OpenRaiser/Intern-Atlas huggingface dataset:
Mapping the Privacy-Utility Tradeoff in Mobile Phone Data for Development
Venue: arXiv cs.CY 2018
Year: 2018

Today's age of data holds high potential to enhance the way we pursue and monitor progress in the fields of development and humanitarian action. We study the relation between data utility and privacy risk in large-scale behavioral data, focusing on mobile phone metadata as paradigmatic domain. To measure utility, we survey experts about the value of mobile phone metadata at various spatial and temporal granularity levels. To measure privacy, we propose a formal and intuitive measure of reidentification risk$\unicode{x2014}$the information ratio$\unicode{x2014}$and compute it at each granularity level. Our results confirm the existence of a stark tradeoff between data utility and reidentifiability, where the most valuable datasets are also most prone to reidentification. When data is specified at ZIP-code and hourly levels, outside knowledge of only 7% of a person's data suffices for reidentification and retrieval of the remaining 93%. In contrast, in the least valuable dataset, specified at municipality and daily levels, reidentification requires on average outside knowledge of 51%, or 31 data points, of a person's data to retrieve the remaining 49%. Overall, our findings show that coarsening data directly erodes its value, and highlight the need for using data-coarsening, not as stand-alone mechanism, but in combination with data-sharing models that provide adjustable degrees of accountability and security.
From Thumbnails to Summaries - A single Deep Neural Network to Rule Them All
Venue: arXiv cs.MM 2018
Year: 2018

Video summaries come in many forms, from traditional single-image thumbnails, animated thumbnails, storyboards, to trailer-like video summaries. Content creators use the summaries to display the most attractive portion of their videos; the users use them to quickly evaluate if a video is worth watching. All forms of summaries are essential to video viewers, content creators, and advertisers. Often video content management systems have to generate multiple versions of summaries that vary in duration and presentational forms. We present a framework ReconstSum that utilizes LSTM-based autoencoder architecture to extract and select a sparse subset of video frames or keyshots that optimally represent the input video in an unsupervised manner. The encoder selects a subset from the input video while the decoder seeks to reconstruct the video from the selection. The goal is to minimize the difference between the original input video and the reconstructed video. Our method is easily extendable to generate a variety of applications including static video thumbnails, animated thumbnails, storyboards and "trailer-like" highlights. We specifically study and evaluate two most popular use cases: thumbnail generation and storyboard generation. We demonstrate that our methods generate better results than the state-of-the-art techniques in both use cases.
Multi-Beam UAV Communication in Cellular Uplink: Cooperative Interference Cancellation and Sum-Rate Maximization
Venue: arXiv cs.IT 2018
Year: 2018

Integrating unmanned aerial vehicles (UAVs) into the cellular network as new aerial users is a promising solution to meet their ever-increasing communication demands in a plethora of applications. Due to the high UAV altitude, the channels between UAVs and the ground base stations (GBSs) are dominated by the strong line-of-sight (LoS) links, thus severe interference may be generated to/from the GBSs in the uplink/downlink, which renders the interference management with coexisting terrestrial and aerial users a more challenging problem to solve. In this paper, we study the uplink communication from a multi-antenna UAV to a set of GBSs in its signal coverage region. Among these GBSs, we denote available GBSs as the ones that do not serve any terrestrial users at the assigned resource block (RB) of the UAV, and occupied GBSs as the rest that are serving their respectively associated terrestrial users in the same RB. We propose a new cooperative interference cancellation strategy for the multi-beam UAV uplink communication, which aims to eliminate the co-channel interference at each of the occupied GBSs and in the meanwhile maximize the sum-rate to the available GBSs. Specifically, the multi-antenna UAV sends multiple data streams to selected available GBSs, which in turn forward their decoded data streams to their backhaul-connected occupied GBSs for interference cancellation. To draw useful insights, the maximum degrees-of-freedom (DoF) achievable by the multi-beam UAV communication for sum-rate maximization in the high signal-to-noise ratio (SNR) regime is first characterized, subject to the stringent constraint that all the occupied GBSs do not suffer from any interference in the UAV's uplink transmission. Then, based on the DoF-optimal design, the achievable sum-rate at finite SNR is maximized, subject to given maximum allowable interference power constraints at each occupied GBS.
Did We Get It Right? Predicting Query Performance in E-commerce Search
Venue: arXiv cs.IR 2018
Year: 2018

In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not. This is a critical question in evaluating any e-commerce search engine. While this question is traditionally answered using simple metrics like query click-through rate (CTR), we observe that in e-commerce search, such metrics can be misleading. Upon inspection, we find cases where CTR is high but the results are poor and vice versa. Similar cases exist for other metrics like time to click which are often also used for evaluating search engines. We aim to learn the quality of the results served by the search engine based on users' interactions with the results. Although this problem has been studied in the web search context, this is the first study for e-commerce search, to the best of our knowledge. Despite certain commonalities with evaluating web search engines, there are several major differences such as underlying reasons for search failure, and availability of rich user interaction data with products (e.g. adding a product to the cart). We study large-scale user interaction logs from Flipkart's search engine, analyze behavioral patterns and build models to classify queries based on user behavior signals. We demonstrate the feasibility and efficacy of such models in accurately predicting query performance. Our classifier is able to achieve an average AUC of 0.75 on a held-out test set.
Repairing Description Logic Ontologies by Weakening Axioms
Venue: arXiv cs.LO 2018
Year: 2018

The classical approach for repairing a Description Logic ontology O in the sense of removing an unwanted consequence $α$ is to delete a minimal number of axioms from O such that the resulting ontology O' does not have the consequence $α$. However, the complete deletion of axioms may be too rough, in the sense that it may also remove consequences that are actually wanted. To alleviate this problem, we propose a more gentle way of repair in which axioms are not necessarily deleted, but only weakened. On the one hand, we investigate general properties of this gentle repair method. On the other hand, we propose and analyze concrete approaches for weakening axioms expressed in the Description Logic EL.
Attention-based Pyramid Aggregation Network for Visual Place Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.
Stability Analysis of Piecewise Affine Systems with Multi-model Model Predictive Control
Venue: arXiv cs.SY 2018
Year: 2018

Constrained model predictive control (MPC) is a widely used control strategy, which employs moving horizon-based on-line optimisation to compute the optimum path of the manipulated variables. Nonlinear MPC can utilize detailed models but it is computationally expensive; on the other hand linear MPC may not be adequate. Piecewise affine (PWA) models can describe the underlying nonlinear dynamics more accurately, therefore they can provide a viable trade-off through their use in multi-model linear MPC configurations, which avoid integer programming. However, such schemes may introduce uncertainty affecting the closed loop stability. In this work, we propose an input to output stability analysis for closed loop systems, consisting of PWA models, where an observer and multi-model linear MPC are applied together, under unstructured uncertainty. Integral quadratic constraints (IQCs) are employed to assess the robustness of MPC under uncertainty. We create a model pool, by performing linearisation on selected transient points. All the possible uncertainties and nonlinearities (including the controller) can be introduced in the framework, assuming that they admit the appropriate IQCs, whilst the dissipation inequality can provide necessary conditions incorporating IQCs. We demonstrate the existence of static multipliers, which can reduce the conservatism of the stability analysis significantly. The proposed methodology is demonstrated through two engineering case studies.
There is more to PCG than Meets the Eye: NPC AI, Dynamic Camera, PVS and Lightmaps
Venue: arXiv cs.GR 2018
Year: 2018

Procedural content generation (PCG) concerns all sorts of algorithms and tools which automatically produce game content, without requiring manual authoring by game artists. Besides generating com-plex static meshes, the PCG core usually encompasses geometrical information about the game world that can be useful in supporting other critical subsystems of the game engine. We discuss our experi-ence from the development of the iOS game title named "Fallen God: Escape Underworld", and show how our PCG produced extra metadata regarding the game world, in particular: (i) an annotated dun-geon graph to support path finding for NPC AI to attack or avoid the player (working for bipeds, birds, insects and serpents); (ii) a quantized voxel space to allow discrete A* for the dynamic camera system to work in the continuous 3d space; (iii) dungeon portals to support a dynamic PVS; and (iv) procedural ambient occlusion and tessellation of a separate set of simplified meshes to support very-fast and high-quality light mapping.
Studying Preferences and Concerns about Information Disclosure in Email Notifications
Venue: arXiv cs.HC 2018
Year: 2018

The proliferation of network-connected devices and applications has resulted in people receiving dozens, or hundreds, of notifications per day. When people are in the presence of others, each notification poses some risk of accidental information disclosure; onlookers may see notifications appear above the lock screen of a mobile phone, on the periphery of a desktop or laptop display, or projected onscreen during a presentation. In this paper, we quantify the prevalence of these accidental disclosures in the context of email notifications, and we study people's relevant preferences and concerns. Our results are compiled from an exploratory retrospective survey of 131 respondents, and a separate contextual-labeling study in which 169 participants labeled 1,040 meeting-email pairs. We find that, for 53% of people, at least 1 in 10 email notifications poses an information disclosure risk. We also find that the real or perceived severity of these risks depend both on user characteristics and attributes of the meeting or email (e.g. the number of recipients or attendees). We conclude by exploring machine learning algorithms to predict people's comfort levels given an email notification and a context, then we present implications for the design of future contextually-relevant notification systems.
End-to-End Simulation of Integrated Access and Backhaul at mmWaves
Venue: arXiv cs.NI 2018
Year: 2018

Recently, the millimeter wave (mmWave) bands have been investigated as a means to support the foreseen extreme data rate demands of next-generation cellular networks (5G). However, in order to overcome the severe isotropic path loss and the harsh propagation experienced at such high frequencies, a dense base station deployment is required, which may be infeasible because of the unavailability of fiber drops to provide wired backhauling. To address this challenge, the 3GPP is investigating the concept of Integrated Access and Backhaul (IAB), i.e., the possibility of providing wireless backhaul to the mobile terminals. In this paper, we (i) extend the capabilities of the existing mmWave module for ns-3 to support advanced IAB functionalities, and (ii) evaluate the end-to-end performance of the IAB architecture through system-level full-stack simulations in terms of experienced throughput and communication latency. We finally provide guidelines on how to design optimal wireless backhaul solutions in the presence of resource-constrained and traffic-congested mmWave scenarios.
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer. Our GNAP block is designed to give dynamic weights to local features in different spatial positions without losing spatial symmetry. We use a GNAP block in a face feature embedding CNN to produce discriminative face feature vectors for pose-robust face recognition. The GNAP block is of very cheap computational cost, but it is very powerful for frontal-profile face recognition. Under the CFP frontal-profile protocol, the GNAP block can not only reduce EER dramatically but also boost TPR@FPR=0.1% (TPR i.e. True Positive Rate, FPR i.e. False Positive Rate) substantially. Our experiments show that the GNAP block greatly promotes pose-robust face recognition over the base model especially at low false positive rate.
Power Management Policies for AWGN Channels with Slow-Varying Harvested Energy
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study power management (PM) policies for an Energy Harvesting Additive White Gaussian Noise (EH-AWGN) channel. The arrival rate of the harvested energy is assumed to remain unchanged during each data frame (block code) and to change independently across block codes. The harvested energy sequence is known causally (online) at the transmitter. The transmitter is equipped with a rechargeable battery with infinite energy storage capacity. The transmitter is able to adapt the allocated energy and the corresponding transmission rate of each block according to a PM policy. Three novel online PM policies are established. The policies are universal, in the sense of the distribution of the harvested energy, and simple, in the sense of complexity, and asymptotically optimal, in the sense of maximum achievable average rates (throughput) taken over a long-term horizon of blocks.
Explicit Auditing
Venue: arXiv cs.LO 2018
Year: 2018

The Calculus of Audited Units (CAU) is a typed lambda calculus resulting from a computational interpretation of Artemov's Justification Logic under the Curry-Howard isomorphism; it extends the simply typed lambda calculus by providing audited types, inhabited by expressions carrying a trail of their past computation history. Unlike most other auditing techniques, CAU allows the inspection of trails at runtime as a first-class operation, with applications in security, debugging, and transparency of scientific computation. An efficient implementation of CAU is challenging: not only do the sizes of trails grow rapidly, but they also need to be normalized after every beta reduction. In this paper, we study how to reduce terms more efficiently in an untyped variant of CAU by means of explicit substitutions and explicit auditing operations, finally deriving a call-by-value abstract machine.
Traj2User: exploiting embeddings for computing similarity of users mobile behavior
Venue: arXiv cs.SI 2018
Year: 2018

Semantic trajectories are high level representations of user movements where several aspects related to the movement context are represented as heterogeneous textual labels. With the objective of finding a meaningful similarity measure for semantically enriched trajectories, we propose Traj2User, a Word2Vec-inspired method for the generation of a vector representation of user movements as user embeddings. Traj2User uses simple representations of trajectories and delegates the definition of the similarity model to the learning process of the network. Preliminary results show that Traj2User is able to generate effective user embeddings.
Fusion Subspace Clustering: Full and Incomplete Data
Venue: arXiv cs.LG 2018
Year: 2018

Modern inference and learning often hinge on identifying low-dimensional structures that approximate large scale data. Subspace clustering achieves this through a union of linear subspaces. However, in contemporary applications data is increasingly often incomplete, rendering standard (full-data) methods inapplicable. On the other hand, existing incomplete-data methods present major drawbacks, like lifting an already high-dimensional problem, or requiring a super polynomial number of samples. Motivated by this, we introduce a new subspace clustering algorithm inspired by fusion penalties. The main idea is to permanently assign each datum to a subspace of its own, and minimize the distance between the subspaces of all data, so that subspaces of the same cluster get fused together. Our approach is entirely new to both, full and missing data, and unlike other methods, it directly allows noise, it requires no liftings, it allows low, high, and even full-rank data, it approaches optimal (information-theoretic) sampling rates, and it does not rely on other methods such as low-rank matrix completion to handle missing data. Furthermore, our extensive experiments on both real and synthetic data show that our approach performs comparably to the state-of-the-art with complete data, and dramatically better if data is missing.
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
Venue: arXiv cs.LG 2018
Year: 2018

We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model's global behavior, we propose the LIME-FOLD algorithm ---a heuristic-based inductive logic programming (ILP) algorithm capable of learning non-monotonic logic programs---that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. Meanwhile, the number of induced rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.
Energy-Efficiency Gains of Caching for Interference Channels
Venue: arXiv cs.IT 2018
Year: 2018

This paper initiates the study of energy-efficiency gains provided by caching. We focus on the cache-aided Gaussian interference channel in the low-SNR regime. We propose a strategy that creates content overlaps at the transmitter caches to allow for co-operation between the transmitters. This co-operation yields a beamforming gain, which has to be traded off against a multicasting gain. We evaluate the performance of this strategy and show its approximate optimality in both the single-receiver case and the single-transmitter case.
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
Venue: arXiv cs.CV 2018
Year: 2018

Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding Network (ATEN) that alternatively performs temporal encoding among key frames and flow-guided feature propagation from other consecutive frames between two key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the instance-level parsing result for each key frame, which integrates both the global human parsing and instance-level human segmentation into a unified model. To balance between accuracy and efficiency, the flow-guided feature propagation is used to directly parse consecutive frames according to their identified temporal consistency with key frames. On the other hand, ATEN leverages the convolution gated recurrent units (convGRU) to exploit temporal changes over a series of key frames, which are further used to facilitate the frame-level instance-level parsing. By alternatively performing direct feature propagation between consistent frames and temporal encoding network among key frames, our ATEN achieves a good balance between frame-level accuracy and time efficiency, which is a common crucial problem in video object segmentation research. To demonstrate the superiority of our ATEN, extensive experiments are conducted on the most popular video segmentation benchmark (DAVIS) and a newly collected Video Instance-level Parsing (VIP) dataset, which is the first video instance-level human parsing dataset comprised of 404 sequences and over 20k frames with instance-level and pixel-wise annotations.
Memristor-based Synaptic Sampling Machines
Venue: arXiv cs.ET 2018
Year: 2018

Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.