OpenRaiser/Intern-Atlas huggingface dataset:
Vote Delegation with Unknown Preferences
Venue: arXiv cs.GT 2021
Year: 2021

We examine vote delegation when preferences of agents are private information. One group of agents (delegators) does not want to participate in voting and abstains under conventional voting or can delegate its votes to the other group (voters) who decide between two alternatives. We show that free delegation favors minorities, that is, alternatives that have a lower chance of winning ex-ante. The same occurs if the number of voting rights that actual voters can have is capped. When the number of delegators increases, the probability that the ex-ante minority wins under free and capped delegation converges to the one under conventional voting--albeit non-monotonically. Our results are obtained in a private value setting but can be readily translated into an information aggregation setting when voters receive a signal about the ''correct" alternative with some probability.
A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation
Venue: arXiv cs.RO 2021
Year: 2021

Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to minimise the disruption caused to humans while moving. This implies predicting how people will move and complying with social conventions. Avoiding disrupting personal spaces, people's paths and interactions are examples of these social conventions. This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms. Along with the model, this paper presents an evolution of the dataset SocNav1 [25] which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using different Graph Neural Network blocks. The model trained achieves close-to-human performance in the dataset. In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. The dataset and the model are available in a public repository (https://github.com/gnns4hri/sngnnv2).
Sparsely Factored Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

The standard approach to incorporate linguistic information to neural machine translation systems consists in maintaining separate vocabularies for each of the annotated features to be incorporated (e.g. POS tags, dependency relation label), embed them, and then aggregate them with each subword in the word they belong to. This approach, however, cannot easily accommodate annotation schemes that are not dense for every word. We propose a method suited for such a case, showing large improvements in out-of-domain data, and comparable quality for the in-domain data. Experiments are performed in morphologically-rich languages like Basque and German, for the case of low-resource scenarios.
Automatic Face Understanding: Recognizing Families in Photos
Venue: arXiv cs.CV 2021
Year: 2021

We built the largest database for kinship recognition. The data were labeled using a novel clustering algorithm that used label proposals as side information to guide more accurate clusters. Great savings in time and human input was had. Statistically, FIW shows enormous gains over its predecessors. We have several benchmarks in kinship verification, family classification, tri-subject verification, and large-scale search and retrieval. We also trained CNNs on FIW and deployed the model on the renowned KinWild I and II to gain SOTA. Most recently, we further augmented FIW with MM. Now, video dynamics, audio, and text captions can be used in the decision making of kinship recognition systems. We expect FIW will significantly impact research and reality. Additionally, we tackled the classic problem of facial landmark localization. A majority of these networks have objectives based on L1 or L2 norms, which inherit several disadvantages. The locations of landmarks are determined from generated heatmaps from which predicted landmark locations get penalized without accounting for the spread: a high scatter corresponds to low confidence and vice-versa. To address this, we introduced an objective that penalizes for low confidence. Another issue is a dependency on labeled data, which is expensive to collect and susceptible to error. We addressed both issues by proposing an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims SOTA on renowned benchmarks. Furthermore, our model is robust with a reduced size: 1/8 the number of channels is comparable to SOTA in real-time on a CPU. Finally, we built BFW to serve as a proxy to measure bias across ethnicity and gender subgroups, allowing us to characterize FR performances per subgroup. We show performances are non-optimal when a single threshold is used to determine whether sample pairs are genuine.
A Survey on Locality Sensitive Hashing Algorithms and their Applications
Venue: arXiv cs.DB 2021
Year: 2021

Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular techniques for finding approximate nearest neighbor searches in high-dimensional spaces. The main benefits of LSH are its sub-linear query performance and theoretical guarantees on the query accuracy. In this survey paper, we provide a review of state-of-the-art LSH and Distributed LSH techniques. Most importantly, unlike any other prior survey, we present how Locality Sensitive Hashing is utilized in different application domains.
Scanning the Cycle: Timing-based Authentication on PLCs
Venue: arXiv cs.CR 2021
Year: 2021

Programmable Logic Controllers (PLCs) are a core component of an Industrial Control System (ICS). However, if a PLC is compromised or the commands sent across a network from the PLCs are spoofed, consequences could be catastrophic. In this work, a novel technique to authenticate PLCs is proposed that aims at raising the bar against powerful attackers while being compatible with real-time systems. The proposed technique captures timing information for each controller in a non-invasive manner. It is argued that Scan Cycle is a unique feature of a PLC that can be approximated passively by observing network traffic. An attacker that spoofs commands issued by the PLCs would deviate from such fingerprints. To detect replay attacks a PLC Watermarking technique is proposed. PLC Watermarking models the relationship between the scan cycle and the control logic by modeling the input/output as a function of request/response messages of a PLC. The proposed technique is validated on an operational water treatment plant (SWaT) and smart grid (EPIC) testbed. Results from experiments indicate that PLCs can be distinguished based on their scan cycle timing characteristics.
BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation
Venue: arXiv cs.CV 2021
Year: 2021

Reducing outcome variance is an essential task in deep learning based medical image analysis. Bootstrap aggregating, also known as bagging, is a canonical ensemble algorithm for aggregating weak learners to become a strong learner. Random forest is one of the most powerful machine learning algorithms before deep learning era, whose superior performance is driven by fitting bagged decision trees (weak learners). Inspired by the random forest technique, we propose a simple bagging ensemble deep segmentation (BEDs) method to train multiple U-Nets with partial training data to segment dense nuclei on pathological images. The contributions of this study are three-fold: (1) developing a self-ensemble learning framework for nucleus segmentation; (2) aggregating testing stage augmentation with self-ensemble learning; and (3) elucidating the idea that self-ensemble and testing stage stain augmentation are complementary strategies for a superior segmentation performance. Implementation Detail: https://github.com/xingli1102/BEDs.
Mobile Computational Photography: A Tour
Venue: arXiv cs.CV 2021
Year: 2021

The first mobile camera phone was sold only 20 years ago, when taking pictures with one's phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is more camera than phone. How did this happen? This transformation was enabled by advances in computational photography -the science and engineering of making great images from small form factor, mobile cameras. Modern algorithmic and computing advances, including machine learning, have changed the rules of photography, bringing to it new modes of capture, post-processing, storage, and sharing. In this paper, we give a brief history of mobile computational photography and describe some of the key technological components, including burst photography, noise reduction, and super-resolution. At each step, we may draw naive parallels to the human visual system.
Designing Approximately Optimal Search on Matching Platforms
Venue: arXiv cs.GT 2021
Year: 2021

We study the design of a decentralized two-sided matching market in which agents' search is guided by the platform. There are finitely many agent types, each with (potentially random) preferences drawn from known type-specific distributions. Equipped with knowledge of these distributions, the platform guides the search process by determining the meeting rate between each pair of types from the two sides. Focusing on symmetric pairwise preferences in a continuum model, we first characterize the unique stationary equilibrium that arises given a feasible set of meeting rates. We then introduce the platform's optimal directed search problem, which involves optimizing meeting rates to maximize equilibrium social welfare. We first show that incentive issues arising from congestion and cannibalization make the design problem fairly intricate. Nonetheless, we develop an efficiently computable search design whose corresponding equilibrium achieves at least 1/4 the social welfare of the optimal design. In fact, our construction always recovers at least 1/4 the first-best social welfare, where agents' incentives are disregarded. Our directed search design is simple and easy-to-implement, as its corresponding bipartite graph consists of disjoint stars. Furthermore, our design implies the platform can substantially limit choice and yet induce an equilibrium with an approximately optimal welfare. Finally, we show that approximation is likely the best we can hope for by establishing that the problem of designing optimal directed search is NP-hard to even approximate beyond a certain constant factor.
Selecting Features for the Next Release in a System of Systems Context
Venue: arXiv cs.SE 2021
Year: 2021

Smart Cities are developing in parallel with the global trend towards urbanization. The ultimate goal of Smart City projects is to deliver a positive impact for the citizens and the socio-economic and ecological environment. This involves the challenge to derive concrete requirements for (technical) projects from overarching concepts like Quality of Life (QoL) and Subjective Well-Being (SWB). Linking long-term, impact oriented goals with project outputs and outcomes is a complex problem. Decision making on requirements and resulting features of single Smart City projects (or systems) is even more complex since cities are not like monolithic, hierarchical and well structured systems. Nevertheless, systems engineering provides concepts which support decision making in such situations. Complex socio-technical systems such as smart cities can be characterized as systems of systems (SoS). A SoS is composed of independently developed systems that nevertheless provide a higher-level integrated functionality. To add new functionality to a SoS, either existing systems must be extended or new systems must be developed and integrated. In both cases, the extension of functionality is usually done in small increments and structured via software releases. However, the decision which features to include in the next release is complex and difficult to manage when done manually. To address this, we make use of the multi-objective next release problem (MONRP) to search for an optimal set of features for a software release in a SoS context. In order to refine the search in an early planning phase, we propose a technique to model and validate the features using the scenario modeling language for Kotlin (SMLK). This is demonstrated with a proof-of-concept implementation.
Grid Cell Path Integration For Movement-Based Visual Object Recognition
Venue: arXiv cs.AI 2021
Year: 2021

Grid cells enable the brain to model the physical space of the world and navigate effectively via path integration, updating self-position using information from self-movement. Recent proposals suggest that the brain might use similar mechanisms to understand the structure of objects in diverse sensory modalities, including vision. In machine vision, object recognition given a sequence of sensory samples of an image, such as saccades, is a challenging problem when the sequence does not follow a consistent, fixed pattern - yet this is something humans do naturally and effortlessly. We explore how grid cell-based path integration in a cortical network can support reliable recognition of objects given an arbitrary sequence of inputs. Our network (GridCellNet) uses grid cell computations to integrate visual information and make predictions based on movements. We use local Hebbian plasticity rules to learn rapidly from a handful of examples (few-shot learning), and consider the task of recognizing MNIST digits given only a sequence of image feature patches. We compare GridCellNet to k-Nearest Neighbour (k-NN) classifiers as well as recurrent neural networks (RNNs), both of which lack explicit mechanisms for handling arbitrary sequences of input samples. We show that GridCellNet can reliably perform classification, generalizing to both unseen examples and completely novel sequence trajectories. We further show that inference is often successful after sampling a fraction of the input space, enabling the predictive GridCellNet to reconstruct the rest of the image given just a few movements. We propose that dynamically moving agents with active sensors can use grid cell representations not only for navigation, but also for efficient recognition and feature prediction of seen objects.
Smart Build Targets Batching Service at Google
Venue: arXiv cs.SE 2021
Year: 2021

Google has a monolithic codebase with tens of millions build targets. Each build target specifies the information that is needed to build a software artifact or run tests. It is common to execute a subset of build targets at each revision and make sure that the change does not break the codebase. Google's build service system uses Bazel to build targets. Bazel takes as input a build that specifies the execution context, flags and build targets to run. The outputs are the build libraries, binaries or test results. To be able to support developer's daily activities, the build service system runs millions of builds per day. It is a known issue that a build with many targets could run out of the allocated memory or exceed its execution deadline. This is problematic because it reduces the developer's productivity, e.g. code submissions or binary releases. In this paper, we propose a technique that predicts the memory usage and executor occupancy of a build. The technique batches a set of targets such that the build created with those targets does not run out of memory or exceed its deadline. This approach significantly reduces the number of builds that run out of memory or exceed the deadlines, hence improving developer's productivity.
Categorising Software Contexts: Research-in-Progress
Venue: arXiv cs.SE 2021
Year: 2021

A growing number of researchers suggest that software process must be tailored to a project's context to achieve maximal performance. Researchers have studied 'context' in an ad-hoc way, with focus on those contextual factors that appear to be of significance. The result is that we have no useful basis upon which to contrast and compare studies. We are currently researching a theoretical basis for software context for the purpose of tailoring and note that a deeper consideration of the meaning of the term 'context' is required before we can proceed. In this paper, we examine the term and present a model based on insights gained from our initial categorisation of contextual factors from the literature. We test our understanding by analysing a further six documents. Our contribution thus far is a model that we believe will support a theoretical operationalisation of software context for the purpose of process tailoring.
DeepMuD: Multi-user Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning
Venue: arXiv cs.IT 2021
Year: 2021

In this letter, we propose a deep learning-aided multi-user detection (DeepMuD) in uplink non-orthogonal multiple access (NOMA) to empower the massive machine-type communication where an offline-trained Long Short-Term Memory (LSTM)-based network is used for multi-user detection. In the proposed DeepMuD, a perfect channel state information (CSI) is also not required since it is able to perform a joint channel estimation and multi-user detection with the pilot responses, where the pilot-to-frame ratio is very low. The proposed DeepMuD improves the error performance of the uplink NOMA significantly and outperforms the conventional detectors (even with perfect CSI). Moreover, this gain becomes superb with the increase in the number of Internet of Things (IoT) devices. Furthermore, the proposed DeepMuD has a flexible detection and regardless of the number of IoT devices, the multi-user detection can be performed. Thus, an arbitrary number of IoT devices can be served without a signaling overhead, which enables the grant-free communication.
A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space
Venue: arXiv cs.LG 2021
Year: 2021

Recent studies revealed the mathematical connection of deep neural network (DNN) and dynamic system. However, the fundamental principle of DNN has not been fully characterized with dynamic system in terms of optimization and generalization. To this end, we build the connection of DNN and continuity equation where the measure is conserved to model the forward propagation process of DNN which has not been addressed before. DNN learns the transformation of the input distribution to the output one. However, in the measure space, there are infinite curves connecting two distributions. Which one can lead to good optimization and generaliztion for DNN? By diving the optimal transport theory, we find DNN with weight decay attempts to learn the geodesic curve in the Wasserstein space, which is induced by the optimal transport map. Compared with plain network, ResNet is a better approximation to the geodesic curve, which explains why ResNet can be optimized and generalize better. Numerical experiments show that the data tracks of both plain network and ResNet tend to be line-shape in term of line-shape score (LSS), and the map learned by ResNet is closer to the optimal transport map in term of optimal transport score (OTS). In a word, we conclude a mathematical principle of deep learning is to learn the geodesic curve in the Wasserstein space; and deep learning is a great engineering realization of continuous transformation in high-dimensional space.
DSRN: an Efficient Deep Network for Image Relighting
Venue: arXiv cs.CV 2021
Year: 2021

Custom and natural lighting conditions can be emulated in images of the scene during post-editing. Extraordinary capabilities of the deep learning framework can be utilized for such purpose. Deep image relighting allows automatic photo enhancement by illumination-specific retouching. Most of the state-of-the-art methods for relighting are run-time intensive and memory inefficient. In this paper, we propose an efficient, real-time framework Deep Stacked Relighting Network (DSRN) for image relighting by utilizing the aggregated features from input image at different scales. Our model is very lightweight with total size of about 42 MB and has an average inference time of about 0.0116s for image of resolution $1024 \times 1024$ which is faster as compared to other multi-scale models. Our solution is quite robust for translating image color temperature from input image to target image and also performs moderately for light gradient generation with respect to the target image. Additionally, we show that if images illuminated from opposite directions are used as input, the qualitative results improve over using a single input image.
On the Impact of Attachment Strategies for Payment Channel Networks
Venue: arXiv cs.NI 2021
Year: 2021

Payment channel networks, such as Bitcoin's Lightning Network, promise to improve the scalability of blockchain systems by processing the majority of transactions off-chain. Due to the design, the positioning of nodes in the network topology is a highly influential factor regarding the experienced performance, costs, and fee revenue of network participants. As a consequence, today's Lightning Network is built around a small number of highly-connected hubs. Recent literature shows the centralizing tendencies to be incentive-compatible and at the same time detrimental to security and privacy. The choice of attachment strategies therefore becomes a crucial factor for the future of such systems. In this paper, we provide an empirical study on the (local and global) impact of various attachment strategies for payment channel networks. To this end, we introduce candidate strategies from the field of graph theory and analyze them with respect to their computational complexity as well as their repercussions for end users and service providers. Moreover, we evaluate their long-term impact on the network topology.
Sparse-Interest Network for Sequential Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Recent methods in sequential recommendation focus on learning an overall embedding vector from a user's behavior sequence for the next-item recommendation. However, from empirical analysis, we discovered that a user's behavior sequence often contains multiple conceptually distinct items, while a unified embedding vector is primarily affected by one's most recent frequent actions. Thus, it may fail to infer the next preferred item if conceptually similar items are not dominant in recent interactions. To this end, an alternative solution is to represent each user with multiple embedding vectors encoding different aspects of the user's intentions. Nevertheless, recent work on multi-interest embedding usually considers a small number of concepts discovered via clustering, which may not be comparable to the large pool of item categories in real systems. It is a non-trivial task to effectively model a large number of diverse conceptual prototypes, as items are often not conceptually well clustered in fine granularity. Besides, an individual usually interacts with only a sparse set of concepts. In light of this, we propose a novel \textbf{S}parse \textbf{I}nterest \textbf{NE}twork (SINE) for sequential recommendation. Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly. Given multiple interest embeddings, we develop an interest aggregation module to actively predict the user's current intention and then use it to explicitly model multiple interests for next-item prediction. Empirical results on several public benchmark datasets and one large-scale industrial dataset demonstrate that SINE can achieve substantial improvement over state-of-the-art methods.