizations to update the truss decomposition after every edge addition. These optimizations are designed to reduce the number of edges that are explored by the algorithm. Third, it extends this algorithm to batch updates (i.e., where the truss decomposition needs to be updated after a set of edges are added), which reduces the overall computations that need to be performed. We evaluated the performance of our algorithms on real-world datasets. Our incremental algorithm achieves over 250000x average speedup for inserting an edge in a graph with 10 million edges relative to the non-incremental algorithm. Further, our experiments on batch updates show that our batch algorithm consistently performs better than the incremental algorithm.
Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households
Venue: arXiv cs.LG 2019
Year: 2019

Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power consumption of an entire house. This information allows households to receive customized advice on how to better manage their electrical consumption. In this paper, we present an alternative NILM method that breaks down the aggregated power signal into categories of appliances. The ultimate goal is to use this approach for demand-side management to estimate potential flexibility within the electricity consumption of households. Our method is implemented as an algorithm combining NILM and load profile simulation. This algorithm, based on a Markov model, allocates an activity chain to each inhabitant of the household, deduces from the whole-house power measurement and statistical data the appliance usage, generate the power profile accordingly and finally returns the share of energy consumed by each appliance category over time. To analyze its performance, the algorithm was benchmarked against several state-of-the-art NILM algorithms and tested on three public datasets. The proposed algorithm is unsupervised; hence it does not require any labeled data, which are expensive to acquire. Although better performance is shown for the supervised algorithms, our proposed unsupervised algorithm achieves a similar range of uncertainty while saving on the cost of acquiring labeled data. Additionally, our method requires lower computational power compared to most of the tested NILM algorithms. It was designed for low-sampling-rate power measurement (every 15 min), which corresponds to the frequency range of most common smart meters.
On Distributed Runtime Verification by Aggregate Computing
Venue: arXiv cs.SE 2019
Year: 2019

Runtime verification is a computing analysis paradigm based on observing a system at runtime (to check its expected behaviour) by means of monitors generated from formal specifications. Distributed runtime verification is runtime verification in connection with distributed systems: it comprises both monitoring of distributed systems and using distributed systems for monitoring. Aggregate computing is a programming paradigm based on a reference computing machine that is the aggregate collection of devices that cooperatively carry out a computational process: the details of behaviour, position and number of devices are largely abstracted away, to be replaced with a space-filling computational environment. In this position paper we argue, by means of simple examples, that aggregate computing is particularly well suited for implementing distributed monitors. Our aim is to foster further research on how to generate aggregate computing monitors from suitable formal specifications.
Language Tasks and Language Games: On Methodology in Current Natural Language Processing Research
Venue: arXiv cs.CL 2019
Year: 2019

"This paper introduces a new task and a new dataset", "we improve the state of the art in X by Y" -- it is rare to find a current natural language processing paper (or AI paper more generally) that does not contain such statements. What is mostly left implicit, however, is the assumption that this necessarily constitutes progress, and what it constitutes progress towards. Here, we make more precise the normally impressionistically used notions of language task and language game and ask how a research programme built on these might make progress towards the goal of modelling general language competence.
Image Captioning with Sparse Recurrent Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

Recurrent Neural Network (RNN) has been widely used to tackle a wide variety of language generation problems and are capable of attaining state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment to mobile and embedded devices infeasible. Driven by this problem, many works have proposed a number of pruning methods to reduce the sizes of the RNN model. In this work, we propose an end-to-end pruning method for image captioning models equipped with visual attention. Our proposed method is able to achieve sparsity levels up to 97.5% without significant performance loss relative to the baseline (~ 2% loss at 40x compression after fine-tuning). Our method is also simple to use and tune, facilitating faster development times for neural network practitioners. We perform extensive experiments on the popular MS-COCO dataset in order to empirically validate the efficacy of our proposed method.
The Rise and Fall of the Note: Changing Paper Lengths in ACM CSCW, 2000-2018
Venue: arXiv cs.HC 2019
Year: 2019

In this note, I quantitatively examine various trends in the lengths of published papers in ACM CSCW from 2000-2018, focusing on several major transitions in editorial and reviewing policy. The focus is on the rise and fall of the 4-page note, which was introduced in 2004 as a separate submission type to the 10-page double-column "full paper" format. From 2004-2012, 4-page notes of 2,500 to 4,500 words consistently represented about 20-35\% of all publications. In 2013, minimum and maximum page lengths were officially removed, with no formal distinction made between full papers and notes. The note soon completely disappeared as a distinct genre, which co-occurred with a trend in steadily rising paper lengths. I discuss such findings both as they directly relate to local concerns in CSCW and in the context of longstanding theoretical discussions around genre theory and how socio-technical structures and affordances impact participation in distributed, computer-mediated organizations and user-generated content platforms. There are many possible explanations for the decline of the note and the emergence of longer and longer papers, which I identify for future work. I conclude by addressing the implications of such findings for the CSCW community, particularly given how genre norms impact what kinds of scholarship and scholars thrive in CSCW, as well as whether new top-down rules or bottom-up guidelines ought to be developed around paper lengths and different kinds of contributions.
A Large-Scale Empirical Study of Geotagging Behavior on Twitter
Venue: arXiv cs.SI 2019
Year: 2019

Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.
Metric-based Regularization and Temporal Ensemble for Multi-task Learning using Heterogeneous Unsupervised Tasks
Venue: arXiv cs.CV 2019
Year: 2019

One of the ways to improve the performance of a target task is to learn the transfer of abundant knowledge of a pre-trained network. However, learning of the pre-trained network requires high computation capability and large-scale labeled dataset. To mitigate the burden of large-scale labeling, learning in un/self-supervised manner can be a solution. In addition, using unsupervised multi-task learning, a generalized feature representation can be learned. However, unsupervised multi-task learning can be biased to a specific task. To overcome this problem, we propose the metric-based regularization term and temporal task ensemble (TTE) for multi-task learning. Since these two techniques prevent the entire network from learning in a state deviated to a specific task, it is possible to learn a generalized feature representation that appropriately reflects the characteristics of each task without biasing. Experimental results for three target tasks such as classification, object detection and embedding clustering prove that the TTE-based multi-task framework is more effective than the state-of-the-art (SOTA) method in improving the performance of a target task.
ICurry
Venue: arXiv cs.PL 2019
Year: 2019

FlatCurry is a well-established intermediate representation of Curry programs used in compilers that translate Curry code into Prolog and Haskell code. Some FlatCurry constructs have no direct translation into imperative code. These constructs must be each handled differently when translating Curry code into C, C++ and Python code. We introduce a new representation of Curry programs, called ICurry, and derive a translation from all FlatCurry constructs into ICurry. We present the syntax of ICurry and the translation from FlatCurry to ICurry. We present a model of functional logic computations as graph rewriting, show how this model can be implemented in a low-level imperative language, and describe the translation from ICurry to this model.