OpenRaiser/Intern-Atlas huggingface dataset:
An Empirical Analysis of AI Contributions to Sustainable Cities (SDG11)
Venue: arXiv cs.AI 2022
Year: 2022

Artificial Intelligence (AI) presents opportunities to develop tools and techniques for addressing some of the major global challenges and deliver solutions with significant social and economic impacts. The application of AI has far-reaching implications for the 17 Sustainable Development Goals (SDGs) in general, and sustainable urban development in particular. However, existing attempts to understand and use the opportunities offered by AI for SDG 11 have been explored sparsely, and the shortage of empirical evidence about the practical application of AI remains. In this chapter, we analyze the contribution of AI to support the progress of SDG 11 (Sustainable Cities and Communities). We address the knowledge gap by empirically analyzing the AI systems (N = 29) from the AIxSDG database and the Community Research and Development Information Service (CORDIS) database. Our analysis revealed that AI systems have indeed contributed to advancing sustainable cities in several ways (e.g., waste management, air quality monitoring, disaster response management, transportation management), but many projects are still working for citizens and not with them. This snapshot of AI's impact on SDG11 is inherently partial, yet useful to advance our understanding as we move towards more mature systems and research on the impact of AI systems for social good.
Approximate Policy Iteration with Bisimulation Metrics
Venue: arXiv cs.LG 2022
Year: 2022

Bisimulation metrics define a distance measure between states of a Markov decision process (MDP) based on a comparison of reward sequences. Due to this property they provide theoretical guarantees in value function approximation (VFA). In this work we first prove that bisimulation and $π$-bisimulation metrics can be defined via a more general class of Sinkhorn distances, which unifies various state similarity metrics used in recent work. Then we describe an approximate policy iteration (API) procedure that uses a bisimulation-based discretization of the state space for VFA and prove asymptotic performance bounds. Next, we bound the difference between $π$-bisimulation metrics in terms of the change in the policies themselves. Based on these results, we design an API($α$) procedure that employs conservative policy updates and enjoys better performance bounds than the naive API approach. We discuss how such API procedures map onto practical actor-critic methods that use bisimulation metrics for state representation learning. Lastly, we validate our theoretical results and investigate their practical implications via a controlled empirical analysis based on an implementation of bisimulation-based API for finite MDPs.
Diversity Multiplexing Trade-off and Selection Gain in Media-Based Modulation
Venue: arXiv cs.IT 2022
Year: 2022

The idea of Media-based Modulation (MBM) is to embed information in the variations of the transmission media (channel states). Using an RF closure with $w$ RF walls, MBM creates a set of $2^w$ select-able states for the end-to-end channel. Each state represents an index of an MBM constellation point. In each state, the wave (tone) emanating from the transmit antenna experiences many pseudo-random back-and-forth reflections within the RF closure. The RF signal, upon finally leaving the RF closure, further propagates in the rich scattering environment to reach the receiver. This results in an independent complex channel gain to each receive antenna. As a result, coordinates of different MBM constellation points (vectors of channel gains formed over received antennas) will be independent of each other. This is unlike legacy transmission schemes where a fixed constellation structure used at the transmitter will be multiplied by a fixed, but random, channel gain. Due to this independence property, MBM offers several advantages vs. legacy systems, including "additivity of information over multiple receive antennas (regardless of the number of transmit antennas)", and "inherent diversity over a static fading channel". This work studies the Diversity-Multiplexing Trade-off of an MBM constellation. Analytical expressions are provided that demonstrate the advantages of MBM vs. legacy systems. In particular, it is shown that a $1\times N_r$ SIMO-MBM constellation equipped with an MDS code (even with a relatively small code length) significantly outperforms an $N_r\times N_r$ legacy MIMO.
An Automated Approach for Privacy Leakage Identification in IoT Apps
Venue: arXiv cs.SE 2022
Year: 2022

This paper presents a fully automated static analysis approach and a tool, Taint-Things, for the identification of tainted flows in SmartThings IoT apps. Taint-Things accurately identifies all tainted flows reported by one of the state-of-the-art tools with at least 4 times improved performance. Our approach reports potential vulnerable tainted flows in a form of a concise security slice, where the relevant parts of the code are given with the lines affecting the sensitive information, which could provide security auditors with an effective and precise tool to pinpoint security issues in SmartThings apps under test. We also present and test ways to add precision to Taint-Things by adding extra sensitivities; we provide different approaches for flow, path and context sensitive analyses through modules that can be added to Taint-Things. We present experiments to evaluate Taint-Things by running it on a SmartThings app dataset as well as testing for precision and recall on a set generated by a mutation framework to see how much coverage is achieved without adding false positives. This shows an improvement in performance both in terms of speed up to 4 folds, as well as improving the precision avoiding false positives by providing a higher level of flow and path sensitivity analysis in comparison with one of state of the art tools.
Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned
Venue: arXiv cs.LG 2022
Year: 2022

The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late 2019 as one of the largest machine learning competitions ever held focused on building performance. It was hosted on the Kaggle platform and resulted in 39,402 prediction submissions, with the top five teams splitting $25,000 in prize money. This paper outlines lessons learned from participants, mainly from teams who scored in the top 5% of the competition. Various insights were gained from their experience through an online survey, analysis of publicly shared submissions and notebooks, and the documentation of the winning teams. The top-performing solutions mostly used ensembles of Gradient Boosting Machine (GBM) tree-based models, with the LightGBM package being the most popular. The survey participants indicated that the preprocessing and feature extraction phases were the most important aspects of creating the best modeling approach. All the survey respondents used Python as their primary modeling tool, and it was common to use Jupyter-style Notebooks as development environments. These conclusions are essential to help steer the research and practical implementation of building energy meter prediction in the future.
Redactor: A Data-centric and Individualized Defense Against Inference Attacks
Venue: arXiv cs.LG 2022
Year: 2022

Information leakage is becoming a critical problem as various information becomes publicly available by mistake, and machine learning models train on that data to provide services. As a result, one's private information could easily be memorized by such trained models. Unfortunately, deleting information is out of the question as the data is already exposed to the Web or third-party platforms. Moreover, we cannot necessarily control the labeling process and the model trainings by other parties either. In this setting, we study the problem of targeted disinformation generation where the goal is to dilute the data and thus make a model safer and more robust against inference attacks on a specific target (e.g., a person's profile) by only inserting new data. Our method finds the closest points to the target in the input space that will be labeled as a different class. Since we cannot control the labeling process, we instead conservatively estimate the labels probabilistically by combining decision boundaries of multiple classifiers using data programming techniques. Our experiments show that a probabilistic decision boundary can be a good proxy for labelers, and that our approach is effective in defending against inference attacks and can scale to large data.
Universality of parametric Coupling Flows over parametric diffeomorphisms
Venue: arXiv cs.LG 2022
Year: 2022

Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression. The approximation universality for CFlows is of paramount importance to ensure the model expressiveness. In this paper, we prove that CFlows can approximate any diffeomorphism in C^k-norm if its layers can approximate certain single-coordinate transforms. Specifically, we derive that a composition of affine coupling layers and invertible linear transforms achieves this universality. Furthermore, in parametric cases where the diffeomorphism depends on some extra parameters, we prove the corresponding approximation theorems for our proposed parametric coupling flows named Para-CFlows. In practice, we apply Para-CFlows as a neural surrogate model in contextual Bayesian optimization tasks, to demonstrate its superiority over other neural surrogate models in terms of optimization performance.
Soft Actor-Critic with Inhibitory Networks for Faster Retraining
Venue: arXiv cs.LG 2022
Year: 2022

Reusing previously trained models is critical in deep reinforcement learning to speed up training of new agents. However, it is unclear how to acquire new skills when objectives and constraints are in conflict with previously learned skills. Moreover, when retraining, there is an intrinsic conflict between exploiting what has already been learned and exploring new skills. In soft actor-critic (SAC) methods, a temperature parameter can be dynamically adjusted to weight the action entropy and balance the explore $\times$ exploit trade-off. However, controlling a single coefficient can be challenging within the context of retraining, even more so when goals are contradictory. In this work, inspired by neuroscience research, we propose a novel approach using inhibitory networks to allow separate and adaptive state value evaluations, as well as distinct automatic entropy tuning. Ultimately, our approach allows for controlling inhibition to handle conflict between exploiting less risky, acquired behaviors and exploring novel ones to overcome more challenging tasks. We validate our method through experiments in OpenAI Gym environments.
Prompt-Guided Injection of Conformation to Pre-trained Protein Model
Venue: arXiv cs.AI 2022
Year: 2022

Pre-trained protein models (PTPMs) represent a protein with one fixed embedding and thus are not capable for diverse tasks. For example, protein structures can shift, namely protein folding, between several conformations in various biological processes. To enable PTPMs to produce task-aware representations, we propose to learn interpretable, pluggable and extensible protein prompts as a way of injecting task-related knowledge into PTPMs. In this regard, prior PTPM optimization with the masked language modeling task can be interpreted as learning a sequence prompt (Seq prompt) that enables PTPMs to capture the sequential dependency between amino acids. To incorporate conformational knowledge to PTPMs, we propose an interaction-conformation prompt (IC prompt) that is learned through back-propagation with the protein-protein interaction task. As an instantiation, we present a conformation-aware pre-trained protein model that learns both sequence and interaction-conformation prompts in a multi-task setting. We conduct comprehensive experiments on nine protein datasets. Results confirm our expectation that using the sequence prompt does not hurt PTPMs' performance on sequence-related tasks while incorporating the interaction-conformation prompt significantly improves PTPMs' performance on tasks where conformational knowledge counts. We also show the learned prompts can be combined and extended to deal with new complex tasks.
Improved Bounds for Fractional Online Matching Problems
Venue: arXiv cs.DS 2022
Year: 2022

Online bipartite matching with one-sided arrival and its variants have been extensively studied since the seminal work of Karp, Vazirani, and Vazirani (STOC 1990). Motivated by real-life applications with dynamic market structures, e.g. ride-sharing, two generalizations of the classical one-sided arrival model are proposed to allow non-bipartite graphs and to allow all vertices to arrive online. Namely, online matching with general vertex arrival is introduced by Wang and Wong (ICALP 2015), and fully online matching is introduced by Huang et al. (JACM 2020). In this paper, we study the fractional versions of the two models. We improve three out of the four state-of-the-art upper and lower bounds of the two models. For fully online matching, we design a $0.6$-competitive algorithm and prove no algorithm can be $0.613$-competitive. For online matching with general vertex arrival, we prove no algorithm can be $0.584$-competitive. Moreover, we give an arguably more intuitive algorithm for the general vertex arrival model, compared to the algorithm of Wang and Wong, while attaining the same competitive ratio of $0.526$.
Think-Aloud Verbalizations for Identifying User Experience Problems: Effects of Language Proficiency with Chinese Non-Native English Speakers
Venue: arXiv cs.HC 2022
Year: 2022

Subtle patterns in users' think-aloud (TA) verbalizations (i.e., utterances) are shown to be telltale signs of user experience (UX) problems and used to build artificial intelligence (AI) models or AI-assisted tools to help UX evaluators identify UX problems automatically or semi-automatically. Despite the potential of such verbalization patterns, they were uncovered with native English speakers. As most people who speak English are non-native speakers, it is important to investigate whether similar patterns exist in non-native English speakers' TA verbalizations. As a first step to answer this question, we conducted think-aloud usability testing with Chinese non-native English speakers and native English speakers using three common TA protocols. We compared their verbalizations and UX problems that they encountered to understand the effects of language and TA protocols. Our findings show that both language groups had similar amounts and proportions of verbalization categories, encountered similar problems, and had similar verbalization patterns that indicate UX problems. Furthermore, TA protocols did not significantly affect the correlations between verbalizations and problems. Based on the findings, we present three design implications for UX practitioners and the design of AI-assisted analysis tools.
Multi-Objective Quality Diversity Optimization
Venue: arXiv cs.AI 2022
Year: 2022

In this work, we consider the problem of Quality-Diversity (QD) optimization with multiple objectives. QD algorithms have been proposed to search for a large collection of both diverse and high-performing solutions instead of a single set of local optima. Thriving for diversity was shown to be useful in many industrial and robotics applications. On the other hand, most real-life problems exhibit several potentially antagonist objectives to be optimized. Hence being able to optimize for multiple objectives with an appropriate technique while thriving for diversity is important to many fields. Here, we propose an extension of the MAP-Elites algorithm in the multi-objective setting: Multi-Objective MAP-Elites (MOME). Namely, it combines the diversity inherited from the MAP-Elites grid algorithm with the strength of multi-objective optimizations by filling each cell with a Pareto Front. As such, it allows to extract diverse solutions in the descriptor space while exploring different compromises between objectives. We evaluate our method on several tasks, from standard optimization problems to robotics simulations. Our experimental evaluation shows the ability of MOME to provide diverse solutions while providing global performances similar to standard multi-objective algorithms.
The development of a portable elbow exoskeleton with a Twisted Strings Actuator to assist patients with upper limb inhabitation
Venue: arXiv cs.RO 2022
Year: 2022

Over the years, the number of exoskeleton devices utilized for upper-limb rehabilitation has increased dramatically, each with its own set of pros and cons. Most exoskeletons are not portable, limiting their utility to daily use for house patients. Additionally, the huge size of some grounded exoskeletons consumes space while maintaining a sophisticated structure and require more expensive materials. In other words, to maintain affordability, the device's structure must be simple. Thus, in this work, a portable elbow exoskeleton is developed using SolidWorks to incorporate a Twisted Strings Actuator (TSA) to aid in upper-limb rehabilitation and to provide an alternative for those with compromised limbs to recuperate. Experiments are conducted to identify the optimal value for building a more flexible elbow exoskeleton prototype by analyzing stress, strain conditions, torque, forces, and strings. Preliminary computational findings reveal that for the proposed intended prototype, a string length of.033 m and a torque value ranging from 1.5 Nm to 3 Nm are optimal.
The 6-Ds of Creating AI-Enabled Systems
Venue: arXiv cs.SE 2022
Year: 2022

We are entering our tenth year of the current Artificial Intelligence (AI) spring, and, as with previous AI hype cycles, the threat of an AI winter looms. AI winters occurred because of ineffective approaches towards navigating the technology valley of death. The 6-D framework provides an end-to-end framework to successfully navigate this challenge. The 6-D framework starts with problem decomposition to identify potential AI solutions, and ends with considerations for deployment of AI-enabled systems. Each component of the 6-D framework and a precision medicine use case is described in this paper.
Eager Functions as Processes (long version)
Venue: arXiv cs.LO 2022
Year: 2022

We study Milner's encoding of the call-by-value $λ$-calculus into the $π$-calculus. We show that, by tuning the encoding to two subcalculi of the $π$-calculus (Internal $π$ and Asynchronous Local $π$), the equivalence on $λ$-terms induced by the encoding coincides with Lassen's eager normalform bisimilarity, extended to handle $η$-equality. As behavioural equivalence in the $π$-calculus we consider contextual equivalence and barbed congruence. We also extend the results to preorders. A crucial technical ingredient in the proofs is the recently-introduced technique of unique solutions of equations, further developed in this paper. In this respect, the paper also intends to be an extended case study on the applicability and expressiveness of the technique.
Optical skin: Sensor-integration-free multimodal flexible sensing
Venue: arXiv cs.CV 2022
Year: 2022

The biological skin enables animals to sense various stimuli. Extensive efforts have been made recently to develop smart skin-like sensors to extend the capabilities of biological skins; however, simultaneous sensing of several types of stimuli in a large area remains challenging because this requires large-scale sensor integration with numerous wire connections. We propose a simple, highly sensitive, and multimodal sensing approach, which does not require integrating multiple sensors. The proposed approach is based on an optical interference technique, which can encode the information of various stimuli as a spatial pattern. In contrast to the existing approach, the proposed approach, combined with a deep neural network, enables us to freely select the sensing mode according to our purpose. As a key example, we demonstrate simultaneous sensing mode of three different physical quantities, contact force, contact location, and temperature, using a single soft material without requiring complex integration. Another unique property of the proposed approach is spatially continuous sensing with ultrahigh resolution of few tens of micrometers, which enables identifying the shape of the object in contact. Furthermore, we present a haptic soft device for a human-machine interface. The proposed approach encourages the development of high-performance optical skins.
Deep Learning based Channel Estimation for Massive MIMO with Hybrid Transceivers
Venue: arXiv cs.IT 2022
Year: 2022

Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity.
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations
Venue: arXiv cs.CR 2022
Year: 2022

Due to privacy concerns, multi-party gradient tree boosting algorithms have become widely popular amongst machine learning researchers and practitioners. However, limited existing works have focused on vertically partitioned datasets, and the few existing works are either not scalable or tend to leak information. Thus, in this work, we propose SSXGB which is a scalable and secure multi-party gradient tree boosting framework for vertically partitioned datasets with partially outsourced computations. Specifically, we employ an additive homomorphic encryption (HE) scheme for security. We design two sub-protocols based on the HE scheme to perform non-linear operations associated with gradient tree boosting algorithms. Next, we propose a secure training and a secure prediction algorithms under the SSXGB framework. Then we provide theoretical security and communication analysis for the proposed framework. Finally, we evaluate the performance of the framework with experiments using two real-world datasets.