Reviewerly/exHarmony huggingface dataset:
Understanding and Preventing Capacity Loss in Reinforcement Learning

The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks. We identify a mechanism by which non-stationary prediction targets can prevent learning progress in deep RL agents: \textit{capacity loss}, whereby networks trained on a sequence of target values lose their ability to quickly update their predictions over time. We demonstrate that capacity loss occurs in a range of RL agents and environments, and is particularly damaging to performance in sparse-reward tasks. We then present a simple regularizer, Initial Feature Regularization (InFeR), that mitigates this phenomenon by regressing a subspace of features towards its value at initialization, leading to significant performance improvements in sparse-reward environments such as Montezuma's Revenge. We conclude that preventing capacity loss is crucial to enable agents to maximally benefit from the learning signals they obtain throughout the entire training trajectory.
Recent Progress in Conversational AI

Conversational artificial intelligence (AI) is becoming an increasingly popular topic among industry and academia. With the fast development of neural network-based models, a lot of neural-based conversational AI system are developed. We will provide a brief review of the recent progress in the Conversational AI, including the commonly adopted techniques, notable works, famous competitions from academia and industry and widely used datasets.
Reconceptualising English Language Teacher's Journey through the Lens of Threshold Concepts

This conceptual paper provides an overview of Malaysia's English Language Teaching (ELT) situation and briefly introduces Threshold Concepts in teacher education.This paper is based on the literature review related to the identification of threshold concepts in English language teaching.The study aims to discuss the possibility of adapting the Threshold Concepts framework to understand and charter the language teaching journey, particularly how English as a Second Language (ESL) teachers transition and experience the identity and ontological shifts throughout their teaching career.This study proposes a new conceptual perspective in understanding the ELT journey among ESL teachers, which will be relevant to the teacher training industry to leverage and support the professional development of English language teachers.The study recommends future empirical research to probe further the relationship of these concepts and an in-depth inquiry into the experiences of experts in ELT in their transformational journey to become effective teachers.
Application of AI Face Recognition Technology in Swipe Card Attendance Systems for Hospitals

Traditional swipe card attendance systems for hospitals cannot effectively protect employees’ personal information and ensure that the employees are swiping their own cards. To solve the problem, the present study proposes a novel hospital swipe card attendance system using an artificial intelligence (AI) face modeling system with an open-source face database. The proposed system employs a multi-task cascaded convolutional network (MTCNN) algorithm and FaceNet to improve the performance of face recognition. The system can compare the face of the one who swipes a card with the faces of cardholders in the database, thereby preventing the one from clocking in on behalf of others. The results show that the application of AI technology in the hospital swipe card attendance system can realize the promise of protecting employees’ personal information and verifying employees’ identities.
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization

In this work we describe an Adaptive Regularization using Cubics (ARC) method for large-scale nonconvex unconstrained optimization using Limited-memory Quasi-Newton (LQN) matrices. ARC methods are a relatively new family of optimization strategies that utilize a cubic-regularization (CR) term in place of trust-regions and line-searches. LQN methods offer a large-scale alternative to using explicit second-order information by taking identical inputs to those used by popular first-order methods such as stochastic gradient descent (SGD). Solving the CR subproblem exactly requires Newton's method, yet using properties of the internal structure of LQN matrices, we are able to find exact solutions to the CR subproblem in a matrix-free manner, providing large speedups and scaling into modern size requirements. Additionally, we expand upon previous ARC work and explicitly incorporate first-order updates into our algorithm. We provide experimental results when the SR1 update is used, which show substantial speed-ups and competitive performance compared to Adam and other second order optimizers on deep neural networks (DNNs). We find that our new approach, ARCLQN, compares to modern optimizers with minimal tuning, a common pain-point for second order methods.
MDS and AMDS symbol-pair codes are constructed from repeated-root codes

Symbol-pair codes introduced by Cassuto and Blaum in 2010 are designed to protect against the pair errors in symbol-pair read channels. One of the central themes in symbol-error correction is the construction of maximal distance separable (MDS) symbol-pair codes that possess the largest possible pair-error correcting performance. In this paper, we construct more general generator polynomials for two classes of MDS symbol-pair codes with code length $lp$. Based on repeated-root cyclic codes, we derive all MDS symbol-pair codes of length $3p$, when the degree of the generator polynomials is no more than 10. We also give two new classes of (almost maximal distance separable) AMDS symbol-pair codes with the length $lp$ or $4p$ by virtue of repeated-root cyclic codes. For length $3p$, we derive all AMDS symbol-pair codes, when the degree of the generator polynomials is less than 10. The main results are obtained by determining the solutions of certain equations over finite fields.
Exploring Widevine for Fun and Profit

For years, Digital Right Management (DRM) systems have been used as the go-to solution for media content protection against piracy. With the growing consumption of content using Over-the-Top platforms, such as Netflix or Prime Video, DRMs have been deployed on numerous devices considered as potential hostile environments. In this paper, we focus on the most widespread solution, the closed-source Widevine DRM. Installed on billions of devices, Widevine relies on cryptographic operations to protect content. Our work presents a study of Widevine internals on Android, mapping its distinct components and bringing out its different cryptographic keys involved in content decryption. We provide a structural view of Widevine as a protocol with its complete key ladder. Based on our insights, we develop WideXtractor, a tool based on Frida to trace Widevine function calls and intercept messages for inspection. Using this tool, we analyze Netflix usage of Widevine as a proof-of-concept, and raised privacy concerns on user-tracking. In addition, we leverage our knowledge to bypass the obfuscation of Android Widevine software-only version, namely L3, and recover its Root-of-Trust.
Topological order and entanglement dynamics in the measurement-only XZZX quantum code

We examine the dynamics of a $(1+1)$-dimensional measurement-only circuit defined by the stabilizers of the [[5,1,3]] quantum error correcting code interrupted by single-qubit Pauli measurements. The code corrects arbitrary single-qubit errors and it stabilizes an area law entangled state with a $D_2 = \mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry protected topological (SPT) order, as well as a symmetry breaking (SB) order from a two-fold bulk degeneracy. The Pauli measurements break the topological order and induce a phase transition into a trivial area law phase. Allowing more than one type of Pauli measurement increases the measurement-induced frustration, and the SPT and SB order can be broken either simultaneously or separately at nonzero measurement rate. This yields a rich phase diagram and unanticipated critical behavior at the phase transitions. Although the correlation length exponent $ν=\tfrac43$ and the dynamical critical exponent $z=1$ are consistent with bond percolation, the prefactor of the logarithmic entanglement growth may take non-integer multiples of the percolation value. Remarkably, we identify a robust transient scaling regime for the purification dynamics of $L$ qubits. It reveals a modified dynamical critical exponent $z^*\neq z$, which is observable up to times $t\sim L^{z^*}$ and is reminiscent of the relaxation of critical systems into a prethermal state.
Boson Sampling for Generalized Bosons

We introduce the notion of "generalized bosons" whose exchange statistics resemble those of bosons, but the local bosonic commutator $[a_i,a_i^\dagger]=1$ is replaced by an arbitrary single-mode operator that is diagonal in the generalized Fock basis. Examples of generalized bosons include boson pairs and spins. We consider the analogue of the boson sampling task for these particles and observe that its output probabilities are still given by permanents, so that the results regarding hardness of sampling directly carry over. Finally, we propose implementations of generalized boson sampling in circuit-QED and ion-trap platforms.
Dual-Domain Image Synthesis using Segmentation-Guided GAN

We introduce a segmentation-guided approach to synthesise images that integrate features from two distinct domains. Images synthesised by our dual-domain model belong to one domain within the semantic mask, and to another in the rest of the image - smoothly integrated. We build on the successes of few-shot StyleGAN and single-shot semantic segmentation to minimise the amount of training required in utilising two domains. The method combines a few-shot cross-domain StyleGAN with a latent optimiser to achieve images containing features of two distinct domains. We use a segmentation-guided perceptual loss, which compares both pixel-level and activations between domain-specific and dual-domain synthetic images. Results demonstrate qualitatively and quantitatively that our model is capable of synthesising dual-domain images on a variety of objects (faces, horses, cats, cars), domains (natural, caricature, sketches) and part-based masks (eyes, nose, mouth, hair, car bonnet). The code is publicly available at: https://github.com/denabazazian/Dual-Domain-Synthesis.
Category-Aware Transformer Network for Better Human-Object Interaction Detection

Human-Object Interactions (HOI) detection, which aims to localize a human and a relevant object while recognizing their interaction, is crucial for understanding a still image. Recently, transformer-based models have significantly advanced the progress of HOI detection. However, the capability of these models has not been fully explored since the Object Query of the model is always simply initialized as just zeros, which would affect the performance. In this paper, we try to study the issue of promoting transformer-based HOI detectors by initializing the Object Query with category-aware semantic information. To this end, we innovatively propose the Category-Aware Transformer Network (CATN). Specifically, the Object Query would be initialized via category priors represented by an external object detection model to yield better performance. Moreover, such category priors can be further used for enhancing the representation ability of features via the attention mechanism. We have firstly verified our idea via the Oracle experiment by initializing the Object Query with the groundtruth category information. And then extensive experiments have been conducted to show that a HOI detection model equipped with our idea outperforms the baseline by a large margin to achieve a new state-of-the-art result.
Study of University Library and Adult Information Literacy Education

This paper describes the meaning of information calibre education and the necessity of the information calibre education in adult higher education. Meanwhile, this paper also analyses the advantages which the university library has in the area of information calibre education for adult students and the effective ways to implement adult education in university libraries.
Creating Cloud Monitors from Models, Securing Cloud

In cloud computing, permission is a critical security risk. Its goal is to limit user access to the network. The large number of resources associated with REST APIs found in the cloud makes implementing security requirements difficult and error-prone. Security cloud monitors are employed to solve the issue. The performance and security needs are represented using a model- driven methodology. Cloud monitors are created using models. Contracts are included in the cloud monitors, which automatically verify the execution. The cloud monitor is implemented using the Django framework, and the solution is validated using OpenStack
Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks

The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption. In this paper, we propose a novel two-step nonparametric approach called Deep Feature Screening (DeepFS) that can overcome these problems and identify significant features with high precision for ultra high-dimensional, low-sample-size data. This approach first extracts a low-dimensional representation of input data and then applies feature screening based on multivariate rank distance correlation recently developed by Deb and Sen (2021). This approach combines the strengths of both deep neural networks and feature screening, and thereby has the following appealing features in addition to its ability of handling ultra high-dimensional data with small number of samples: (1) it is model free and distribution free; (2) it can be used for both supervised and unsupervised feature selection; and (3) it is capable of recovering the original input data. The superiority of DeepFS is demonstrated via extensive simulation studies and real data analyses.
From teaching experience. X. Regular, semi-regular and irregular plane partitions

The article deals with regular, semi-regular and irregular plane partitions, among the latter are partitions into equal polygons and arbitrary partitions with simple knots. It continues the author's supplementary lectures to the university textbook «Crystallography» by G. M. Popov and I. I. Shafranovsky, which was recommended to the students of geology of the St. Petersburg Mining University. The new topic is interesting because the first problem is solved elementary in the form of a Diophantine equation, the second is solved by algorithmic enumeration of variants, the third is related to the 18th Gilbert problem and is not exhausted yet, and the fourth shows the transition to the geometry of quasi-crystalline and amorphous structures. It gives examples of easily formulated but difficult to solve problems and encourages students to be scientifically creative in geometric crystallography and related disciplines (petrography, materials science). The presentation of the theory is illustrated with examples of plane partitions in urban interiors of St. Petersburg.
Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality

Recent work by Lakshminarayanan and Singh [2020] provided a dual view for fully connected deep neural networks (DNNs) with rectified linear units (ReLU). It was shown that (i) the information in the gates is analytically characterised by a kernel called the neural path kernel (NPK) and (ii) most critical information is learnt in the gates, in that, given the learnt gates, the weights can be retrained from scratch without significant loss in performance. Using the dual view, in this paper, we rethink the conventional interpretations of DNNs thereby explicitsing the implicit interpretability of DNNs. Towards this, we first show new theoretical properties namely rotational invariance and ensemble structure of the NPK in the presence of convolutional layers and skip connections respectively. Our theory leads to two surprising empirical results that challenge conventional wisdom: (i) the weights can be trained even with a constant 1 input, (ii) the gating masks can be shuffled, without any significant loss in performance. These results motivate a novel class of networks which we call deep linearly gated networks (DLGNs). DLGNs using the phenomenon of dual lifting pave way to more direct and simpler interpretation of DNNs as opposed to conventional interpretations. We show via extensive experiments on CIFAR-10 and CIFAR-100 that these DLGNs lead to much better interpretability-accuracy tradeoff.
Efficient Non-Autoregressive GAN Voice Conversion using VQWav2vec Features and Dynamic Convolution

It was shown recently that a combination of ASR and TTS models yield highly competitive performance on standard voice conversion tasks such as the Voice Conversion Challenge 2020 (VCC2020). To obtain good performance both models require pretraining on large amounts of data, thereby obtaining large models that are potentially inefficient in use. In this work we present a model that is significantly smaller and thereby faster in processing while obtaining equivalent performance. To achieve this the proposed model, Dynamic-GAN-VC (DYGAN-VC), uses a non-autoregressive structure and makes use of vector quantised embeddings obtained from a VQWav2vec model. Furthermore dynamic convolution is introduced to improve speech content modeling while requiring a small number of parameters. Objective and subjective evaluation was performed using the VCC2020 task, yielding MOS scores of up to 3.86, and character error rates as low as 4.3\%. This was achieved with approximately half the number of model parameters, and up to 8 times faster decoding speed.
Multi-label topic classification for COVID-19 literature with Bioformer

We describe Bioformer team's participation in the multi-label topic classification task for COVID-19 literature (track 5 of BioCreative VII). Topic classification is performed using different BERT models (BioBERT, PubMedBERT, and Bioformer). We formulate the topic classification task as a sentence pair classification problem, where the title is the first sentence, and the abstract is the second sentence. Our results show that Bioformer outperforms BioBERT and PubMedBERT in this task. Compared to the baseline results, our best model increased micro, macro, and instance-based F1 score by 8.8%, 15.5%, 7.4%, respectively. Bioformer achieved the highest micro F1 and macro F1 scores in this challenge. In post-challenge experiments, we found that pretraining of Bioformer on COVID-19 articles further improves the performance.
Understanding the Domain Gap in LiDAR Object Detection Networks

In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world. However, the open-world is vast and continuously changing, so it is not technically feasible to collect and annotate training datasets which accurately represent this domain. Therefore, there are always domain gaps between training datasets and the open-world which must be understood. In this work, we investigate the domain gaps between high-resolution and low-resolution LiDAR sensors in object detection networks. Using a unique dataset, which enables us to study sensor resolution domain gaps independent of other effects, we show two distinct domain gaps - an inference domain gap and a training domain gap. The inference domain gap is characterised by a strong dependence on the number of LiDAR points per object, while the training gap shows no such dependence. These fndings show that different approaches are required to close these inference and training domain gaps.
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples

A rising number of botnet families have been successfully detected using deep learning architectures. While the variety of attacks increases, these architectures should become more robust against attacks. They have been proven to be very sensitive to small but well constructed perturbations in the input. Botnet detection requires extremely low false-positive rates (FPR), which are not commonly attainable in contemporary deep learning. Attackers try to increase the FPRs by making poisoned samples. The majority of recent research has focused on the use of model loss functions to build adversarial examples and robust models. In this paper, two LSTM-based classification algorithms for botnet classification with an accuracy higher than 98% are presented. Then, the adversarial attack is proposed, which reduces the accuracy to about 30%. Then, by examining the methods for computing the uncertainty, the defense method is proposed to increase the accuracy to about 70%. By using the deep ensemble and stochastic weight averaging quantification methods it has been investigated the uncertainty of the accuracy in the proposed methods.
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI

Counterfactual explanations are increasingly used to address interpretability, recourse, and bias in AI decisions. However, we do not know how well counterfactual explanations help users to understand a systems decisions, since no large scale user studies have compared their efficacy to other sorts of explanations such as causal explanations (which have a longer track record of use in rule based and decision tree models). It is also unknown whether counterfactual explanations are equally effective for categorical as for continuous features, although current methods assume they do. Hence, in a controlled user study with 127 volunteer participants, we tested the effects of counterfactual and causal explanations on the objective accuracy of users predictions of the decisions made by a simple AI system, and participants subjective judgments of satisfaction and trust in the explanations. We discovered a dissociation between objective and subjective measures: counterfactual explanations elicit higher accuracy of predictions than no-explanation control descriptions but no higher accuracy than causal explanations, yet counterfactual explanations elicit greater satisfaction and trust than causal explanations. We also found that users understand explanations referring to categorical features more readily than those referring to continuous features. We discuss the implications of these findings for current and future counterfactual methods in XAI.