OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Approximate Search for Sets of Vectors
Venue: arXiv cs.DS 2021
Year: 2021

We consider a similarity measure between two sets $A$ and $B$ of vectors, that balances the average and maximum cosine distance between pairs of vectors, one from set $A$ and one from set $B$. As a motivation for this measure, we present lineage tracking in a database. To practically realize this measure, we need an approximate search algorithm that given a set of vectors $A$ and sets of vectors $B_1,...,B_n$, the algorithm quickly locates the set $B_i$ that maximizes the similarity measure. For the case where all sets are singleton sets, essentially each is a single vector, there are known efficient approximate search algorithms, e.g., approximated versions of tree search algorithms, locality-sensitive hashing (LSH), vector quantization (VQ) and proximity graph algorithms. In this work, we present approximate search algorithms for the general case. The underlying idea in these algorithms is encoding a set of vectors via a "long" single vector. The proposed approximate approach achieves significant performance gains over an optimized, exact search on vector sets.
YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Soft biometrics inference in surveillance scenarios is a topic of interest for various applications, particularly in security-related areas. However, soft biometric analysis is not extensively reported in wild conditions. In particular, previous works on gender recognition report their results in face datasets, with relatively good image quality and frontal poses. Given the uncertainty of the availability of the facial region in wild conditions, we consider that these methods are not adequate for surveillance settings. To overcome these limitations, we: 1) present frontal and wild face versions of three well-known surveillance datasets; and 2) propose YinYang-Net (YY-Net), a model that effectively and dynamically complements facial and body information, which makes it suitable for gender recognition in wild conditions. The frontal and wild face datasets derive from widely used Pedestrian Attribute Recognition (PAR) sets (PETA, PA-100K, and RAP), using a pose-based approach to filter the frontal samples and facial regions. This approach retrieves the facial region of images with varying image/subject conditions, where the state-of-the-art face detectors often fail. YY-Net combines facial and body information through a learnable fusion matrix and a channel-attention sub-network, focusing on the most influential body parts according to the specific image/subject features. We compare it with five PAR methods, consistently obtaining state-of-the-art results on gender recognition, and reducing the prediction errors by up to 24% in frontal samples. The announced PAR datasets versions and YY-Net serve as the basis for wild soft biometrics classification and are available in https://github.com/Tiago-Roxo.
Digital Passport and Visa Asset Management Using Private and Permissioned Blockchain
Venue: arXiv cs.CR 2021
Year: 2021

Blockchain is currently one of the fastest-growing technologies in the field of Computer Science. It has found a prevalent use in financial applications like cryptocurrency, for example, Bitcoin and Ethereum. They have been able to bring an unforeseen disruption in the field of finance. However, permissionless Blockchains like these have some downsides, namely the computation cost of the Proof of Work algorithm, maximum allowed size for a block, decrease in intelligibility with the increase of the number of blocks in the chain, domination of nodes with higher computing power as miners and validators. These factors have restricted the adoption of permissionless blockchain technology outside the field of finance, such as in medical or legal fields. This paper proposes a solution to these problems using a permissioned blockchain. It does not require a computationally expensive consensus mechanism as permissioned chains call for trust between participating organizations which is achieved via exclusive invitations. We have utilized a third-party orderer to maintain the trust between organizations.
A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
Venue: arXiv cs.LG 2021
Year: 2021

Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson's disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.
DULA: A Differentiable Ergonomics Model for Postural Optimization in Physical HRI
Venue: arXiv cs.RO 2021
Year: 2021

Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an important step in providing feedback for postural correction to improve operator health and comfort. In order to enable efficient computation, previously proposed automated ergonomic assessment and correction tools make approximations or simplifications to gold-standard assessment tools used by ergonomists in practice. In order to retain assessment quality, while improving computational considerations, we introduce DULA, a differentiable and continuous ergonomics model learned to replicate the popular and scientifically validated RULA assessment. We show that DULA provides assessment comparable to RULA while providing computational benefits. We highlight DULA's strength in a demonstration of gradient-based postural optimization for a simulated teleoperation task.
"How to best say it?" : Translating Directives in Machine Language into Natural Language in the Blocks World
Venue: arXiv cs.CL 2021
Year: 2021

We propose a method to generate optimal natural language for block placement directives generated by a machine's planner during human-agent interactions in the blocks world. A non user-friendly machine directive, e.g., move(ObjId, toPos), is transformed into visually and contextually grounded referring expressions that are much easier for the user to comprehend. We describe an algorithm that progressively and generatively transforms the machine's directive in ECI (Elementary Composable Ideas)-space, generating many alternative versions of the directive. We then define a cost function to evaluate the ease of comprehension of these alternatives and select the best option. The parameters for this cost function were derived empirically from a user study that measured utterance-to-action timings.
Counting list homomorphisms from graphs of bounded treewidth: tight complexity bounds
Venue: arXiv cs.CC 2021
Year: 2021

The goal of this work is to give precise bounds on the counting complexity of a family of generalized coloring problems (list homomorphisms) on bounded-treewidth graphs. Given graphs $G$, $H$, and lists $L(v)\subseteq V(H)$ for every $v\in V(G)$, a {\em list homomorphism} is a function $f:V(G)\to V(H)$ that preserves the edges (i.e., $uv\in E(G)$ implies $f(u)f(v)\in E(H)$) and respects the lists (i.e., $f(v)\in L(v))$. Standard techniques show that if $G$ is given with a tree decomposition of width $t$, then the number of list homomorphisms can be counted in time $|V(H)|^t\cdot n^{\mathcal{O}(1)}$. Our main result is determining, for every fixed graph $H$, how much the base $|V(H)|$ in the running time can be improved. For a connected graph $H$ we define $\operatorname{irr}(H)$ the following way: if $H$ has a loop or is nonbipartite, then $\operatorname{irr}(H)$ is the maximum size of a set $S\subseteq V(H)$ where any two vertices have different neighborhoods; if $H$ is bipartite, then $\operatorname{irr}(H)$ is the maximum size of such a set that is fully in one of the bipartition classes. For disconnected $H$, we define $\operatorname{irr}(H)$ as the maximum of $\operatorname{irr}(C)$ over every connected component $C$ of $H$. We show that, for every fixed graph $H$, the number of list homomorphisms from $(G,L)$ to $H$ * can be counted in time $\operatorname{irr}(H)^t\cdot n^{\mathcal{O}(1)}$ if a tree decomposition of $G$ having width at most $t$ is given in the input, and * cannot be counted in time $(\operatorname{irr}(H)-ε)^t\cdot n^{\mathcal{O}(1)}$ for any $ε>0$, even if a tree decomposition of $G$ having width at most $t$ is given in the input, unless the #SETH fails. Thereby we give a precise and complete complexity classification featuring matching upper and lower bounds for all target graphs with or without loops.
A Field Guide to Federated Optimization
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.
A Byzantine Fault-Tolerant Consensus Library for Hyperledger Fabric
Venue: arXiv cs.DC 2021
Year: 2021

Hyperledger Fabric is an enterprise grade permissioned distributed ledger platform that offers modularity for a broad set of industry use cases. One modular component is a pluggable ordering service that establishes consensus on the order of transactions and batches them into blocks. However, as of the time of this writing, there is no production grade Byzantine Fault-Tolerant (BFT) ordering service for Fabric, with the latest version (v2.1) supporting only Crash Fault-Tolerance (CFT). In our work, we address crucial aspects of BFT integration into Fabric that were left unsolved in all prior works, making them unfit for production use. In this work we describe the design and implementation of a BFT ordering service for Fabric, employing a new BFT consensus library. The new library, based on the BFT-Smart protocol and written in Go, is tailored to the blockchain use-case, yet is general enough to cater to a wide variety of other uses. We evaluate the new BFT ordering service by comparing it with the currently supported Raft-based CFT ordering service in Hyperledger Fabric.
Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting
Venue: arXiv cs.CV 2021
Year: 2021

The search for specific objects or motifs is essential to art history as both assist in decoding the meaning of artworks. Digitization has produced large art collections, but manual methods prove to be insufficient to analyze them. In the following, we introduce an algorithm that allows users to search for image regions containing specific motifs or objects and find similar regions in an extensive dataset, helping art historians to analyze large digitized art collections. Computer vision has presented efficient methods for visual instance retrieval across photographs. However, applied to art collections, they reveal severe deficiencies because of diverse motifs and massive domain shifts induced by differences in techniques, materials, and styles. In this paper, we present a multi-style feature fusion approach that successfully reduces the domain gap and improves retrieval results without labelled data or curated image collections. Our region-based voting with GPU-accelerated approximate nearest-neighbour search allows us to find and localize even small motifs within an extensive dataset in a few seconds. We obtain state-of-the-art results on the Brueghel dataset and demonstrate its generalization to inhomogeneous collections with a large number of distractors.
Online Allocation and Display Ads Optimization with Surplus Supply
Venue: arXiv cs.GT 2021
Year: 2021

In this work, we study a scenario where a publisher seeks to maximize its total revenue across two sales channels: guaranteed contracts that promise to deliver a certain number of impressions to the advertisers, and spot demands through an Ad Exchange. On the one hand, if a guaranteed contract is not fully delivered, it incurs a penalty for the publisher. On the other hand, the publisher might be able to sell an impression at a high price in the Ad Exchange. How does a publisher maximize its total revenue as a sum of the revenue from the Ad Exchange and the loss from the under-delivery penalty? We study this problem parameterized by \emph{supply factor $f$}: a notion we introduce that, intuitively, captures the number of times a publisher can satisfy all its guaranteed contracts given its inventory supply. In this work we present a fast simple deterministic algorithm with the optimal competitive ratio. The algorithm and the optimal competitive ratio are a function of the supply factor, penalty, and the distribution of the bids in the Ad Exchange. Beyond the yield optimization problem, classic online allocation problems such as online bipartite matching of [Karp-Vazirani-Vazirani '90] and its vertex-weighted variant of [Aggarwal et al. '11] can be studied in the presence of the additional supply guaranteed by the supply factor. We show that a supply factor of $f$ improves the approximation factors from $1-1/e$ to $f-fe^{-1/f}$. Our approximation factor is tight and approaches $1$ as $f \to \infty$.
Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing
Venue: arXiv cs.CL 2021
Year: 2021

Automated writing evaluation systems can improve students' writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e.g., surface vs. content) rather than the extent to which revisions actually respond to the feedback provided and improve the essay. We introduce an annotation scheme to capture the nature of sentence-level revisions of evidence use and reasoning (the `RER' scheme) and apply it to 5th- and 6th-grade students' argumentative essays. We show that reliable manual annotation can be achieved and that revision annotations correlate with a holistic assessment of essay improvement in line with the feedback provided. Furthermore, we explore the feasibility of automatically classifying revisions according to our scheme.
Reuse of Semantic Models for Emerging Smart Grids Applications
Venue: arXiv cs.DC 2021
Year: 2021

Data in the energy domain grows at unprecedented rates. Despite the great potential that IoT platforms and other big data-driven technologies have brought in the energy sector, data exchange and data integration are still not wholly achieved. As a result, fragmented applications are developed against energy data silos, and data exchange is limited to few applications. Therefore, this paper identifies semantic models that can be reused for building interoperable energy management services and applications. The ambition is to innovate the Institute Mihajlo Pupin proprietary SCADA system and to enable integration of the Institute Mihajlo Pupin services and applications in the European Union (EU) Energy Data Space. The selection of reusable models has been done based on a set of scenarios related to electricity balancing services, predictive maintenance services, and services for the residential, commercial and industrial sectors.
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments
Venue: arXiv cs.AI 2021
Year: 2021

Reinforcement Learning (RL) is known to be often unsuccessful in environments with sparse extrinsic rewards. A possible countermeasure is to endow RL agents with an intrinsic reward function, or 'intrinsic motivation', which rewards the agent based on certain features of the current sensor state. An intrinsic reward function based on the principle of empowerment assigns rewards proportional to the amount of control the agent has over its own sensors. We implemented a variation on a recently proposed intrinsically motivated agent, which we refer to as the 'curious' agent, and an empowerment-inspired agent. The former leverages sensor state encoding with a variational autoencoder, while the latter predicts the next sensor state via a variational information bottleneck. We compared the performance of both agents to that of an advantage actor-critic baseline in four sparse reward grid worlds. Both the empowerment agent and its curious competitor seem to benefit to similar extents from their intrinsic rewards. This provides some experimental support to the conjecture that empowerment can be used to drive exploration.
Mitigating Memorization in Sample Selection for Learning with Noisy Labels
Venue: arXiv cs.LG 2021
Year: 2021

Because deep learning is vulnerable to noisy labels, sample selection techniques, which train networks with only clean labeled data, have attracted a great attention. However, if the labels are dominantly corrupted by few classes, these noisy samples are called dominant-noisy-labeled samples, the network also learns dominant-noisy-labeled samples rapidly via content-aware optimization. In this study, we propose a compelling criteria to penalize dominant-noisy-labeled samples intensively through class-wise penalty labels. By averaging prediction confidences for the each observed label, we obtain suitable penalty labels that have high values if the labels are largely corrupted by some classes. Experiments were performed using benchmarks (CIFAR-10, CIFAR-100, Tiny-ImageNet) and real-world datasets (ANIMAL-10N, Clothing1M) to evaluate the proposed criteria in various scenarios with different noise rates. Using the proposed sample selection, the learning process of the network becomes significantly robust to noisy labels compared to existing methods in several noise types.
Classifying Component Function in Product Assemblies with Graph Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Function-based design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learning from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F${_1}$-score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems and Design-for-X consideration in function-based design.
Expert Graphs: Synthesizing New Expertise via Collaboration
Venue: arXiv cs.LG 2021
Year: 2021

Consider multiple experts with overlapping expertise working on a classification problem under uncertain input. What constitutes a consistent set of opinions? How can we predict the opinions of experts on missing sub-domains? In this paper, we define a framework of to analyze this problem, termed "expert graphs." In an expert graph, vertices represent classes and edges represent binary opinions on the topics of their vertices. We derive necessary conditions for expert graph validity and use them to create "synthetic experts" which describe opinions consistent with the observed opinions of other experts. We show this framework to be equivalent to the well-studied linear ordering polytope. We show our conditions are not sufficient for describing all expert graphs on cliques, but are sufficient for cycles.
NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming
Venue: arXiv cs.LG 2021
Year: 2021

Video streaming services strive to support high-quality videos at higher resolutions and frame rates to improve the quality of experience (QoE). However, high-quality videos consume considerable amounts of energy on mobile devices. This paper proposes NeuSaver, which reduces the power consumption of mobile devices when streaming videos by applying an adaptive frame rate to each video chunk without compromising user experience. NeuSaver generates an optimal policy that determines the appropriate frame rate for each video chunk using reinforcement learning (RL). The RL model automatically learns the policy that maximizes the QoE goals based on previous observations. NeuSaver also uses an asynchronous advantage actor-critic algorithm to reinforce the RL model quickly and robustly. Streaming servers that support NeuSaver preprocesses videos into segments with various frame rates, which is similar to the process of creating videos with multiple bit rates in dynamic adaptive streaming over HTTP. NeuSaver utilizes the commonly used H.264 video codec. We evaluated NeuSaver in various experiments and a user study through four video categories along with the state-of-the-art model. Our experiments showed that NeuSaver effectively reduces the power consumption of mobile devices when streaming video by an average of 16.14% and up to 23.12% while achieving high QoE.