OpenRaiser/Intern-Atlas huggingface dataset:
Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search
Venue: arXiv cs.LG 2021
Year: 2021

We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too complex to encode manually, generative models can provide a learned latent space to approximate these factors. When used as a search space, however, the range and diversity of possible outputs are limited to the expressivity and generative capabilities of the learned model. We compare the output diversity of a quality diversity evolutionary search performed in two different search spaces: 1) a predefined parameterized space and 2) the latent space of a variational autoencoder model. We find that the search on an explicit parametric encoding creates more diverse artifact sets than searching the latent space. A learned model is better at interpolating between known data points than at extrapolating or expanding towards unseen examples. We recommend using a generative model's latent space primarily to measure similarity between artifacts rather than for search and generation. Whenever a parametric encoding is obtainable, it should be preferred over a learned representation as it produces a higher diversity of solutions.
Threat Landscape for Smart Grid Systems
Venue: arXiv cs.CR 2021
Year: 2021

Smart Grids are energy delivery networks, constituting an evolution of power grids, in which a bidirectional flow between power providers and consumers is established. These flows support the transfer of electricity and information, in order to support automation actions in the context of the energy delivery network. Insofar, many smart grid implementations and implementation proposals have emerged, with varying degrees of feature delivery and sophistication. While smart grids offer many advantages, their distributed nature and information flow streams between energy producers and consumers enable the launching of a number of attacks against the smart grid infrastructure, where the related consequences may range from economic loss to complete failure of the smart grid. In this paper, we survey the threat landscape of smart grids, identifying threats that are specific to this infrastructure, providing an assessment of the severity of the consequences of each attack type, discerning features that can be utilized to detect attacks and listing methods that can be used to mitigate them.
A Probabilistic Approach to Personalize Type-based Facet Ranking for POI Suggestion
Venue: arXiv cs.IR 2021
Year: 2021

Faceted Search Systems (FSS) have become one of the main search interfaces used in vertical search systems, offering users meaningful facets to refine their search query and narrow down the results quickly to find the intended search target. This work focuses on the problem of ranking type-based facets. In a structured information space, type-based facets (t-facets) indicate the category to which each object belongs. When they belong to a large multi-level taxonomy, it is desirable to rank them separately before ranking other facet groups. This helps the searcher in filtering the results according to their type first. This also makes it easier to rank the rest of the facets once the type of the intended search target is selected. Existing research employs the same ranking methods for different facet groups. In this research, we propose a two-step approach to personalize t-facet ranking. The first step assigns a relevance score to each individual leaf-node t-facet. The score is generated using probabilistic models and it reflects t-facet relevance to the query and the user profile. In the second step, this score is used to re-order and select the sub-tree to present to the user. We investigate the usefulness of the proposed method to a Point Of Interest (POI) suggestion task. Our evaluation aims at capturing the user effort required to fulfil her search needs by using the ranked facets. The proposed approach achieved better results than other existing personalized baselines.
Parallel Sandpiles or Spurious Bidirectional Icepiles?
Venue: arXiv cs.FL 2021
Year: 2021

In a recent paper E. Formenti and K. Perrot (FP) introduce a global rule assumed to describe the discrete time dynamics associated with a sandpile model under the parallel application of a suitable local rule acting on d dimensional lattices of cells equipped with uniform neighborhood. In this paper we submit this approach to a critical analysis, in the simplest elementary particular case of a one-dimensional lattice, which can be divided in two parts. In the first part we prove that the FP global rule does not describe the dynamics of standard sandpiles, but rather furnishes a description of the quite different situation of height difference between consecutive piles. This is a semantic uncorrect difference of interpretation. In the second part we investigate the consequences of the uncorrect FP assumption proving that their global rule describes a bidirectional spurious dynamics of icepiles (rather than sandpiles), in the sense that this latter is the consequence of application of three local rules: bidirectional vertical rule, bidirectional horizontal rule (typical of icepiles), and a granule jump from the bottom to the top (spurious rule of the dynamics).
Attacks on a Privacy-Preserving Publish-Subscribe System and a Ride-Hailing Service
Venue: arXiv cs.CR 2021
Year: 2021

A privacy-preserving Context-Aware Publish-Subscribe System (CA-PSS) enables an intermediary (broker) to match the content from a publisher and the subscription by a subscriber based on the current context while preserving confidentiality of the subscriptions and notifications. While a privacy-preserving Ride-Hailing Service (RHS) enables an intermediary (service provider) to match a ride request with a taxi driver in a privacy-friendly manner. In this work, we attack a privacy-preserving CA-PSS proposed by Nabeel et al. (2013), where we show that any entity in the system including the broker can learn the confidential subscriptions of the subscribers. We also attack a privacy-preserving RHS called lpRide proposed by Yu et al. (2019), where we show that any rider/driver can efficiently recover the secret keys of all other riders and drivers. Also, we show that any rider/driver will be able to learn the location of any rider. The attacks are based on our cryptanalysis of the modified Paillier cryptosystem proposed by Nabeel et al. that forms a building block for both the above protocols.
Combinatorial Multi-armed Bandits for Resource Allocation
Venue: arXiv cs.LG 2021
Year: 2021

We study the sequential resource allocation problem where a decision maker repeatedly allocates budgets between resources. Motivating examples include allocating limited computing time or wireless spectrum bands to multiple users (i.e., resources). At each timestep, the decision maker should distribute its available budgets among different resources to maximize the expected reward, or equivalently to minimize the cumulative regret. In doing so, the decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward. For example, users may send messages of different urgency over wireless spectrum bands; the reward generated by allocating spectrum to a user then depends on the message's urgency. We assume each user's reward follows a random process that is initially unknown. We design combinatorial multi-armed bandit algorithms to solve this problem with discrete or continuous budgets. We prove the proposed algorithms achieve logarithmic regrets under semi-bandit feedback.
Did I delete my cookies? Cookies respawning with browser fingerprinting
Venue: arXiv cs.CR 2021
Year: 2021

Stateful and stateless web tracking gathered much attention in the last decade, however they were always measured separately. To the best of our knowledge, our study is the first to detect and measure cookie respawning with browser and machine fingerprinting. We develop a detection methodology that allows us to detect cookies dependency on browser and machine features. Our results show that 1,150 out of the top 30, 000 Alexa websites deploy this tracking mechanism. We further uncover how domains collaborate to respawn cookies through fingerprinting. We find out that this technique can be used to track users across websites even when third-party cookies are deprecated. Together with a legal scholar, we conclude that cookie respawning with browser fingerprinting lacks legal interpretation under the GDPR and the ePrivacy directive, but its use in practice may breach them, thus subjecting it to fines up to 20 million euro.
Stability Constrained Mobile Manipulation Planning on Rough Terrain
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents a framework that allows online dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot's quasi-static path is then used as an initial guess to warm-start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint.
MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors
Venue: arXiv cs.LG 2021
Year: 2021

The advances of sensor technology enable people to monitor air quality through widely distributed low-cost sensors. However, measurements from these sensors usually encounter high biases and require a calibration step to reach an acceptable performance in down-streaming analytical tasks. Most existing calibration methods calibrate one type of sensor at a time, which we call single-task calibration. Despite the popularity of this single-task schema, it may neglect interactions among calibration tasks of different sensors, which encompass underlying information to promote calibration performance. In this paper, we propose a multi-task calibration network (MTNet) to calibrate multiple sensors (e.g., carbon monoxide and nitrogen oxide sensors) simultaneously, modeling the interactions among tasks. MTNet consists of a single shared module, and several task-specific modules. Specifically, in the shared module, we extend the multi-gate mixture-of-experts structure to harmonize the task conflicts and correlations among different tasks; in each task-specific module, we introduce a feature selection strategy to customize the input for the specific task. These improvements allow MTNet to learn interaction information shared across different tasks, and task-specific information for each calibration task as well. We evaluate MTNet on three real-world datasets and compare it with several established baselines. The experimental results demonstrate that MTNet achieves the state-of-the-art performance.
An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle Imagery
Venue: arXiv cs.CV 2021
Year: 2021

Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus, which is an endemic plant that exists in the Tehuacán-Cuicatlán Valley in southeastern Mexico. We used a public dataset created by a group of researchers that consists of more than 20000 remote sensing images. The experimental results confirm the effectiveness of the proposed model compared to other models reported in the literature like InceptionV3 and the modified LeNet-5 CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall, 97.5% as f1-score, and 0.056 loss.
Who Gets What, According to Whom? An Analysis of Fairness Perceptions in Service Allocation
Venue: arXiv cs.CY 2021
Year: 2021

Algorithmic fairness research has traditionally been linked to the disciplines of philosophy, ethics, and economics, where notions of fairness are prescriptive and seek objectivity. Increasingly, however, scholars are turning to the study of what different people perceive to be fair, and how these perceptions can or should help to shape the design of machine learning, particularly in the policy realm. The present work experimentally explores five novel research questions at the intersection of the "Who," "What," and "How" of fairness perceptions. Specifically, we present the results of a multi-factor conjoint analysis study that quantifies the effects of the specific context in which a question is asked, the framing of the given question, and who is answering it. Our results broadly suggest that the "Who" and "What," at least, matter in ways that are 1) not easily explained by any one theoretical perspective, 2) have critical implications for how perceptions of fairness should be measured and/or integrated into algorithmic decision-making systems.
Learning Robust Latent Representations for Controllable Speech Synthesis
Venue: arXiv cs.SD 2021
Year: 2021

State-of-the-art Variational Auto-Encoders (VAEs) for learning disentangled latent representations give impressive results in discovering features like pitch, pause duration, and accent in speech data, leading to highly controllable text-to-speech (TTS) synthesis. However, these LSTM-based VAEs fail to learn latent clusters of speaker attributes when trained on either limited or noisy datasets. Further, different latent variables start encoding the same features, limiting the control and expressiveness during speech synthesis. To resolve these issues, we propose RTI-VAE (Reordered Transformer with Information reduction VAE) where we minimize the mutual information between different latent variables and devise a modified Transformer architecture with layer reordering to learn controllable latent representations in speech data. We show that RTI-VAE reduces the cluster overlap of speaker attributes by at least 30\% over LSTM-VAE and by at least 7\% over vanilla Transformer-VAE.
Friend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem
Venue: arXiv cs.CY 2021
Year: 2021

We introduce the identity labeling problem - given an individual in a social situation, can we predict what identity(ies) they will be labeled with by someone else? This problem remains a theoretical gap and methodological challenge, evidenced by the fact that models of social-cognition often sidestep the issue by treating identities as already known. We build on insights from existing models to develop a new framework, entitled Latent Cognitive Social Spaces, that can incorporate multiple social cues including sentiment information, socio-demographic characteristics, and institutional associations to estimate the most culturally expected identity. We apply our model to data collected in two vignette experiments, finding that it predicts identity labeling choices of participants with a mean absolute error of 10.9%, a 100% improvement over previous models based on parallel constraint satisfaction and affect control theory.
Safety of the Intended Driving Behavior Using Rulebooks
Venue: arXiv cs.RO 2021
Year: 2021

Autonomous Vehicles (AVs) are complex systems that drive in uncertain environments and potentially navigate unforeseeable situations. Safety of these systems requires not only an absence of malfunctions but also high performance of functions in many different scenarios. The ISO/PAS 21448 [1] guidance recommends a process to ensure the Safety of the Intended Functionality (SOTIF) for road vehicles. This process starts with a functional specification that fully describes the intended functionality and further includes the verification and validation that the AV meets this specification. For the path planning function, defining the correct sequence of control actions for each vehicle in all potential driving situations is intractable. In this paper, the authors provide a link between the Rulebooks framework, presented by [2], and the SOTIF process. We establish that Rulebooks provide a functional description of the path planning task in an AV and discuss the potential usage of the method for verification and validation.
Towards Benchmarking the Utility of Explanations for Model Debugging
Venue: arXiv cs.AI 2021
Year: 2021

Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision. But how useful are they for an end-user towards accomplishing a given task? In this vision paper, we argue the need for a benchmark to facilitate evaluations of the utility of post-hoc explanation methods. As a first step to this end, we enumerate desirable properties that such a benchmark should possess for the task of debugging text classifiers. Additionally, we highlight that such a benchmark facilitates not only assessing the effectiveness of explanations but also their efficiency.
AP-side WLAN Analytics
Venue: arXiv cs.NI 2021
Year: 2021

Monitoring the network performance experienced by the end-user is crucial for managers of wireless networks as it can enable them to remotely modify the network parameters to improve the end-user experience. Unfortunately, for performance monitoring, managers are typically limited to the logs of the Access Points (APs) that they manage. This information does not directly capture factors that can hinder station (STA) side transmissions. Consequently, state-of-the-art methods to measure such metrics primarily involve active measurements. Unfortunately, such active measurements increase traffic load and if used regularly and for all the STAs can potentially disrupt user traffic, thereby worsening performance for other users in the network and draining the battery of mobile devices. This thesis enables passive AP-side network analytics. In the first part of the thesis, I present virtual speed test, a measurement based framework that enables an AP to estimate speed test results for any of its associated clients solely based on AP-side observables. Next, I present Uplink Latency Microscope (uScope), an AP-side framework for estimation of WLAN uplink latency for any of the associated STAs and decomposition into its constituent components. Similar to virtual speed test, uScope makes estimations solely based on passive AP-side observations. We implement both frameworks on a commodity hardware platform and conduct extensive field trials on a university campus and in a residential apartment complex. In over 1 million tests, the two proposed frameworks demonstrate an estimation accuracy with errors under 10%.
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
Venue: arXiv cs.LG 2021
Year: 2021

Graph Neural Networks (GNNs) are proven to be powerful models to generate node embedding for downstream applications. However, due to the high computation complexity of GNN inference, it is hard to deploy GNNs for large-scale or real-time applications. In this paper, we propose to accelerate GNN inference by pruning the dimensions in each layer with negligible accuracy loss. Our pruning framework uses a novel LASSO regression formulation for GNNs to identify feature dimensions (channels) that have high influence on the output activation. We identify two inference scenarios and design pruning schemes based on their computation and memory usage for each. To further reduce the inference complexity, we effectively store and reuse hidden features of visited nodes, which significantly reduces the number of supporting nodes needed to compute the target embedding. We evaluate the proposed method with the node classification problem on five popular datasets and a real-time spam detection application. We demonstrate that the pruned GNN models greatly reduce computation and memory usage with little accuracy loss. For full inference, the proposed method achieves an average of 3.27x speedup with only 0.002 drop in F1-Micro on GPU. For batched inference, the proposed method achieves an average of 6.67x speedup with only 0.003 drop in F1-Micro on CPU. To the best of our knowledge, we are the first to accelerate large scale real-time GNN inference through channel pruning.
Language Acquisition is Embodied, Interactive, Emotive: a Research Proposal
Venue: arXiv cs.CL 2021
Year: 2021

Humans' experience of the world is profoundly multimodal from the beginning, so why do existing state-of-the-art language models only use text as a modality to learn and represent semantic meaning? In this paper we review the literature on the role of embodiment and emotion in the interactive setting of spoken dialogue as necessary prerequisites for language learning for human children, including how words in child vocabularies are largely concrete, then shift to become more abstract as the children get older. We sketch a model of semantics that leverages current transformer-based models and a word-level grounded model, then explain the robot-dialogue system that will make use of our semantic model, the setting for the system to learn language, and existing benchmarks for evaluation.
Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing
Venue: arXiv cs.ET 2021
Year: 2021

Recently, spatial photonic Ising machines (SPIM) have been demonstrated to compute the minima of Hamiltonians for large-scale spin systems. Here we propose to implement an antiferromagnetic model through optoelectronic correlation computing with SPIM. Also we exploit the gauge transformation which enables encoding the spins and the interaction strengths in a single phase-only spatial light modulator. With a simple setup, we experimentally show the ground state search of an antiferromagnetic model with $40000$ spins in number-partitioning problem. Thus such an optoelectronic computing exhibits great programmability and scalability for the practical applications of studying statistical systems and combinatorial optimization problems.
Adaptive Policy Transfer in Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Efficient and robust policy transfer remains a key challenge for reinforcement learning to become viable for real-wold robotics. Policy transfer through warm initialization, imitation, or interacting over a large set of agents with randomized instances, have been commonly applied to solve a variety of Reinforcement Learning tasks. However, this seems far from how skill transfer happens in the biological world: Humans and animals are able to quickly adapt the learned behaviors between similar tasks and learn new skills when presented with new situations. Here we seek to answer the question: Will learning to combine adaptation and exploration lead to a more efficient transfer of policies between domains? We introduce a principled mechanism that can "Adapt-to-Learn", that is adapt the source policy to learn to solve a target task with significant transition differences and uncertainties. We show that the presented method learns to seamlessly combine learning from adaptation and exploration and leads to a robust policy transfer algorithm with significantly reduced sample complexity in transferring skills between related tasks.