Reviewerly/exHarmony huggingface dataset:
Active Vision for Early Recognition of Human Actions

We propose a method for early recognition of human actions, one that can take advantages of multiple cameras while satisfying the constraints due to limited communication bandwidth and processing power. Our method considers multiple cameras, and at each time step, it will decide the best camera to use so that a confident recognition decision can be reached as soon as possible. We formulate the camera selection problem as a sequential decision process, and learn a view selection policy based on reinforcement learning. We also develop a novel recurrent neural network architecture to account for the unobserved video frames and the irregular intervals between the observed frames. Experiments on three datasets demonstrate the effectiveness of our approach for early recognition of human actions.
An ego network analysis of sextortionists

We consider a particular instance of user interactions in the Bitcoin network, that of interactions among wallet addresses belonging to scammers. Aggregation of multiple inputs and change addresses are common heuristics used to establish relationships among addresses and analyze transaction amounts in the Bitcoin network. We propose a flow centric approach that complements such heuristics, by studying the branching, merger and propagation of Bitcoin flows. We study a recent sextortion campaign by exploring the ego network of known offending wallet addresses. We compare and combine different existing and new heuristics, which allows us to identify (1) Bitcoin addresses of interest (including possible recurrent go-to addresses for the scammers) and (2) relevant Bitcoin flows, from scam Bitcoin addresses to a Binance exchange and to other other scam addresses, that suggest connections among prima facie disparate waves of similar scams.
Sulphide capacity prediction of CaO–SiO<sub>2</sub>–MgO–Al<sub>2</sub>O<sub>3</sub> slag system by using regularized extreme learning machine

Desulphurization is essential in the steelmaking process for high-quality steel production, and sulphide capacity has proven to be an effective index to evaluate the desulphurization ability of molten slag or flux. Several analytical or empirical models have been proposed to calculate the sulphide capacity. However, these models usually show insufficient generalization ability when new variables/data are introduced, which limits their practical application. In this work, experimental data were collected from the literature and a regularized extreme learning machine (RELM) model was established to predict the sulphide capacity of the CaO–SiO2–MgO–Al2O3 slag system. The results demonstrated that the proposed model is robust for the prediction of sulphide capacity under different conditions. The coefficient of determination (R2), correlation coefficient (r), root-mean-square error (RMSE) of the optimal model reached 0.9763, 0.9881, 0.113, respectively, which outperform the results of the reported models.
Quantum Computer Architecture Toward Full-Stack Quantum Accelerators

This article presents the definition and implementation of a quantum computer architecture to enable creating a new computational device-a quantum computer as an accelerator. A key question addressed is what such a quantum computer is and how it relates to the classical processor that controls the entire execution process. In this article, we present explicitly the idea of a quantum accelerator that contains the full stack of the layers of an accelerator. Such a stack starts at the highest level describing the target application of the accelerator. The next layer abstracts the quantum logic outlining the algorithm that is to be executed on the quantum accelerator. In our case, the logic is expressed in the universal quantum-classical hybrid computation language developed in the group, called OpenQL, which visualized the quantum processor as a computational accelerator. The OpenQL compiler translates the program to a common assembly language, called cQASM, which can be executed on a quantum simulator. The cQASM represents the instruction set that can be executed by the microarchitecture implemented in the quantum accelerator. In a subsequent step, the compiler can convert the cQASM to generate the eQASM, which is executable on a particular experimental device incorporating the platform-specific parameters. This way, we are able to distinguish clearly the experimental research toward better qubits, and the industrial and societal applications that need to be developed and executed on a quantum device. The first case offers experimental physicists with a full-stack experimental platform using realistic qubits with decoherence and error-rates, whereas the second case offers perfect qubits to the quantum application developer, where there is neither decoherence nor error-rates. We conclude the article by explicitly presenting three examples of full-stack quantum accelerators, for an experimental superconducting processor, for quantum accelerated genome sequencing and for near-term generic optimization problems based on quantum heuristic approaches. The two later full-stack models are currently being actively researched in our group.
Stereoscopic Flash and No-Flash Photography for Shape and Albedo Recovery

We present a minimal imaging setup that harnesses both geometric and photometric approaches for shape and albedo recovery. We adopt a stereo camera and a flashlight to capture a stereo image pair and a flash/no-flash pair. From the stereo image pair, we recover a rough shape that captures low-frequency shape variation without high-frequency details. From the flash/no-flash pair, we derive an image formation model for Lambertian objects under natural lighting, based on which a fine normal map is obtained and fused with the rough shape. Further, we use the flash/no-flash pair for cast shadow detection and albedo canceling, making the shape recovery robust against shadows and albedo variation. We verify the effectiveness of our approach on both synthetic and real-world data.
Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization

Non-factoid question answering (QA) is one of the most extensive yet challenging application and research areas of retrieval-based question answering. In particular, answers to non-factoid questions can often be too lengthy and redundant to comprehend, which leads to the great demand on answer sumamrization in non-factoid QA. However, the multi-level interactions between QA pairs and the interrelation among different answer sentences are usually modeled separately on current answer summarization studies. In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization. Specifically, we design a hierarchical compare-aggregate method to integrate the interaction between QA pairs in both word-level and sentence-level into the final question and answer representations. After that, we conduct the question-aware sequential extractor to produce a summary for the lengthy answer. Experimental results show that answer summarization benefits from both hierarchical and sequential context modeling and our method achieves superior performance on WikiHowQA and PubMedQA.
How to Ask What to Say?: Strategies for Evaluating Natural Language Interfaces for Data Visualization

In this article, we discuss challenges and strategies for evaluating natural language interfaces (NLIs) for data visualization. Through an examination of prior studies and reflecting on own experiences in evaluating visualization NLIs, we highlight benefits and considerations of three task framing strategies: Jeopardy-style facts, open-ended tasks, and target replication tasks. We hope the discussions in this article can guide future researchers working on visualization NLIs and help them avoid common challenges and pitfalls when evaluating these systems. Finally, to motivate future research, we highlight topics that call for further investigation including development of new evaluation metrics, and considering the type of natural language input (spoken versus typed), among others.
Using Player's Body-Orientation to Model Pass Feasibility in Soccer

Given a monocular video of a soccer match, this paper presents a computational model to estimate the most feasible pass at any given time. The method leverages offensive player's orientation (plus their location) and opponents' spatial configuration to compute the feasibility of pass events within players of the same team. Orientation data is gathered from body pose estimations that are properly projected onto the 2D game field; moreover, a geometrical solution is provided, through the definition of a feasibility measure, to determine which players are better oriented towards each other. Once analyzed more than 6000 pass events, results show that, by including orientation as a feasibility measure, a robust computational model can be built, reaching more than 0.7 Top-3 accuracy. Finally, the combination of the orientation feasibility measure with the recently introduced Expected Possession Value metric is studied; promising results are obtained, thus showing that existing models can be refined by using orientation as a key feature. These models could help both coaches and analysts to have a better understanding of the game and to improve the players' decision-making process.
Concentration on the Boolean hypercube via pathwise stochastic analysis

We develop a new technique for proving concentration inequalities which relate between the variance and influences of Boolean functions.
Further Non-local and Channel Attention Networks for Vehicle Re-identification

Vehicle re-identification remains challenging due to large intra-class difference and small inter-class variance. To address this problem, in AICity Vehicle Re-ID task 2020, we propose a two-branch adaptive attention network-Further Non-local and Channel attention (FNC) to improve feature representation and discrimination. Specifically, inspired by two-stream theory of visual cortex, based on Non-local and channel relation, a two-branch FNC network is constructed to capture multiple useful information. Second, an effective attention fusion method is proposed to sufficiently model the effects from spatial and channel attention. The experimental results show that our algorithm achieves 66.25%/Rank-1 and 53.54%/mAP in 2020 AIC-ity Challenge Vehicle Re-ID task without using extra data, annotation and other auxiliary information, which demonstrate the effectiveness of the proposed FNC network.
Superkernel Neural Architecture Search for Image Denoising

Recent advancements in Neural Architecture Search (NAS) resulted in finding new state-of-the-art Artificial Neural Network (ANN) solutions for tasks like image classification, object detection, or semantic segmentation without substantial human supervision. In this paper, we focus on exploring NAS for a dense prediction task that is image denoising. Due to a costly training procedure, most NAS solutions for image enhancement rely on reinforcement learning or evolutionary algorithm exploration, which usually take weeks (or even months) to train. Therefore, we introduce a new efficient implementation of various superkernel techniques that enable fast (6-8 RTX2080 GPU hours) single-shot training of models for dense predictions. We demonstrate the effectiveness of our method on the SIDD+ benchmark for image denoising [3].
Towards Geostatistical Learning for the Geosciences: A Case Study in Improving the Spatial Awareness of Spectral Clustering

Abstract The particularities of geosystems and geoscience data must be understood before any development or implementation of statistical learning algorithms. Without such knowledge, the predictions and inferences may not be accurate and physically consistent. Accuracy, transparency and interpretability, credibility, and physical realism are minimum criteria for statistical learning algorithms when applied to the geosciences. This study briefly reviews several characteristics of geoscience data and challenges for novel statistical learning algorithms. A novel spatial spectral clustering approach is introduced to illustrate how statistical learners can be adapted for modelling geoscience data. The spatial awareness and physical realism of the spectral clustering are improved by utilising a dissimilarity matrix based on nonparametric higher-order spatial statistics. The proposed model-free technique can identify meaningful spatial clusters (i.e. meaningful geographical subregions) from multivariate spatial data at different scales without the need to define a model of co-dependence. Several mixed (e.g. continuous and categorical) variables can be used as inputs to the proposed clustering technique. The proposed technique is illustrated using synthetic and real mining datasets. The results of the case studies confirm the usefulness of the proposed method for modelling spatial data.
Contextual Re-Ranking with Behavior Aware Transformers

In this work, we focus on the contextual document ranking task, which deals with the challenge of user interaction modeling for conversational search. Given a history of user feedback behaviors, such as issuing a query, clicking a document, and skipping a document, we propose to introduce behavior awareness to a neural ranker, resulting in a Hierarchical Behavior Aware Transformers (HBA-Transformers) model. The hierarchy is composed of an intra-behavior attention layer and an inter-behavior attention layer to let the system effectively distinguish and model different user behaviors. Our extensive experiments on the AOL session dataset demonstrate that the hierarchical behavior aware architecture is more powerful than a simple combination of history behaviors. Besides, we analyze the conversational property of queries. We show that coherent sessions tend to be more conversational and thus are more demanding in terms of considering history user behaviors.
Does preprocessing help in fast sequence comparisons?

We study edit distance computation with preprocessing: the preprocessing algorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed strings. This model is inspired by scenarios where we would like to compute edit distance between many pairs in the same pool of strings.
Modelling of a Nonlinear Fuzzy Three-Input PID Controller and Its Simulation and Experimental Realization

Over the last three decades, finding the mathematical models of fuzzy controllers has become an interesting area of research in the control community. The mathematical model of a controller gives a clear insight into the analytical structure of a controller and helps to understand the control problem in the framework of well-defined control theory. Because of modelling and computational complexities, from the literature, it seems to the authors that modelling of three-input fuzzy PID controller has rarely been attempted using the Center of Gravity (CoG) defuzzification. The purpose of the present manuscript is to unveil the exact mathematical model of a nonlinear fuzzy three-input PID controller using CoG defuzzification and to investigate its properties. To justify the theoretical development made in this manuscript, the applicability of the proposed controller is shown through simulation and real-time studies. In the simulation, a nonlinear plant, an unstable plant with large time-delay, and a higher-order plant with dead-time and inverse response are controlled. Whereas in real-time an unstable nonlinear Magnetic levitation (Maglev) plant is controlled. As the proposed controller structure does not depend on plant dynamics, it can easily be implemented for other control applications also.
Attribute Aware Filter-Drop for Bias-Invariant Classification

The widespread applicability of deep learning based algorithms demands dedicated attention towards ensuring unbiased behavior. Biased feature learning (for or against a particular sub-group) might often result in unfair predictions. In order to address the above issue, this research proposes a novel Filter-Drop algorithm for learning unbiased representations. The proposed technique focuses on learning the features useful for predicting the biasing attribute (or the sensitive attribute), followed by their elimination while performing the primary classification task. To this effect, a multi-task network is trained, which prevents the features capturing the attribute variations from being used for the primary classification task. The efficacy of the proposed Filter-Drop technique is demonstrated on two facial analysis datasets: UTKFace dataset and FairFace dataset. The proposed technique achieves similar performance across different ethnicity groups while training with highly skewed training data as well.
Read, Attend, and Exclude: Multi-Choice Reading Comprehension by Mimicking Human Reasoning Process

Multi-Choice Reading Comprehension~(MCRC) is an essential task where a machine selects the correct answer from multiple choices given a context document and a corresponding question. Existing methods usually make predictions based on a single-round reasoning process with the attention mechanism, however, this may be insufficient for tasks that require a more complex reasoning process. To effectively comprehend the context and select the correct answer from different perspectives, we propose the Read-Attend-Exclude (RAE) model which is motivated by what human readers do for MCRC in multi-rounds reasoning process. Specifically, the RAE model includes four components: the Scan Reading Module, the Attended Intensive Reading Module, the Answer Exclusion Module, and the Gated Fusion Module that makes the final decisions collectively based on the aforementioned three modules. Extensive experiments demonstrate the strong results of the proposed model on the DREAM dataset and the effectiveness of all proposed modules.
BlockChain to Prevent Fraudulent Activities: Buying and Selling Property Using BlockChain

Blockchain can have umpteen number of uses, one of them being its use in Real Estate. Blockchain and Real Estate go hand in hand. The kind of security and transparency needed in Real Estate is exceptionally provided by Blockchain. Consider a scenario where you buy a house. The proof that the property is owned by you is the deed or an agreement signed by the government which is physically present on a piece of paper and the records maintained by the government in the ledger. The procedure to this is simple; you buy the house, get it registered at the government office where they note it down in a register/ledger and you’ve got yourself a house. If the page in which your name is registered is torn or damaged, you might still own the house but apart from the deed, there will be no other concrete proof that the house is owned by you. So, you need to hope that you don’t lose the deed or someone doesn’t damage or misplace the government ledger. That’s the kind of guarantee you have. We propose a system which will help us to solve this problem. Suppose you wish to buy or sell property, you can provide the requirements asked for by the smart contract and get yourself a deed digitally which is uploaded as a new block in the chain. The copy of each node will be present on several servers which helps us to maintain integrity in the case of an attack or system failure.
Good Evaluation Measures based on Document Preferences

For offline evaluation of IR systems, some researchers have proposed to utilise pairwise document preference assessments instead of relevance assessments of individual documents, as it may be easier for assessors to make relative decisions rather than absolute ones. Simple preference-based evaluation measures such as ppref and wpref have been proposed, but the past decade did not see any wide use of such measures. One reason for this may be that, while these new measures have been reported to behave more or less similarly to traditional measures based on absolute assessments, whether they actually align with the users' perception of search engine result pages (SERPs) has been unknown. The present study addresses exactly this question, after formally defining two classes of preference-based measures called Pref measures and Δ-measures. We show that the best of these measures perform at least as well as an average assessor in terms of agreement with users' SERP preferences, and that implicit document preferences (i.e., those suggested by a SERP that retrieves one document but not the other) play a much more important role than explicit preferences (i.e., those suggested by a SERP that retrieves one document above the other). We have released our data set containing 119,646 document preferences, so that the feasibility of document preferenced-based evaluation can be further pursued by the IR community.
A Local Flatness Based Variational Approach to Retinex

A topic of continued interest in Retinex over the years has been finding ways to implement it with computational models of improved accuracy and efficiency. We have devised a new approach to digitally implementing the Retinex using a local deviation based variational model. The new model leads to improvements in the computed image quality with respect to illumination correction and image enhancement. Several contributions are made: 1) a new prior constraint, which we call local flatness, is proposed, and a new measure of Local Deviation (LD) is developed to quantify the degree of local illumination flatness; 2) a variational problem is defined and the solution is found by a logical sequence of steps; 3) discrete implementation of the variational solution is shown to effectively estimate and remove uneven illumination, yielding an accurate recovered image. Unlike other physical prior based variational Retinex models, which use the L2 norm of the illumination gradient to enforce smoothness of illumination, our LD prior selectively imposes local flatness on illumination by calculating the deviation between the estimated illumination surface to a reference plane. In the experiments, pseudo ground truth images are created by superimposing uneven illumination on real scenes, providing an effective way to objectively assess algorithm performance. The experimental results show that our method can reconstruct more accurate recovered images than other state-of-the-art methods, while maintaining good contrast.
Sampling Bias Due to Near-Duplicates in Learning to Rank

Learning to rank~(LTR) is the de facto standard for web search, improving upon classical retrieval models by exploiting (in)direct relevance feedback from user judgments, interaction logs, etc. We investigate for the first time the effect of a sampling bias on LTR~models due to the potential presence of near-duplicate web pages in the training data, and how (in)consistent relevance feedback of duplicates influences an LTR~model's decisions. To examine this bias, we construct a series of specialized LTR~datasets based on the ClueWeb09 corpus with varying amounts of near-duplicates. We devise worst-case and average-case train/test splits that are evaluated on popular pointwise, pairwise, and listwise LTR~models. Our experiments demonstrate that duplication causes overfitting and thus less effective models, making a strong case for the benefits of systematic deduplication before training and model evaluation.