OpenRaiser/Intern-Atlas huggingface dataset:
Release Practices for Mobile Apps--What do Users and Developers Think?
Venue: arXiv cs.SE 2019
Year: 2019

Large software organizations such as Facebook or Netflix, who otherwise make daily or even hourly releases of their web applications using continuous delivery, have had to invest heavily into a customized release strategy for their mobile apps, because the vetting process of app stores introduces lag and uncertainty into the release process. Amidst these large, resourceful organizations, it is unknown how the average mobile app developer organizes her app's releases, even though an incorrect strategy might bring a premature app update to the market that drives away customers towards the heavy market competition. To understand the common release strategies used for mobile apps, the rationale behind them and their perceived impact on users, we performed two surveys with users and developers. We found that half of the developers have a clear strategy for their mobile app releases, since especially the more experienced developers believe that it affects user feedback. We also found that users are aware of new app updates, yet only half of the surveyed users enables automatic updating of apps. While the release date and frequency is not a decisive factor to install an app, users prefer to install apps that were updated more recently and less frequently. Our study suggests that an app's release strategy is a factor that affects the ongoing success of mobile apps.
EQSA: Earthquake Situational Analytics from Social Media
Venue: arXiv cs.IR 2019
Year: 2019

This paper introduces EQSA, an interactive exploratory tool for earthquake situational analytics using social media. EQSA is designed to support users to characterize the condition across the area around the earthquake zone, regarding related events, resources to be allocated, and responses from the community. On the general level, changes in the volume of messages from chosen categories are presented, assisting users in conveying a general idea of the condition. More in-depth analysis is provided with topic evolution, community visualization, and location representation. EQSA is developed with intuitive, interactive features and multiple linked views, visualizing social media data, and supporting users to gain a comprehensive insight into the situation. In this paper, we present the application of EQSA with the VAST Challenge 2019: Mini-Challenge 3 (MC3) dataset.
Monotonic Properties of Completed Aggregates in Recursive Queries
Venue: arXiv cs.DB 2019
Year: 2019

The use of aggregates in recursion enables efficient and scalable support for a wide range of BigData algorithms, including those used in graph applications, KDD applications, and ML applications, which have proven difficult to be expressed and supported efficiently in BigData systems supporting Datalog or SQL. The problem with these languages and systems is that, to avoid the semantic and computational issues created by non-monotonic constructs in recursion, they only allow programs that are stratified with respect to negation and aggregates. Now, while this crippling restriction is well-justified for negation, it is frequently unjustified for aggregates, since (i) aggregates are often monotonic in the standard lattice of set-containment, (ii) the PreM property guarantees that programs with extrema in recursion are equivalent to stratified programs where extrema are used as post-constraints, and (iii) any program computing any aggregates on sets of facts of predictable cardinality tantamounts to stratified programs where the precomputation of the cardinality of the set is followed by a stratum where recursive rules only use monotonic constructs. With (i) and (ii) covered in previous papers, this paper focuses on (iii) using examples of great practical interest. For such examples, we provide a formal semantics that is conducive to efficient and scalable implementations via well-known techniques such as semi-naive fixpoint currently supported by most Datalog and SQL3 systems.
LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we explore the task of generating photo-realistic face images from lines. Previous methods based on conditional generative adversarial networks (cGANs) have shown their power to generate visually plausible images when a conditional image and an output image share well-aligned structures. However, these models fail to synthesize face images with a whole set of well-defined structures, e.g. eyes, noses, mouths, etc., especially when the conditional line map lacks one or several parts. To address this problem, we propose a conditional self-attention generative adversarial network (CSAGAN). We introduce a conditional self-attention mechanism to cGANs to capture long-range dependencies between different regions in faces. We also build a multi-scale discriminator. The large-scale discriminator enforces the completeness of global structures and the small-scale discriminator encourages fine details, thereby enhancing the realism of generated face images. We evaluate the proposed model on the CelebA-HD dataset by two perceptual user studies and three quantitative metrics. The experiment results demonstrate that our method generates high-quality facial images while preserving facial structures. Our results outperform state-of-the-art methods both quantitatively and qualitatively.
PT-CoDE: Pre-trained Context-Dependent Encoder for Utterance-level Emotion Recognition
Venue: arXiv cs.CL 2019
Year: 2019

Utterance-level emotion recognition (ULER) is a significant research topic for understanding human behaviors and developing empathetic chatting machines in the artificial intelligence area. Unlike traditional text classification problem, this task is supported by a limited number of datasets, among which most contain inadequate conversations or speeches. Such a data scarcity issue limits the possibility of training larger and more powerful models for this task. Witnessing the success of transfer learning in natural language process (NLP), we propose to pre-train a context-dependent encoder (CoDE) for ULER by learning from unlabeled conversation data. Essentially, CoDE is a hierarchical architecture that contains an utterance encoder and a conversation encoder, making it different from those works that aim to pre-train a universal sentence encoder. Also, we propose a new pre-training task named "conversation completion" (CoCo), which attempts to select the correct answer from candidate answers to fill a masked utterance in a question conversation. The CoCo task is carried out on pure movie subtitles so that our CoDE can be pre-trained in an unsupervised fashion. Finally, the pre-trained CoDE (PT-CoDE) is fine-tuned for ULER and boosts the model performance significantly on five datasets.
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text
Venue: arXiv cs.LG 2019
Year: 2019

Guaranteeing a certain level of user privacy in an arbitrary piece of text is a challenging issue. However, with this challenge comes the potential of unlocking access to vast data stores for training machine learning models and supporting data driven decisions. We address this problem through the lens of dx-privacy, a generalization of Differential Privacy to non Hamming distance metrics. In this work, we explore word representations in Hyperbolic space as a means of preserving privacy in text. We provide a proof satisfying dx-privacy, then we define a probability distribution in Hyperbolic space and describe a way to sample from it in high dimensions. Privacy is provided by perturbing vector representations of words in high dimensional Hyperbolic space to obtain a semantic generalization. We conduct a series of experiments to demonstrate the tradeoff between privacy and utility. Our privacy experiments illustrate protections against an authorship attribution algorithm while our utility experiments highlight the minimal impact of our perturbations on several downstream machine learning models. Compared to the Euclidean baseline, we observe > 20x greater guarantees on expected privacy against comparable worst case statistics.
Predicting ice flow using machine learning
Venue: arXiv cs.LG 2019
Year: 2019

Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniques from unsupervised learning of future video frame prediction, to increase the accuracy of ice flow tracking in multi-spectral satellite images. As the volume of cryosphere data increases in coming years, this is an interesting and important opportunity for machine learning to address a global challenge for climate change, risk management from floods, and conserving freshwater resources. Future frame prediction of ice melt and tracking the optical flow of ice dynamics presents modeling difficulties, due to uncertainties in global temperature increase, changing precipitation patterns, occlusion from cloud cover, rapid melting and glacier retreat due to black carbon aerosol deposition, from wildfires or human fossil emissions. We show the adversarial learning method helps improve the accuracy of tracking the optical flow of ice dynamics compared to existing methods in climate science. We present a dataset, IceNet, to encourage machine learning research and to help facilitate further applications in the areas of cryospheric science and climate change.
BatteryLab, A Distributed Power Monitoring Platform For Mobile Devices
Venue: arXiv cs.DC 2019
Year: 2019

Recent advances in cloud computing have simplified the way that both software development and testing are performed. Unfortunately, this is not true for battery testing for which state of the art test-beds simply consist of one phone attached to a power meter. These test-beds have limited resources, access, and are overall hard to maintain; for these reasons, they often sit idle with no experiment to run. In this paper, we propose to share existing battery testing setups and build BatteryLab, a distributed platform for battery measurements. Our vision is to transform independent battery testing setups into vantage points of a planetary-scale measurement platform offering heterogeneous devices and testing conditions. In the paper, we design and deploy a combination of hardware and software solutions to enable BatteryLab's vision. We then preliminarily evaluate BatteryLab's accuracy of battery reporting, along with some system benchmarking. We also demonstrate how BatteryLab can be used by researchers to investigate a simple research question.
Secure Digital Administration in Medical Environment
Venue: arXiv cs.CR 2019
Year: 2019

The efficiency and service quality in a medical environment can be improved by using electronic documents (or e-docs) and digital signatures to speed up both doctors' activity and to provide in the same time easy retrieval and use of needed data without loosing convenience. Our proposed solution satisfies these needs by making use of the AIDA system and many cutting-edge techniques to build a digitalized management system. To be more specific, we present firstly the AIDA document exchange framework for the management (creation, search, storage) of e-docs expressed in XML format. The framework provides security services, like privacy, authorization, authentication and non-repudiation. We describe next the use of AIDA in a real world medical service, namely the creation of electronic medical records (e- MRs). Doctors can use mobile devices that embed a secure trustworthy environment defined also in AIDA for digitally signing the e-MRs. Technically speaking a handheld PC equipped with a smart-card reader and integrating What You See Is What You Sign (WYSIWYS) features will be used for viewing and signing the e-MRs. Furthermore the proposed system is easily integrated with the infrastructure (e.g., database system) already in use at hospital's administration site and allows easy handling and updating of data processed on the mobile devices. The use of web interface for the operations to be executed on the mobile device or for those executed on the remote part of the system makes the whole application homogeneous and easy to use.
You Can Run, But You Cannot Hide: Using Elevation Profiles to Breach Location Privacy through Trajectory Prediction
Venue: arXiv cs.CR 2019
Year: 2019

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Strava and Runkeeper, utilize information for activity tracking, and have recently witnessed a boom in popularity. Those trackers have their own web platforms, and allow users to share activities on such platforms, or even with other social network platforms. To preserve privacy of users while allowing sharing, those platforms allow users to disclose partial information, such as the elevation profile for an activity, which supposedly will not leak the location trajectory. In this work we examine the extent to which publicly available elevation profiles can be used to predict the location trajectory of users. To tackle this problem, we devise three threat settings under which the city, borough, or even a route can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning- and deep learning-based techniques, and achieve a prediction success rate ranging from 59.59% to 95.83%. The findings are alarming, and highlight that sharing information such as elevation profile may have significant privacy risks.
Using machine learning and information visualisation for discovering latent topics in Twitter news
Venue: arXiv cs.SI 2019
Year: 2019

We propose a method to discover latent topics and visualise large collections of tweets for easy identification and interpretation of topics, and exemplify its use with tweets from a Colombian mass media giant in the period 2014--2019. The latent topic analysis is performed in two ways: with the training of a Latent Dirichlet Allocation model, and with the combination of the FastText unsupervised model to represent tweets as vectors and the implementation of K-means clustering to group tweets into topics. Using a classification task, we found that people respond differently according to the various news topics. The classification tasks consists of the following: given a reply to a news tweet, we train a supervised algorithm to predict the topic of the news tweet solely from the reply. Furthermore, we show how the Colombian peace treaty has had a profound impact on the Colombian society, as it is the topic in which most people engage to show their opinions.
Signatures and models for syntax and operational semantics in the presence of variable binding
Venue: arXiv cs.LO 2019
Year: 2019

This thesis deals with the specification and construction of syntax and operational semantics of a programming language. We work with a general notion of signature for specifying objects of a given category as initial objects in a suitable category of models.This characterization, in the spirit of Initial Semantics, gives a justification of the recursion principle. Languages with variable binding, such as the pure lambda calculus, are monads on the category of sets specified through the classical algebraic signatures. The first extensions to syntaxes with equations that we consider are quotients of these algebraic signatures. They allow, for example, to specify a binary commutative operation. But some equations, such as associativity, seem to remain out of reach. We thus introduce the notion of 2-signature, consisting in two parts: a specification of operations through a usual signature as before, and a set of equations among them. We identify the class of algebraic 2-signatures for which the existence of the associated syntax is guaranteed. Finally, we takle the specification of the operational semantics of a programming language such as lambda calculus with beta-reduction. To this end, we introduce the notion of reduction monad and their signatures, then we generalize them to get the notion of operational monad.
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach
Venue: arXiv cs.NI 2019
Year: 2019

Federated learning allows mobile devices, i.e., workers, to use their local data to collaboratively train a global model required by the model owner. Federated learning thus addresses the privacy issues of traditional machine learning. However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy. To address the energy constraint, a power beacon can be used that recharges energy to the workers. However, the model owner may need to pay an energy cost to the power beacon for the energy recharge. To address the high network resource cost, the model owner can use a WiFi channel, called default channel, for the global model transmissions. However, communication interruptions may occur due to the instability of the default channel quality. For this, special channels such as LTE channels can be used, but this incurs channel cost. As such, the problem of the model owner is to decide amounts of energy recharged to the workers and to choose channels used to transmit its global model to the workers to maximize the number of global model transmissions while minimizing the energy and channel costs. This is challenging for the model owner under the uncertainty of the channel, energy and mobility states of the workers. In this paper, we thus propose to employ the Deep Q-Network (DQN) that enables the model owner to find the optimal decisions on the energy and the channels without any a priori network knowledge. Simulation results show that the proposed DQN always achieves better performance compared to the conventional algorithms.
Attribute-aware Pedestrian Detection in a Crowd
Venue: arXiv cs.CV 2019
Year: 2019

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusion and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression~(NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, a novel ground truth target is designed to alleviate the difficulties caused by the attribute configuration and extremely class imbalance issues during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on two benchmark datasets including CityPersons and CrowdHuman. The experimental results show that our approach outperforms state-of-the-art methods at a large margin on pedestrian detection.
On large-scale genre classification in symbolically encoded music by automatic identification of repeating patterns
Venue: arXiv cs.IR 2019
Year: 2019

The importance of repetitions in music is well-known. In this paper, we study music repetitions in the context of effective and efficient automatic genre classification in large-scale music-databases. We aim at enhancing the access and organization of pieces of music in Digital Libraries by allowing automatic categorization of entire collections by considering only their musical content. We handover to the public a set of genre-specific patterns to support research in musicology. The patterns can be used, for instance, to explore and analyze the relations between musical genres. There are many existing algorithms that could be used to identify and extract repeating patterns in symbolically encoded music. In our case, the extracted patterns are used as representations of the pieces of music on the underlying corpus and, consecutively, to train and evaluate a classifier to automatically identify genres. In this paper, we apply two very fast algorithms enabling us to experiment on large and diverse corpora. Thus, we are able to find patterns with strong discrimination power that can be used in various applications. We carried out experiments on a corpus containing over 40,000 MIDI files annotated with at least one genre. The experiments suggest that our approach is scalable and capable of dealing with real-world-size music collections.
Semi-Decentralized Coordinated Online Learning for Continuous Games with Coupled Constraints via Augmented Lagrangian
Venue: arXiv cs.GT 2019
Year: 2019

We consider a class of concave continuous games in which the corresponding admissible strategy profile of each player underlies affine coupling constraints. We propose a novel algorithm that leads the relevant population dynamic toward Nash equilibrium. This algorithm is based on a mirror ascent algorithm, which suits with the framework of no-regret online learning, and on the augmented Lagrangian method. The decentralization aspect of the algorithm corresponds to the aspects that the iterate of each player requires the local information about how she contributes to the coupling constraints and the price vector broadcasted by a central coordinator. So each player needs not know about the population action. Moreover, no specific control by the central primary coordinator is required. We give a condition on the step sizes and the degree of the augmentation of the Lagrangian, such that the proposed algorithm converges to a generalized Nash equilibrium.
Domain-agnostic Question-Answering with Adversarial Training
Venue: arXiv cs.CL 2019
Year: 2019

Adapting models to new domain without finetuning is a challenging problem in deep learning. In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task. Our model consists of a conventional QA model and a discriminator. The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features. We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model.