Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input's privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.
AN IMPROVED SECURITY FRAMEWORK IN HEALTH CARE USING HYBRID COMPUTING

Cloud computing is a new category of service that gives each customer access to a large-scale computing network. Since most cloud computing platforms provide services to a large number of people who aren't considered to be trustworthy, various cyber attacks may potentially target them. As a result, a cloud computing system must provide a security monitoring mechanism to protect the Virtual Machine from attacks. In this case, there is a tradeoff between the security level of the security system and its performance. If we need strong security, we'll need more laws or patterns, which means we'll need a lot more computational resources in proportion to the strength of security. As a result of the declining number of resources allocated to customers, we will add a new protection scheme in cloud environments to the VM in this report. Hence, the proposed system Proposed Elliptic curve – Diffie Hellman EC(DH)2 Algorithm is designed and deployed to improve the security in healthcare domain using hybrid computing. The most popular and recent technologies such as cloud computing and fog computing are integrated to explore data movement and stable medical data health-care information. Based on the experimental results, it is inferred that the proposed system offers high security and less operating time while handling the data making its deployment in the healthcare domain.
Kernel-based convolution expansion for facial expression recognition

The ever-growing depth and width of Convolutional Neural Networks (CNNs) drastically increases the number of their parameters and requires more powerful devices to train and deploy. In this paper, we propose a new architecture that outperforms the classical linear convolution function by expanding the latter to a higher degree kernel function without additional weights. We opt for Taylor Series Kernel which maps input data to a higher-dimensional Reproducing Kernel Hilbert Space (RKHS). Mapping features to a higher-order RKHS is performed in both implicit and explicit ways. For the former way, we compute several polynomial kernels of different degrees leveraging the kernel trick. Whereas, the latter way is achieved by concatenating the result of these polynomial kernels. The proposed Taylor Series Kernelized Convolution (TSKC) is able to learn more complex patterns than the linear convolution kernel and thus be more discriminative. The experiments conducted on Facial Expression Recognition (FER) datasets demonstrate that TSKC outperforms the ordinary convolution layer without additional parameters.
TALCS: An open-source Mandarin-English code-switching corpus and a speech recognition baseline

This paper introduces a new corpus of Mandarin-English code-switching speech recognition--TALCS corpus, suitable for training and evaluating code-switching speech recognition systems. TALCS corpus is derived from real online one-to-one English teaching scenes in TAL education group, which contains roughly 587 hours of speech sampled at 16 kHz. To our best knowledge, TALCS corpus is the largest well labeled Mandarin-English code-switching open source automatic speech recognition (ASR) dataset in the world. In this paper, we will introduce the recording procedure in detail, including audio capturing devices and corpus environments. And the TALCS corpus is freely available for download under the permissive license1. Using TALCS corpus, we conduct ASR experiments in two popular speech recognition toolkits to make a baseline system, including ESPnet and Wenet. The Mixture Error Rate (MER) performance in the two speech recognition toolkits is compared in TALCS corpus. The experimental results implies that the quality of audio recordings and transcriptions are promising and the baseline system is workable.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values

Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions-potentially causing harms once deployed. However, how to take action to address these patterns is not always clear. In a collaboration between ML and human-computer interaction researchers, physicians, and data scientists, we develop GAM Changer, the first interactive system to help domain experts and data scientists easily and responsibly edit Generalized Additive Models (GAMs) and fix problematic patterns. With novel interaction techniques, our tool puts interpretability into action-empowering users to analyze, validate, and align model behaviors with their knowledge and values. Physicians have started to use our tool to investigate and fix pneumonia and sepsis risk prediction models, and an evaluation with 7 data scientists working in diverse domains highlights that our tool is easy to use, meets their model editing needs, and fits into their current workflows. Built with modern web technologies, our tool runs locally in users' web browsers or computational notebooks, lowering the barrier to use. GAM Changer is available at the following public demo link: https://interpret.ml/gam-changer.
AnchorFace: Boosting TAR@FAR for Practical Face Recognition

Within the field of face recognition (FR), it is widely accepted that the key objective is to optimize the entire feature space in the training process and acquire robust feature representations. However, most real-world FR systems tend to operate at a pre-defined False Accept Rate (FAR), and the corresponding True Accept Rate (TAR) represents the performance of the FR systems, which indicates that the optimization on the pre-defined FAR is more meaningful and important in the practical evaluation process. In this paper, we call the predefined FAR as Anchor FAR, and we argue that the existing FR loss functions cannot guarantee the optimal TAR under the Anchor FAR, which impedes further improvements of FR systems. To this end, we propose AnchorFace to bridge the aforementioned gap between the training and practical evaluation process for FR. Given the Anchor FAR, AnchorFace can boost the performance of FR systems by directly optimizing the non-differentiable FR evaluation metrics. Specifically, in AnchorFace, we first calculate the similarities of the positive and negative pairs based on both the features of the current batch and the stored features in the maintained online-updating set. Then, we generate the differentiable TAR loss and FAR loss using a soften strategy. Our AnchorFace can be readily integrated into most existing FR loss functions, and extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of AnchorFace.
Ensemble Semi-supervised Entity Alignment via Cycle-Teaching

Entity alignment is to find identical entities in different knowledge graphs. Although embedding-based entity alignment has recently achieved remarkable progress, training data insufficiency remains a critical challenge. Conventional semi-supervised methods also suffer from the incorrect entity alignment in newly proposed training data. To resolve these issues, we design an iterative cycle-teaching framework for semi-supervised entity alignment. The key idea is to train multiple entity alignment models (called aligners) simultaneously and let each aligner iteratively teach its successor the proposed new entity alignment. We propose a diversity-aware alignment selection method to choose reliable entity alignment for each aligner. We also design a conflict resolution mechanism to resolve the alignment conflict when combining the new alignment of an aligner and that from its teacher. Besides, considering the influence of cycle-teaching order, we elaborately design a strategy to arrange the optimal order that can maximize the overall performance of multiple aligners. The cycle-teaching process can break the limitations of each model's learning capability and reduce the noise in new training data, leading to improved performance. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed cycle-teaching framework, which significantly outperforms the state-of-the-art models when the training data is insufficient and the new entity alignment has much noise.
Carry-Less to BIKE Faster

Recent advances in the development of quantum computers manifest the urge to initiate the transition from classic public key cryptography to quantum secure algorithms. Therefore, NIST has initiated a post-quantum cryptography standardization process which is currently in its third and final round. One of the Key Encapsulation Mechanism (KEM) candidates is BIKE. In this paper we optimize the algorithm to achieve new speed-records for constant-time implementations of BIKE with parameter set bikel1 on two different embedded architectures. For the ARM Cortex-M4 we leverage the performance benefit of bit-polynomial multiplication in radix-16 to outperform existing implementations. We explore different algorithmic approaches on the RISC-V-based VexRiscv platform and implement parts of the standard RISC-V Bitmanip Extension to measure its impact on BIKE. Our results indicate boundaries and trade-offs between different approaches for bit-polynomial multiplication beyond the BIKE use-case.
A Preliminary Study of SMOTE on Imbalanced Big Datasets When Dealing with Sparse and Dense High Dimensionality

The interest in exploiting big datasets with machine learning has led to adapting classic strategies in this new paradigm determined by volume, speed, and variety. Because data quality is a determining factor in constructing a classifier, it has also been necessary to adapt or develop new data preprocessing techniques. One of the challenges of most significant interest is the class imbalance problem, where the class of interest has a smaller number of examples concerning another class called the majority. To alleviate this problem, one of the most recognized techniques is SMOTE, which is characterized by generating instances of the minority class through a process that uses the nearest neighbor rule and the Euclidean distance. Various articles have shown that SMOTE is not appropriate for datasets with high dimensionality. However, in big data, datasets with high dimensionality have contained many zeros. Therefore, in this article, our objective is to analyze the SMOTE-BD behavior on imbalanced big datasets with sparse and dense dimensionality. Experimental results using two classifiers and big datasets with different dimensionalities suggest that sparsity is a predominant factor than the dimensionality in the behavior of SMOTE-BD.
Privacy-Preserving and Verifiable Cloud-Aided Disease Diagnosis and Prediction With Hyperplane Decision-Based Classifier

With the vigorous development and gradual maturity of machine learning (ML) technologies, the AI-assisted disease diagnosis and prediction ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> ) system has been extensively studied and can be expected to be intensively deployed in the real world. However, as the scale of ML data increases exponentially, the training and application of ML models impose a great burden on resource-constrained terminals. Designing cloud/edge server-aided <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> protocols is becoming a popular topic. Whereas, the sensitivity of ML data, the intellectual property of ML models, and the uncontrollability of servers bring great security challenges to this promising computing paradigm. In this article, we initialize a new four-party framework for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> system which consists of users, third-party test institution, AI doctor, and cloud/edge server. With this framework, we design two high-efficiency and secure outsourcing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> protocols under two different security models. By comprehensively employing secure hash functions, Householder transformations, and random permutations, we realize the following design objectives: 1) user’s actual identification is invisible to the other parties; 2) user’s feature vector is blinded to the AI doctor and the server; 3) the ML model of the AI doctor is confidential to the server; 4) AI doctor can obtain decent computational savings compared with achieving the diagnosis task by itself; and 5) AI doctor can verify the server’s misbehaviors with a nonnegligible probability under the security model with a fully malicious server. We argue these claims with rigid theoretical proofs and corroborate them with extensive experimental analysis.
Multivariate Side-Informed Gaussian Embedding Minimizing Statistical Detectability

Steganography schemes based on a deflection criterion for embedding posses a clear advantage against schemes based on heuristics as they provide a direct link between theoretical detectability and empirical performance. However, this advantage depends on the accuracy of the cover and stego model underlying the embedding scheme. In this work we propose an original steganography scheme based on a realistic model of sensor noise, taking into account the camera model, the ISO setting and the processing pipeline. Exploiting this statistical model allows us to take correlations between DCT coefficients into account. Several types of dependency models are presented, including a very general lattice model which accurately models dependencies introduced by a large class of processing pipelines of interest. We show in particular that the stego signal which minimizes the KL divergence under this model has a covariance proportional to the cover noise covariance. The resulting embedding scheme achieves state-of-the-art performances which go well beyond the current standards in side-informed JPEG steganography.
Blind Rotation in Fully Homomorphic Encryption with Extended Keys

Most solutions for fully homomorphic encryption rely on hard lattice problems. Accordingly, the resulting ciphertexts must contain a certain level of noise to guarantee the security of the encryption. Running homomorphic operations on these noisy ciphertexts in turn further increases the noise level in the resulting ciphertexts. If the noise exceeds a given threshold, the ciphertexts are no longer decryptable. Bootstrapping enables to deal with this issue by resetting the noise present in a ciphertext to a nominal level. Certain fully homomorphic encryption schemes require the use of binary keys for the bootstrapping operation. This paper describes how to extend the underlying blind rotation so as to efficiently support a wider number of key formats. It also investigates a multi-digit approach wherein multiple key digits are processed concurrently. All in all, the proposed solutions offer more flexibility in the parameter selection and yield a variety of new trade-offs for better performance.
Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering

Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means [1] and hierarchical agglomerative clustering (HAC) [2], to explore human locomotion and wayfinding using a VR Magic Carpet (VMC) [3], a table test version known as the Corsi Block Tapping task (CBT) [4]. This variation was carried out in the context of a virtual reality experimental setup. The participants were required to memorize a sequence of target positions projected on the rug and walk to each target figuring in the displayed sequence. the participant’s trajectory was collected and analyzed from a kinematic perspective. An earlier study [5] identified three different categories, but the classification remained ambiguous, implying that they include both kinds of individuals (normal and patients with cognitive spatial impairments). On this basis, we utilized K-Means and HAC to distinguish the navigation behavior of patients from normal individuals, emphasizing the most important discrepancies and then delving deeper to gain more insights.
Generating Scientific Definitions with Controllable Complexity

Unfamiliar terminology and complex language can present barriers to understanding science. Natural language processing stands to help address these issues by automatically defining unfamiliar terms. We introduce a new task and dataset for defining scientific terms and controlling the complexity of generated definitions as a way of adapting to a specific reader’s background knowledge. We test four definition generation methods for this new task, finding that a sequence-to-sequence approach is most successful. We then explore the version of the task in which definitions are generated at a target complexity level. We introduce a novel reranking approach and find in human evaluations that it offers superior fluency while also controlling complexity, compared to several controllable generation baselines.
Can Pre-trained Language Models Interpret Similes as Smart as Human?

Simile interpretation is a crucial task in natural language processing.Nowadays, pre-trained language models (PLMs) have achieved stateof-the-art performance on many tasks.However, it remains under-explored whether PLMs can interpret similes or not.In this paper, we investigate the ability of PLMs in simile interpretation by designing a novel task named Simile Property Probing, i.e., to let the PLMs infer the shared properties of similes.We construct our simile property probing datasets from both general textual corpora and humandesigned questions, containing 1,633 examples covering seven main categories.Our empirical study based on the constructed datasets shows that PLMs can infer similes' shared properties while still underperforming humans.To bridge the gap with human performance, we additionally design a knowledge-enhanced training objective by incorporating the simile knowledge into PLMs via knowledge embedding methods.Our method results in a gain of 8.58% in the probing task and 1.37% in the downstream task of sentiment classification.The datasets and code are publicly available at https://github.com/Abbey4799/PLMs-Interpret-Simile.
Asymmetric Cryptography Among Different 5G Core Networks

In 5G networks, packets transmitted from local UPF of a 5G network to another UPF in different destination 5G network is not encrypted. This conducts a risk of data leakage, particularly along the connection established between the two UPFs. To solve this problem, in this study, we propose an architecture that encrypts/decrypts packets with edge computers by using asymmetric cryptography for UEs.
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering

While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of them.Characterizing the strengths and weaknesses of the two readers is crucial not only for making a more informed reader selection in practice but also for developing a deeper understanding to foster further research on improving readers in a principled manner.Motivated by this goal, we make the first attempt to systematically study the comparison of extractive and generative readers for question answering.To be aligned with the state-of-the-art, we explore nine transformer-based large pre-trained language models (PrLMs) as backbone architectures.Furthermore, we organize our findings under two main categories: (1) keeping the architecture invariant, and (2) varying the underlying PrLMs.Among several interesting findings, it is important to highlight that (1) the generative readers perform better in long context QA, (2) the extractive readers perform better in short context while also showing better out-of-domain generalization, and (3) the encoder of encoder-decoder PrLMs (e.g., T5) turns out to be a strong extractive reader and outperforms the standard choice of encoderonly PrLMs (e.g., RoBERTa).We also study the effect of multi-task learning on the two types of readers varying the underlying PrLMs and perform qualitative and quantitative diagnosis to provide further insights into future directions in modeling better readers.
Colour Difference Classification for Dyed Fabrics Based on Differential Evolution with Dynamic Parameter Selection to Optimise the Output Regularisation Extreme Learning Machine

A novel optimisation technique based on the differential evolution (DE) algorithm with dynamic parameter selection (DPS-DE) is proposed to develop a colour difference classification model for dyed fabrics, improve the classification accuracy, and optimise the output regularisation extreme learning machine (RELM). The technique proposed is known as DPS-DE-RELM and has three major differences compared with DE-ELM: (1) Considering that the traditional ELM provides an illness solution based on the output weights, DE is proposed to optimise the output of the RELM. (2) Considering the simple parameter setting of the traditional algorithm, the DE algorithm with DPS is adopted. (3) For DPS, an optimal range of parameters is chosen, and the efficiency of the algorithm is significantly improved. This study analyses the colour difference classification of fabric images captured under standard lighting based on the DPS-DE-RELM algorithm. First, the colour difference of the fabric images is calculated and six color-difference-related features extracted, and second the features are classified into five different levels based on the perception of humans. Finally, a colour difference classification model is built based on the DPS-DE-RELM algorithm, and then the optimal classification model suitable for this study is selected. The experimental results show that the output method with regularisation parameters can achieve a maximum classification accuracy of 98.87%, which is higher compared with the aforementioned optimised original ELM algorithm, which can achieve a maximum accuracy of 84.67%. Therefore, the method proposed has the advantages of greater convergence speed, high classification accuracy, and robustness.