Reviewerly/exHarmony huggingface dataset:
Neural Relighting and Expression Transfer On Video Portraits

Photo-realistic video portrait reenactment benefits virtual production and numerous VR/AR experiences. The task remains challenging as the reenacted expression should match the source while the lighting should be adjustable to new environments. We present a neural relighting and expression transfer technique to transfer the facial expressions from a source performer to a portrait video of a target performer while enabling dynamic relighting. Our approach employs 4D reflectance field learning, model-based facial performance capture and target-aware neural rendering. Specifically, given a short sequence of the target performer's OLAT, we apply a rendering-to-video translation network to first synthesize the OLAT result of new sequences with unseen expressions. We then design a semantic-aware facial normalization scheme along with a multi-frame multi-task learning strategy to encode the content, segmentation, and motion flows for reliably inferring the reflectance field. This allows us to simultaneously control facial expression and apply virtual relighting. Extensive experiments demonstrate that our technique can robustly handle challenging expressions and lighting environments and produce results at a cinematographic quality.
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart

Correctly classifying adversarial examples is an essential but challenging requirement for safely deploying machine learning models. As reported in RobustBench, even the state-of-the-art adversarially trained models struggle to exceed 67% robust test accuracy on CIFAR-10, which is far from practical. A complementary way towards robustness is to introduce a rejection option, allowing the model to not return predictions on uncertain inputs, where confidence is a commonly used certainty proxy. Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones. This intriguing property sheds light on using coupling strategies to better detect and reject adversarial examples. We evaluate our rectified rejection (RR) module on CIFAR-10, CIFAR-10-C, and CIFAR-100 under several attacks including adaptive ones, and demonstrate that the RR module is compatible with different adversarial training frameworks on improving robustness, with little extra computation. The code is available at https://github.com/P2333/Rectified-Rejection.
Abstract Vergleichsstellensätze for preordered semifields and semirings I

Real algebra is usually thought of as the study of certain kinds of preorders on fields and rings. Among its core themes are the separation theorems known as Positivstellensätze. However, there is a nascent subfield of real algebra which studies preordered semirings and semifields, which is motivated by applications to probability, graph theory and theoretical computer science, among others. Here, we contribute to this subfield by developing a number of foundational results for it, with two abstract Vergleichsstellensätze being our main theorems. Our first Vergleichsstellensatz states that every semifield preorder is the intersection of its total extensions. We apply this to derive our second main result, a Vergleichsstellensatz for certain non-Archimedean preordered semirings in which the homomorphisms to the tropical reals play an important role. We show how this result recovers the existing Vergleichsstellensatz of Strassen and (through the latter) the classical Positivstellensatz of Krivine--Kadison--Dubois.
MetaQA: Combining Expert Agents for Multi-Skill Question Answering

The recent explosion of question answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or by combining multiple models. Despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer candidates. Through quantitative and qualitative experiments we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches in both in-domain and out-of-domain scenarios, ii) is highly data-efficient to train, and iii) can be adapted to any QA format. We release our code and a dataset of answer predictions from expert agents for 16 QA datasets to foster future developments of multi-agent systems on https://github.com/UKPLab/MetaQA.
Fast and Accurate Light Field Saliency Detection through Deep Encoding

Light field saliency detection -- important due to utility in many vision tasks -- still lacks speed and can improve in accuracy. Due to the formulation of the saliency detection problem in light fields as a segmentation task or a memorizing task, existing approaches consume unnecessarily large amounts of computational resources for training, and have longer execution times for testing. We solve this by aggressively reducing the large light field images to a much smaller three-channel feature map appropriate for saliency detection using an RGB image saliency detector with attention mechanisms. We achieve this by introducing a novel convolutional neural network based features extraction and encoding module. Our saliency detector takes $0.4$ s to process a light field of size $9\times9\times512\times375$ in a CPU and is significantly faster than state-of-the-art light field saliency detectors, with better or comparable accuracy. Furthermore, model size of our architecture is significantly lower compared to state-of-the-art light field saliency detectors. Our work shows that extracting features from light fields through aggressive size reduction and the attention mechanism results in a faster and accurate light field saliency detector leading to near real-time light field processing.
How much pre-training is enough to discover a good subnetwork?

Neural network pruning is useful for discovering efficient, high-performing subnetworks within pre-trained, dense network architectures. More often than not, it involves a three-step process -- pre-training, pruning, and re-training -- that is computationally expensive, as the dense model must be fully pre-trained. While previous work has revealed through experiments the relationship between the amount of pre-training and the performance of the pruned network, a theoretical characterization of such dependency is still missing. Aiming to mathematically analyze the amount of dense network pre-training needed for a pruned network to perform well, we discover a simple theoretical bound in the number of gradient descent pre-training iterations on a two-layer, fully-connected network, beyond which pruning via greedy forward selection [61] yields a subnetwork that achieves good training error. Interestingly, this threshold is shown to be logarithmically dependent upon the size of the dataset, meaning that experiments with larger datasets require more pre-training for subnetworks obtained via pruning to perform well. Lastly, we empirically validate our theoretical results on a multi-layer perceptron trained on MNIST.
Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

Mobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target's home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.
Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction

Despite the recent developments in 3D Face Reconstruction from occluded and noisy face images, the performance is still unsatisfactory. Moreover, most existing methods rely on additional dependencies, posing numerous constraints over the training procedure. Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images. The proposed network contains 1) the Guidance Pipeline to obtain the 3D face coefficients for the clean faces and 2) the Robustification Pipeline to acquire the consistency between the estimated coefficients for occluded or noisy images and the clean counterpart. The proposed image- and feature-level loss functions aid the ROGUE learning process without posing additional dependencies. To facilitate model evaluation, we propose two challenging occlusion face datasets, ReaChOcc and SynChOcc, containing real-world and synthetic occlusion-based face images for robustness evaluation. Also, a noisy variant of the test dataset of CelebA is produced for evaluation. Our method outperforms the current state-of-the-art method by large margins (e.g., for the perceptual errors, a reduction of 23.8% for real-world occlusions, 26.4% for synthetic occlusions, and 22.7% for noisy images), demonstrating the effectiveness of the proposed approach. The occlusion datasets and the corresponding evaluation code are released publicly at https://github.com/ArcTrinity9/Datasets-ReaChOcc-and-SynChOcc.
Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution

When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. We conduct an exploratory study to investigate the properties of commit-relevant mutants, i.e., the test elements of commit-aware mutation testing, by offering a general definition and an experimental approach to identify them. We thus, aim at investigating the prevalence, location, comparative advantages of commit-aware mutation testing over time (i.e., the program evolution) and the predictive power of several commit-related features to understand the essential properties for its best-effort application case. Our approach utilizes the impact of mutants and the effects of one mutant on another in capturing and analyzing the implicit interactions between the changed and unchanged code parts. The study analyses millions of mutants (over 10 million), 288 commits, five (5) different open-source software projects involving over 68,213 CPU days of computation and sets a ground truth where we perform our analysis. Our analysis shows that commit-relevant mutants are located mainly outside of program commit change (81%), while an effective selection of commit-relevant mutants can reduce the number of mutants by up to 93%. In addition, we demonstrate that commit relevant mutation testing is significantly more effective and efficient than state-of-the-art baselines. Our analysis of the predictive power of mutants and commit-related features in predicting commit-relevant mutants found that most proxy features do not reliably predict commit-relevant mutants. This empirical study highlights the properties of commit-relevant mutants and demonstrates the importance of identifying and selecting commit-relevant mutants when testing evolving software systems.
On Linear Convergence of Policy Gradient Methods for Finite MDPs

We revisit the finite time analysis of policy gradient methods in the one of the simplest settings: finite state and action MDPs with a policy class consisting of all stochastic policies and with exact gradient evaluations. There has been some recent work viewing this setting as an instance of smooth non-linear optimization problems and showing sub-linear convergence rates with small step-sizes. Here, we take a different perspective based on connections with policy iteration and show that many variants of policy gradient methods succeed with large step-sizes and attain a linear rate of convergence.
A Blockchain-based Data Governance Framework with Privacy Protection and Provenance for e-Prescription

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, and consent management. We introduce a decentralized data governance framework based on blockchain technology and proxy re-encryption to let data owners control and track their data through privacy-enhancing and consent management mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We have used Digital e-prescription as the use case since it has multiple stakeholders and sensitive data while enabling the medical fraternity to manage patients' prescription data, involving patients as data owners, doctors and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Ethereum, and pyUmbral PRE show that the proposed decentralized system guarantees transparency, privacy, and trust with minimal overhead.
Identifying Overly Restrictive Matching Patterns in SMT-based Program Verifiers (extended version)

Universal quantifiers occur frequently in proof obligations produced by program verifiers, for instance, to axiomatize uninterpreted functions and to express properties of arrays. SMT-based verifiers typically reason about them via E-matching, an SMT algorithm that requires syntactic matching patterns to guide the quantifier instantiations. Devising good matching patterns is challenging. In particular, overly restrictive patterns may lead to spurious verification errors if the quantifiers needed for a proof are not instantiated; they may also conceal unsoundness caused by inconsistent axiomatizations. In this paper, we present the first technique that identifies and helps the users remedy the effects of overly restrictive matching patterns. We designed a novel algorithm to synthesize missing triggering terms required to complete a proof. Tool developers can use this information to refine their matching patterns and prevent similar verification errors, or to fix a detected unsoundness.
Contribution of the bceao's monetary policy to price stability in mali from 1970 to 2009

This paper empirically analyses the contribution of the BCEAO's monetary policy to price stability in Mali over the period 1970–2009. In terms of methodology, we used an error correction model (ECM). In general, the results show a relative inefficiency of the BCEAO's monetary policy on price stability in Mali. In addition, an increase in the import price index leads to an increase in the general price level in the short and long term. Similarly, the general price level in Mali remains sensitive to changes in the money supply in the short run, and the effect increases significantly in the long run. Thus, an improvement in the transmission mechanisms of monetary policy in Mali is essential.
INCHI Outreach

Abstract Last November, the InChI TRUST started the release of a newsletter, called InChI OUTREACH. The goal is to share new information about the InChI Project, the InChI Trust and the usage of the tools and technologies that are being created, updated, and maintained. A release 4 to 6 times per year is being planned. Please send any comments, questions, or suggestions to Outreach Director, Rudy Potenzone ( rudy@inchi-trust.org ).
Privacy Protection in Geolocation Monitoring Applications

Active use of geolocation tracking in AI-enabled applications demands to have an understandable and robust privacy policy implemented along with these services. Many already deployed policies may lack important statements to achieve this robustness, and some are too cumbersome for the end users to understand. A privacy policy template has been proposed in this research which is intelligible to the privacy policy developers and end-users. It includes all typical information which a policy should have. Based on this template, two sample privacy policies also have been proposed. The aim of presented project was to help developers make policies that will protect the organization from potential liabilities and help users understand how their data are being collected, stored, and processed in simpler words.
News Communication Value Evaluation Algorithm Based on Deep Neural Network

The evaluation of the value of news communication is an area that deserves more attention, but there has been a dearth of specific research in this area. We must first determine the positioning of the news dissemination activity in order to study the mechanism for evaluating the effect of news dissemination. Fundamentally, assessing the impact of news dissemination is a dialectical balance of accuracy and effectiveness. I can conduct research from the three dimensions of policy orientation, communication orientation, and audience orientation and scientifically evaluate the effect of news communication on a qualitative and quantitative basis, based on the main body of the publisher. With only a small amount of labelled data, a high-quality news propagation value evaluation can be achieved. The value is rationally searched in the era of algorithm recommendation. The traditional “personalised recommendation algorithm” caters to the audience’s interests in one direction, with the goal of gaining their attention and commercial success. The “personalised recommendation algorithm” primarily uses statistical methods in the processing of news information, such as user’s basic information and behaviour information, and does not investigate the attributes and functions of news facts or the quality of news information itself. The research proposed in this paper suggests attempting to design an algorithm based on “news value.” First, it is based on the premise of a rational and caring “public person” who is concerned about the public interest, as well as professional journalistic news selection criteria. “News value” is a theoretical foundation for algorithm design that considers not only the publicity of news but also the audience’s interests. More importantly, this type of algorithm is designed to judge the quality and value of information based on how well the information is understood and then make a decision, returning to the essence of news facts and information.
Functional capabilities of trainees in digital production safety systems

The actions of trainees when working with the digital system of industrial safety training are minimized; they are simple, clear and transparent, do not have a negative impact on the implementation of the processes of instruction, preparation for certification and attestation organized by the resources of the digital system, and do not affect the objectivity of the final result of each of the processes.
Research on simulating the movement track of consumers holding cigarettes by using manipulator

Abstract Consumers’ different smoking habits and movement trajectories in the process of smoking have an important impact on cigarette combustion quality and consumption feeling. By analysing the typical action of holding a cigarette, the position, posture and spatial trajectory of the cigarette were described, and the position and posture of the end effector at the beginning and end of the smoking process were considered. The curve function connecting two endpoints can be selected according to the actual constraints. There are 6 degrees of freedom in space of cigarettes, therefore, in order to simulate the motion trajectory of consumers holding cigarettes, 6R industrial robot can realize the motion trajectory of cigarettes in space. The cigarette is clamped by the cigarette holder installed on the flange at the end of the robot, and the robot drives the cigarette holder to complete the movement track of the cigarette.
Intelligent Robots in Business Processes: A Students’ Perspective

The fourth industrial revolution urges companies to incorporate technologies such as artificial intelligence and robotics in their business processes. The success of emerging technologies to innovate business processes, however, largely depends on the acceptance by employees, e.g., as being the end users for human-robot collaboration. The current paper addresses a yet under investigated technology, namely robotics with more advanced levels of intelligence. The existing literature about intelligent robots is still scarce and mainly limited to applications in particular sectors. Since intelligent robot applications will increase in the medium term, our purpose is to add an interdisciplinary perspective by examining the degree to which acceptance differs among master students as highly-skilled future employees. Our survey shows that gender and academic discipline only have a limited impact to predict the acceptance of intelligent robots at work, but different adoption profiles seem to matter most for robotic process changes. Finally, we discuss the role of students, universities and employees to better prepare for the future digitalization of the workplace.
A study of Augmented Reality as a teaching and learning technology in the field of Design

This paper aims to describe the results of a study about the effectiveness of Augmented Reality (AR) as a learning tool in Design. We proposed a design experiment to assess whether AR could improve learning in introductory drawing lessons. We used a sample of 60 students (N=60) from a technical school in the west of greater São Paulo for this purpose. The sample was divided into two groups: the control group (using just drawing regular tools) and the experimental group (using AR as an additional tool). Three expert reviewers were recruited to evaluate the final design of the participants. The reviewer evaluation results showed no statistical significance regarding reviewers' evaluation between control and experimental groups; however, a survey with the same students showed a statistical significance in their preference for using AR. A qualitative survey with participants also showed a preference for using AR. Based on those results, we argue that although the results did not show a significant difference from the evaluators' point of view, the students' perception suggests that AR can be an important auxiliary tool in the learning process.