Reviewerly/exHarmony huggingface dataset:
Structure-Aware Private Set Intersection, with Applications to Fuzzy Matching

In two-party private set intersection (PSI), Alice holds a set X, Bob holds a set Y, and they learn (only) the contents of $$X \cap Y$$ . We introduce structure-aware PSI protocols, which take advantage of situations where Alice's set X is publicly known to have a certain structure. The goal of structure-aware PSI is to have communication that scales with the description size of Alice's set, rather its cardinality. We introduce a new generic paradigm for structure-aware PSI based on function secret-sharing (FSS). In short, if there exists compact FSS for a class of structured sets, then there exists a semi-honest PSI protocol that supports this class of input sets, with communication cost proportional only to the FSS share size. Several prior protocols for efficient (plain) PSI can be viewed as special cases of our new paradigm, with an implicit FSS for unstructured sets. Our PSI protocol can be instantiated from a significantly weaker flavor of FSS, which has not been previously studied. We develop several improved FSS techniques that take advantage of these relaxed requirements, and which are in some cases exponentially better than existing FSS. Finally, we explore in depth a natural application of structure-aware PSI. If Alice's set X is the union of many radius- $$\delta $$ balls in some metric space, then an intersection between X and Y corresponds to fuzzy PSI, in which the parties learn which of their points are within distance $$\delta $$ . In structure-aware PSI, the communication cost scales with the number of balls in Alice's set, rather than their total volume. Our techniques lead to efficient fuzzy PSI for $$\ell _\infty $$ and $$\ell _1$$ metrics (and approximations of $$\ell _2$$ metric) in high dimensions. We implemented this fuzzy PSI protocol for 2-dimensional $$\ell _\infty $$ metrics. For reasonable input sizes, our protocol requires 45–60% less time and 85% less communication than competing approaches that simply reduce the problem to plain PSI.
Hourglass Attention Network for Image Inpainting

Benefiting from the powerful ability of convolutional neural networks (CNNs) to learn semantic information and texture patterns of images, learning-based image inpainting methods have made noticeable breakthroughs over the years. However, certain inherent defects (e.g. local prior, spatially sharing parameters) of CNNs limit their performance when encountering broken images mixed with invalid information. Compared to convolution, attention has a lower inductive bias, and the output is highly correlated with the input, making it more suitable for processing images with various breakage. Inspired by this, in this paper we propose a novel attention-based network (transformer), called hourglass attention network (HAN) for image inpainting, which builds an hourglass-shaped attention structure to generate appropriate features for complemented images. In addition, we design a novel attention called Laplace attention, which introduces a Laplace distance prior for the vanilla multi-head attention, allowing the feature matching process to consider not only the similarity of features themselves, but also distance between features. With the synergy of hourglass attention structure and Laplace attention, our HAN is able to make full use of hierarchical features to mine effective information for broken images. Experiments on several benchmark datasets demonstrate superior performance by our proposed approach. The code can be found at github.com/dengyecode/hourglassattention .
Ontology Based Question Answering System for Sri Lankan Online School Education

Today, distance education is one of the world’s most popular forms of education, and there are several opportunities for students to receive education online. Here, ontology can be considered one of the leading knowledge representation ways in e-learning systems. This research addressed students’ learning difficulties in Sri Lankan online education during the past two years. Students had to learn from home via online video conferences or audio series taught by teachers. However, students could not learn by asking questions or referring to the library materials to improve their self-studying knowledge. To overcome this issue, this research developed an ontology for school children in Sri Lanka, focusing on their IT syllabus and improving their self-education knowledge. This aims to provide personalized content while improving information searching. Students can ask questions from UI, and questions are taken as an input parameter and generate a query while cleansing for matching processes. Answers are generated by connecting to the index database and ontology repository, and the end output is displayed in the user interface. In the evaluation, it was targeted to categorize the questions according to relevant components, and the research shows the questions that are categorized into relevant categories while enhancing the performance.
Blockchain-Aided and Privacy-Preserving Data Governance in Multi-Stakeholder Applications

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, consent management, and selective disclosure. We introduce a decentralized data governance framework based on blockchain technology, proxy re-encryption, and Boneh, Boyen, and Shacham (BBS) signatures to let data owners control, selectively share and track their data through privacy-enhancing, consent management, and selective disclosure mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We use Digital medical e-prescription as the use case since it handles sensitive data in a multi-stakeholder environment while showing how the medical community can manage patients’ sensitive prescription data, involving patients as data owners, and doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Hyperledger Besu, Ethereum, pyUmbral PRE, and BBS signatures show that the proposed decentralized system is platform-agnostic, scalable and guarantees a higher degree of transparency, privacy, and trust with minimal overhead.
Knowledge Graph-based Conversational Recommender System in Travel

Conversational Recommender System (CRS) aims to accomplish recommendation tasks by interacting with users via natural languages. Different from the traditional recommendation, the communications with users in CRS enable the systems to capture users' latest preferences. Travel is one of the core application scenarios of CRS. However, current studies in travel mainly concentrate on restaurant recommendations, while the key aspect, tourism attraction remains unexplored due to the limitation of the dataset. Therefore, to explore CRS in tourism attraction and city recommendation, we contribute a high-quality dataset in the travel domain named CADT (City-Attraction Dataset for Travel), which consists of 3000+ tourism attractions and corresponding features with a hierarchical structure. We observe that the current state-of-the-art CRS method can hardly yield satisfactory performance on this dataset. To this end, we propose a simple but effective framework named RUI (Representation, Understanding, Interaction), which performs a path seeking process on the given knowledge graph to make travel recommendations. Both automatic and human evaluations are implemented, demonstrating the effectiveness of the RUI framework. Altogether, we introduce and explore the ways to build the first knowledge graph-based conversational recommender system focusing on city and tourism attraction recommendations in travel domain.
Talking Face Generation with Multilingual TTS

Recent studies in talking face generation have focused on building a model that can generalize from any source speech to any target identity. A number of works have already claimed this functionality and have added that their models will also generalize to any language. However, we show, using languages from different language families, that these models do not translate well when the training language and the testing language are sufficiently different. We reduce the scope of the problem to building a language-robust talking face generation system on seen identities, i.e., the target identity is the same as the training identity. In this work, we introduce a talking face generation system that generalizes to different languages. We evaluate the efficacy of our system using a multilingual text-to-speech system. We present the joint text-to-speech system and the talking face generation system as a neural dubber system. Our demo is available at https://bit.ly/ml-face-generation-cvpr22-demo. Also, our screencast is uploaded at https://youtu.be/F6h0s0M4vBI.
Generalizable Patch-Based Neural Rendering

Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The recent focus has been on models that overfit to a single scene, and the few attempts to learn models that can synthesize novel views of unseen scenes mostly consist of combining deep convolutional features with a NeRF-like model. We propose a different paradigm, where no deep visual features and no NeRF-like volume rendering are needed. Our method is capable of predicting the color of a target ray in a novel scene directly, just from a collection of patches sampled from the scene. We first leverage epipolar geometry to extract patches along the epipolar lines of each reference view. Each patch is linearly projected into a 1D feature vector and a sequence of transformers process the collection. For positional encoding, we parameterize rays as in a light field representation, with the crucial difference that the coordinates are canonicalized with respect to the target ray, which makes our method independent of the reference frame and improves generalization. We show that our approach outperforms the state-of-the-art on novel view synthesis of unseen scenes even when being trained with considerably less data than prior work. Our code is available at https://mohammedsuhail.net/gen_patch_neural_rendering/ .
Impact of global government investment on education and research development: A comparative analysis and demystifying the science, technology, innovation, and education conundrum

To secure an inclusive and development-oriented society where nations can create, access, and utilize a scalable and sustainable infrastructure and resources, an equitable and effective investment in education and human capital development, including science, technology, and innovation (STI) has proven to be inevitable. There have been speculations on how disproportionate the global government investments and expenditures may be in achieving those goals across the regions. This study conducts a comparative analysis of data about global government investment in relation to education, STI and researchers/human capital development. We applied a multivariate analysis of variance (MANOVA) and multiple comparisons (Post Hoc Tukey) tests to determine the association between the different types of regions and the level of Educational and STI investment as per percentage of gross domestic product (GDP) and full-time equivalent (FTE) of researchers between 2015 and 2020, respectively. Three categories of data from The UNESCO's Institute for Statistics (UIS) on sustainable development goals (SDGs) were used for the investigations. The analyzed dataset consisted of information about Education as per GDP (n = 190), STI as per GDP (n = 155), and STI as per Researcher FTE (n = 155). The results show that there were differences in the global government investments in the above constructs between the low- and middle-income vs high-income regions. In turn, the study empirically discussed and shed light on the leading factors by considering their implications, and how those can be used to support decision-making and infrastructural investment policies by the various governments, policymakers, financial investors, and educators towards a global/sustainable practice.
Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments

The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment.Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function's challenges.This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA).Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training.Then, experiments are carried out through the MATLAB simulation environment.The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time.The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.
PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s even with the most advanced AI accelerated chips, preventing them from most real-world applications, which often require 100 MB/s. In this paper, we propose PILC, an end-to-end image lossless compression framework that achieves 200 MB/s for both compression and decom-pression with a single NVIDIA Tesla V100 GPU, 10× faster than the most efficient one before. To obtain this result, we first develop an AI codec that combines auto-regressive model and VQ-VAE which performs well in lightweight setting, then we design a low complexity entropy coder that works well with our codec. Experiments show that our framework compresses better than PNG by a margin of 30% in multiple datasets. We believe this is an important step to bring AI compression forward to commercial use.
Using Gamification to Address the Adoption of Blockchain Technology in the Public Sector of Education

The rising issue of corruption in the public sector could be solved by adopting an innovative technology, such as blockchain, which offers data transparency and immutability. We present the opportunities of using blockchain technology in the transactions of the public sector of education and a conceptual approach for the adoption of this technology by the staff of the University of West Attica. Our research has practical value for the information technology managers of the public sector who wish to integrate blockchain into public organizations because we also suggest gaming features for the main public sectors.
PRE-NAS: Evolutionary Neural Architecture Search With Predictor

Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space. Prediction of the performance of a network can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, predictor-assisted evolutionary NAS (PRE-NAS) which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous. This circumvents bias and leads to more accurate predictions. Extensive experiments on the NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, a competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet, respectively.
Efficient NIZKs and Signatures from Commit-and-Open Protocols in the QROM

Commit-and-open $$\Sigma $$ -protocols are a popular class of protocols for constructing non-interactive zero-knowledge arguments and digital-signature schemes via the Fiat-Shamir transformation . Instantiated with hash-based commitments, the resulting non-interactive schemes enjoy tight online-extractability in the random oracle model. Online extractability improves the tightness of security proofs for the resulting digital-signature schemes by avoiding lossy rewinding or forking-lemma based extraction. In this work, we prove tight online extractability in the quantum random oracle model (QROM), showing that the construction supports post-quantum security. First, we consider the default case where committing is done by element-wise hashing. In a second part, we extend our result to Merkle-tree based commitments. Our results yield a significant improvement of the provable post-quantum security of the digital-signature scheme Picnic. Our analysis makes use of a recent framework by Chung et al. [CFHL21] for analysing quantum algorithms in the QROM using purely classical reasoning. Therefore, our results can to a large extent be understood and verified without prior knowledge of quantum information science.
Research Information Infrastructure in Ukraine: first steps towards building a national CRIS

Development and implementation of Current Research Information Systems (CRIS) is one of the most transparent and practical approaches to curate research information on a national level. The process of building and implementing such systems is a complex and time consuming where successful results heavily depend on the established research information infrastructure of a country, the interoperability of the systems and the quality of the information which reside in them. The purpose of this paper is to analyse the existing Ukrainian Research Information Infrastructure and identify which databases could be reused and integrated with a national Ukrainian Current Research Information System (URIS). The analysis showed that there are functional databases and registries that collect data on research activities and could be used as a data sources for the URIS. In particular, the Unified State Electronic Database on Education is a potential data source on higher educational institutions, the National Repository of Academic Texts — on metadata on research output, internal database of the National Research Foundation of Ukraine and database on research projects maintained by Ukrainian Institute of Scientific Technical and Economic Information — on projects. Secondly, it was identified that Ukrainian research infrastructure lacks complete, up-to-date registry on researchers. Finally, we discussed the challenges and solutions for further steps in building national CRIS.
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation

Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, appearance estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene –as opposed to a cropped image of the face– contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning on both the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state-of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code are available for research purposes at https://trust.is.tue.mpg.de .
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images

Image noise modeling is a long-standing problem with many applications in computer vision. Early attempts that propose simple models, such as signal-independent additive white Gaussian noise or the heteroscedastic Gaussian noise model (a.k.a., camera noise level function) are not sufficient to learn the complex behavior of the camera sensor noise. Recently, more complex learning-based models have been proposed that yield better results in noise synthesis and downstream tasks, such as denoising. However, their dependence on supervised data (i.e., paired clean images) is a limiting factor given the challenges in producing ground-truth images. This paper proposes a framework for training a noise model and a denoiser simultaneously while relying only on pairs of noisy images rather than noisy/clean paired image data. We apply this framework to the training of the Noise Flow architecture. The noise synthesis and density estimation results show that our framework outperforms previous signal-processing-based noise models and is on par with its supervised counterpart. The trained denoiser is also shown to significantly improve upon both supervised and weakly supervised baseline denoising approaches. The results indicate that the joint training of a denoiser and a noise model yields significant improvements in the denoiser.