Reviewerly/exHarmony huggingface dataset:
Secure Human Identification Protocol with Human-Computable Passwords

Abstract In this paper we present a new method of secure human-computer identification, which remains safe also in untrusted systems and environments. This method allows the elimination of any supplementary gadgets/devices or theft-sensitive biometric data used by the Multi-Factor Authentication (MFA), and using only one secret as a universal private key for all obtainable online accounts. However, the features of this solution make it best suited for use by an mobile authenticator or by Authentication Authority with the Single-Sign-On (SSO) method of identity and access management, rather than for individual services. Such a key is used by our innovative challenge-response protocol to generate One-Time-Password, e.g., 6-digit OTP, could be calculated by a human in only 15 s, also offline on paper documents with an acceptable level of security required for post-quantum symmetric cyphers, thanks to the hard lattice problem with noise introduced by our new method, which we call Learning with Options (LWO). The secret has the form of an outline like a kind of handwritten autograph, designed in invisible ink on the mapping grid. The password generation process requires following such an invisible contour on the challenge matrix created randomly by the verifier and reading values from secret fields to calculate the OTP.
The Two Dimensions of Worst-case Training and Their Integrated Effect for Out-of-domain Generalization

Training with an emphasis on “hard-to-learn” components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued. Existing literature discussing this “hard-to-learn” concept are mainly expanded either along the dimension of the samples or the dimension of the features. In this paper, we aim to introduce a simple view merging these two dimensions, leading to a new, simple yet effective, heuristic to train machine learning models by emphasizing the worst-cases on both the sample and the feature dimensions. We name our method W2D following the concept of “Worst-case along Two Dimensions”. We validate the idea and demonstrate its empirical strength over standard benchmarks.
Gas Price Prediction Based on Machine Learning Combined with Ethereum Mempool

Gas is the internal pricing (metering system) for running a contract or in general any transaction in Ethereum. With the popularity of Ethereum, the deficiency of current Ethereum transaction pricing mechanism First Price Auctions is being amplified. The fee paid to miners is the gas used multiplied by the gas price. Hence, designing an effective and accurate gas price prediction method is of great significance for improving the efficiency, transparency and security of the Ethereum transaction mechanism. After the Ethereum “London” Hard Fork update, EIP-1559 has been proposed to change the historical gas mechanism and make transaction fees less volatile and more predictable. Therefore, we propose a machine learning based method to predict the gas price of next blocks combined with a dynamic feature exploited from mempool after the proposal of EIP-1559. Specifically, we consider the pending transactions and their gas price in the mempool and take them as a machine learning feature for the first time. Due to the update brought by EIP-1559, we refine more features than the related works. We use machine learning models combined with the mempool features for prediction. Experiments conducted on the dataset manifest that our model combined with the mempool data shows good prediction performance, especially significantly improving the two indicators MAE and RMSE. Furthermore, we analyze and discuss the challenges of our scheme and the potential profound effects brought by our work.
Generative Adversarial Neural Machine Translation for Phonetic Languages via Reinforcement Learning

Neural Machine Translation (NMT) heavily depends on the context vectors generated via attention network for the target word prediction. Existing works primarily focus on generating context vectors from words or subwords of sentences, limiting NMT models' ability to learn sufficient information about the source sentence representations. These situations are even worse when languages belong to extremely low-resource categories due to rare word problem. To improve the learning of source sentence representations and handle the rare word problem of Low Resource Languages (LRLs), we propose a novel improvement in Generative Adversarial Networks (GAN)-NMT by incorporating deep reinforcement learning-based optimised attention in generator and convolutional neural network in discriminator. We also create the novel joint embedding of subwords and sub-phonetic representation of sentences as input to GAN that helps models to learn the better representations and generate suitable context vectors compared to existing traditional approaches for LRLs. To show the effectiveness of our method, we demonstrate experiments on LRLs pairs, e.g., Gujarati <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leftrightarrow$</tex-math></inline-formula> Hindi, Nepali <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leftrightarrow$</tex-math></inline-formula> Hindi, Punjabi <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leftrightarrow$</tex-math></inline-formula> Hindi, Maithili <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leftrightarrow$</tex-math></inline-formula> Hindi and Urdu <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leftrightarrow$</tex-math></inline-formula> Hindi. Our proposed novel approach suppress the existing state-of-the-art techniques with considerable improvement.
Extracting corrective actions from code repositories

Simple detection of bugs, defects or anomalies during software development is not enough - it is necessary to apply corrective actions to eliminate them. To find out whether an anomaly exists in any software, we can measure the quality attributes using software metrics. The main goal of this paper was to find out and explain how to meaningfully attribute metrics to useful corrective actions.
Design Heuristics for Ethical Online Institutions

A major challenge in AI is designing autonomous systems that capture the values of stakeholders, and do so in such away that one can assess the extent to which that system’s behaviour is aligned to those values. In this paper we discuss our response to this challenge that is both practical and built on clear principles. Specifically, we propose eleven heuristics to organise the process of making values operational in the design of particular class of AI systems called online institutions. These are governed systems of interacting communities of human and autonomous artificial agents.
Hermit Crab Optimizer (HCO): A Novel Meta-heuristic Algorithm

This paper proposes a novel meta-heuristic algorithm (MA), called hermit crab optimizer (HCO), which simulates the swarm intelligence of hermit crabs in nature in finding shells protecting and letting them grow in their lifetime. HCO guides search agents separately and in parallel using new solitary and social search operators. It acts similar to a reinforcement learning process, in which the successful agents and failed ones are treated differently, inspired by the group behavior of hermit crabs and environmental characteristics. Computational experiments with well-known test problems confirm HCO's validity, accuracy, robustness, ability to escape local optima, and balance exploration-exploitation.
How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs

We aim to understand how actions are performed and identify subtle differences, such as ‘fold firmly’ vs. ‘fold gently’. To this end, we propose a method which recognizes adverbs across different actions. However, such fine-grained annotations are difficult to obtain and their long-tailed nature makes it challenging to recognize adverbs in rare action-adverb compositions. Our approach therefore uses semi-supervised learning with multiple adverb pseudo-labels to leverage videos with only action labels. Combined with adaptive thresholding of these pseudo-adverbs we are able to make efficient use of the available data while tackling the long-tailed distribution. Additionally, we gather adverb annotations for three existing video retrieval datasets, which allows us to introduce the new tasks of recognizing adverbs in unseen action-adverb compositions and unseen domains. Experiments demonstrate the effectiveness of our method, which outperforms prior work in recognizing adverbs and semi-supervised works adapted for adverb recognition. We also show how adverbs can relate fine-grained actions.
Video Activity Localisation with Uncertainties in Temporal Boundary

Current methods for video activity localisation over time assume implicitly that activity temporal boundaries labelled for model training are determined and precise. However, in unscripted natural videos, different activities mostly transit smoothly, so that it is intrinsically ambiguous to determine in labelling precisely when an activity starts and ends over time. Such uncertainties in temporal labelling are currently ignored in model training, resulting in learning mis-matched video-text correlation with poor generalisation in test. In this work, we solve this problem by introducing Elastic Moment Bounding (EMB) to accommodate flexible and adaptive activity temporal boundaries towards modelling universally interpretable video-text correlation with tolerance to underlying temporal uncertainties in pre-fixed annotations. Specifically, we construct elastic boundaries adaptively by mining and discovering frame-wise temporal endpoints that can maximise the alignment between video segments and query sentences. To enable both more accurate matching (segment content attention) and more robust localisation (segment elastic boundaries), we optimise the selection of frame-wise endpoints subject to segment-wise contents by a novel Guided Attention mechanism. Extensive experiments on three video activity localisation benchmarks demonstrate compellingly the EMB's advantages over existing methods without modelling uncertainty.
Concept of Relational Similarity Search

For decades, the success of the similarity search has been based on a detailed quantification of pairwise similarity of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations more time-consuming. While the k nearest neighbours (kNN) search dominates the real-life applications, we claim that it is principally free of a need for precise similarity quantifications. Based on the well-known fact that a selection of the most similar alternative out of several options is a much easier task than deciding the absolute similarity scores, we propose the search based on an epistemologically simpler concept of relational similarity. Having arbitrary objects $$q, o_1, o_2$$ from the search domain, the kNN search is solvable just by the ability to choose the more similar object to q out of $$o_1, o_2$$ – the decision can also contain a neutral option. We formalise such searching and discuss its advantages concerning similarity quantifications, namely its efficiency and robustness. We also propose a pioneering implementation of the relational similarity search for the Euclidean spaces and report its extreme filtering power in comparison with 3 contemporary techniques.
An Interactive AI-Powered Web Healthcare System

The Covid-19 pandemic has brought many changes in the healthcare industry lately. As things are going normal with time, many health projects designed and used during emergencies are left unexploited. To make the perpetual use of those technologies, the current need should be taken into consideration along with necessary ideas and frameworks to evolve the existing system into. To demonstrate the same, in this paper, we have presented a package of healthcare services powered by Artificial Intelligence via a chatbot system, where a user can entertain the services either by an interactive Graphical User Interface or a conversational chatbot system. This proposed system showcases how a similar Covid-19 system can be developed into a sophisticated healthcare service. This paper emphasises adding Artificial Intelligence to any conventional software via chatbot services which would broaden the services it provides even further. In order to find out the probable best technology to integrate AI with, about 50 papers have been analysed and out of which 27 relevant papers have been included in the literature review. In future, we intend to add medical support and other intelligence-based services to our system in order to meet user requirements and essential features in the field of healthcare.
Quantum Telecloning on NISQ Computers

Due to the no-cloning theorem, generating perfect quantum clones of an arbitrary unknown quantum state is not possible, however approximate quantum clones can be constructed. Quantum telecloning is a protocol that originates from a combination of quantum teleportation and quantum cloning. Here we present $1 \rightarrow 2$ and $1 \rightarrow 3$ quantum telecloning circuits, with and without ancilla, that are theoretically optimal (meaning the clones have the highest fidelity allowed by quantum mechanics), universal (meaning the clone fidelity is independent of the state being cloned), and symmetric (meaning the clones all have the same fidelity). We implement these circuits on gate model IBMQ and Quantinuum NISQ hardware and quantify the clone fidelities using parallel single qubit state tomography. Quantum telecloning using mid-circuit measurement with classical feed-forward control (i.e. real time if statements) is demonstrated on the Quantinuum H1-2 device. Two alternative implementations of quantum telecloning, deferred measurement and post selection, are demonstrated on ibmq\_montreal, where mid-circuit measurements with real time if statements are not available. Our results show that NISQ devices can achieve near-optimal quantum telecloning fidelity; for example the Quantinuum H1-2 device running the telecloning circuits without ancilla achieved a mean clone fidelity of $0.824$ with standard deviation of $0.024$ for two clone circuits and $0.765$ with standard deviation of $0.022$ for three clone circuits. The theoretical fidelity limits are $0.8\overline{3}$ for two clones and $0.\overline{7}$ for three clones. This demonstrates the viability of performing experimental analysis of quantum information networks and quantum cryptography protocols on NISQ computers.
Frequency Domain Based Learning with Transformer for Underwater Image Restoration

Underwater images restoration is a corrective procedure aiming to eliminate the variations caused by the scattering and severe absorption of light when propagating in water. Holistic approaches dominate underwater image restoration by considering colour compensation on the blue channel through CNNs. In this paper, we discard the previous CNN-based network by employing Vision Transformer as the strong baseline for Underwater Image Restoration. To further boost the restoration, we investigate the impact of the frequency domain where higher frequencies represent more detailed information corresponding to the image, which hasn't been widely studied in this field. To this end, a novel loss function is adopted as the regularization defined in the spectral domain. By learning more detailed frequency information of the whole image, our proposed pipeline further enhances the accuracy of the baseline model achieving state-of-art performance on EUVP, UIEB and UFO-120 datasets.
Pretrain, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction

Our work learns a unified model for single-view 3D reconstruction of objects from hundreds of semantic categories. As a scalable alternative to direct 3D supervision, our work relies on segmented image collections for learning 3D of generic categories. Unlike prior works that use similar supervision but learn independent category-specific models from scratch, our approach of learning a unified model simplifies the training process while also allowing the model to benefit from the common structure across categories. Using image collections from standard recognition datasets, we show that our approach allows learning 3D inference for over 150 object categories. We evaluate using two datasets and qualitatively and quantitatively show that our unified reconstruction approach improves over prior category-specific reconstruction baselines. Our final 3D reconstruction model is also capable of zero-shot inference on images from unseen object categories and we empirically show that increasing the number of training categories improves the reconstruction quality.
Efficient Network Pruning via Feature Selection

In this paper, we propose a network pruning method from a feature selection perspective. Generally, deep convolutional neural networks (CNN) consist of numerous conv layers and several fully-connected (dense) layers. The convolutional layers are proven to extract high-level features from input data, which are utilized by the following layers to accomplish the classification task. Motivated by this characteristic, we propose our pruning approach as follows: first, the outputs of the last conv layer are considered as features and feature selection is performed. For previous conv layers, we design a data-free backward iterative pruning mechanism to determine redundant filters in a feature selection manner. Then, for the dense layers, the neurons are pruned according to their feature scores. Finally, we retrain the network to recover performance. We conduct experiments on CIFAR10 and ImageNet with VGGNet and DenseNet, and results show that in most cases, our approach achieves impressive efficiency by resulting in less accuracy drop with similar pruning ratios as others, especially in terms of the parameter compression ratio.
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains

Generalizing learned representations across significantly different visual domains is a fundamental yet crucial ability of the human visual system. While recent self-supervised learning methods have achieved good performances with evaluation set on the same domain as the training set, they will have an undesirable performance decrease when tested on a different domain. Therefore, the self-supervised learning from multiple domains task is proposed to learn domain-invariant features that are not only suitable for evaluation on the same domain as the training set, but also can be generalized to unseen domains. In this paper, we propose a Domain-invariant Masked AutoEncoder (DiMAE) for self-supervised learning from multi-domains, which designs a new pretext task, i.e., the cross-domain reconstruction task, to learn domain-invariant features. The core idea is to augment the input image with style noise from different domains and then reconstruct the image from the embedding of the augmented image, regularizing the encoder to learn domain-invariant features. To accomplish the idea, DiMAE contains two critical designs, 1) content-preserved style mix, which adds style information from other domains to input while persevering the content in a parameter-free manner, and 2) multiple domain-specific decoders, which recovers the corresponding domain style of input to the encoded domain-invariant features for reconstruction. Experiments on PACS and DomainNet illustrate that DiMAE achieves considerable gains compared with recent state-of-the-art methods.
Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction

Modeling the dynamics of people walking is a problem of long-standing interest in computer vision. Many previous works involving pedestrian trajectory prediction define a particular set of individual actions to implicitly model group actions. In this paper, we present a novel architecture named GP-Graph which has collective group representations for effective pedestrian trajectory prediction in crowded environments, and is compatible with all types of existing approaches. A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations. To do this, GP-Graph first learns to assign each pedestrian into the most likely behavior group. Using this assignment information, GP-Graph then forms both intra- and inter-group interactions as graphs, accounting for human-human relations within a group and group-group relations, respectively. To be specific, for the intra-group interaction, we mask pedestrian graph edges out of an associated group. We also propose group pooling &unpooling operations to represent a group with multiple pedestrians as one graph node. Lastly, GP-Graph infers a probability map for socially-acceptable future trajectories from the integrated features of both group interactions. Moreover, we introduce a group-level latent vector sampling to ensure collective inferences over a set of possible future trajectories. Extensive experiments are conducted to validate the effectiveness of our architec ture, which demonstrates consistent performance improvements with publicly available benchmarks. Code is publicly available at https://github.com/inhwanbae/GPGraph .
STfusion: Fast and Flexible Multi-NN Execution Using Spatio-Temporal Block Fusion and Memory Management

To maximize the cost-effectiveness of neural network (NN) accelerators, architects are actively developing single-chip accelerators which can execute many NNs simultaneously. However, previous approaches fail to achieve full performance potential by exploiting only spatial or temporal resource sharing (SS or TS). They also do not consider memory management that can significantly affect performance. This limitation leads to the dire need for a new multi-NN accelerator taking both opportunities with careful memory management. But, it is extremely challenging to design an ideal spatio-temporal sharing accelerator because it requires (1) an algorithm that determines the degree of SS/TS in large exploration spaces, (2) a new STS-enabled accelerator devised with diverse design points, and (3) carefully-designed memory management that minimizes resource contention during numerous data transfers upon reconfiguration. To this end, we propose STfusion, a fast and flexible multi-NN execution architecture. First, STfusion partitions an accelerator into multiple smaller TS-enabled accelerators. Second, STfusion dynamically fuses small accelerators to adjust the accelerator sizes. Third, STfusion manages on-chip buffer in a page-granularity for stall-free data transfers. Lastly, STfusion provides an algorithm that determines the degree of SS/TS to achieve high throughput while satisfying QoS goals. Our evaluation shows that STfusion significantly outperforms state-of-the-art multi-NN accelerators.