Reviewerly/exHarmony huggingface dataset:
A reconstruction error-based framework for label noise detection

Abstract Label noise is an important data quality issue that negatively impacts machine learning algorithms. For example, label noise has been shown to increase the number of instances required to train effective predictive models. It has also been shown to increase model complexity and decrease model interpretability. In addition, label noise can cause the classification results of a learner to be poor. In this paper, we detect label noise with three unsupervised learners, namely $$\textit{principal component analysis} \hbox { (PCA)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>principal component</mml:mi><mml:mspace /><mml:mi>analysis</mml:mi><mml:mspace /><mml:mtext>(PCA)</mml:mtext></mml:mrow></mml:math> , $$\textit{independent component analysis} \hbox { (ICA)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>independent component</mml:mi><mml:mspace /><mml:mi>analysis</mml:mi><mml:mspace /><mml:mtext>(ICA)</mml:mtext></mml:mrow></mml:math> , and autoencoders. We evaluate these three learners on a credit card fraud dataset using multiple noise levels, and then compare results to the traditional Tomek links noise filter. Our binary classification approach, which considers label noise instances as anomalies, uniquely uses reconstruction errors for noisy data in order to identify and filter label noise. For detecting noisy instances, we discovered that the autoencoder algorithm was the top performer (highest recall score of 0.90), while Tomek links performed the worst (highest recall score of 0.62).
An Improved English-to-Mizo Neural Machine Translation

Machine Translation is an effort to bridge language barriers and misinterpretations, making communication more convenient through the automatic translation of languages. The quality of translations produced by corpus-based approaches predominantly depends on the availability of a large parallel corpus. Although machine translation of many Indian languages has progressively gained attention, there is very limited research on machine translation and the challenges of using various machine translation techniques for a low-resource language such as Mizo. In this article, we have implemented and compared statistical-based approaches with modern neural-based approaches for the English–Mizo language pair. We have experimented with different tokenization methods, architectures, and configurations. The performance of translations predicted by the trained models has been evaluated using automatic and human evaluation measures. Furthermore, we have analyzed the prediction errors of the models and the quality of predictions based on variations in sentence length and compared the model performance with the existing baselines.
Act Like a Radiologist: Towards Reliable Multi-View Correspondence Reasoning for Mammogram Mass Detection

Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable information about the breast anatomical prior structure and is of great significance in digital mammography interpretation. However, unlike radiologists who can utilize the natural reasoning ability to identify masses based on multiple mammographic views, how to endow the existing object detection models with the capability of multi-view reasoning is vital for decision-making in clinical diagnosis but remains the boundary to explore. In this paper, we propose an anatomy-aware graph convolutional network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. The proposed AGN consists of three steps. First, we introduce a bipartite graph convolutional network (BGN) to model the intrinsic geometric and semantic relations of ipsilateral views. Second, considering that the visual asymmetry of bilateral views is widely adopted in clinical practice to assist the diagnosis of breast lesions, we propose an inception graph convolutional network (IGN) to model the structural similarities of bilateral views. Finally, based on the constructed graphs, the multi-view information is propagated through nodes methodically, which equips the features learned from the examined view with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance. Visualization results show that AGN provides interpretable visual cues for clinical diagnosis.
Fuzzy Bayesian Knowledge Tracing

Online education promotes the sharing of learning resources. Knowledge tracing (KT) is aimed at tracking the cognition function of students according to their performance on various exercises at different times and has attracted considerable attention. Existing KT models primarily use bisection representations for the performance and cognitive states of students, thus limiting the application scope of these models and the accuracy of the evaluation of student cognitive performance in learning processes. Therefore, fuzzy Bayesian KT models (namely, FBKT and T2FBKT) are proposed to address continuous score scenarios (e.g., subjective examinations) so that the applicability of KT models may be broadened. Moreover, fine-grained cognitive states can be discerned. In particular, referring to type-2 fuzzy theory, T2FBKT mitigates the model uncertainty of FBKT induced by uncertain parameters. Finally, extensive experiments demonstrate the effectiveness of the proposed fuzzy KT models.
Detecting Spectre Attacks Using Hardware Performance Counters

Spectre attacks can be catastrophic and widespread because they exploit common design flaws caused by the speculative capabilities in modern processors to leak sensitive data through side channels. Completely fixing the problem would require a redesign of the architecture for transient execution or the implementation of a new design on re-configurable hardware. However, such fixes cannot be backported to old machines with fixed hardware design. Completely replacing those machines will take a long time. Moreover, existing software patches may cause significant performance overhead. This paper proposes to detect Spectre by monitoring deviations in microarchitectural events using hardware performance counters with promising accuracy above 90% under a variety of workload conditions. However, the attacker may attempt to evade detection by slowing down the attack or mimicking benign programs. This paper thus compares different evasion strategies quantitatively and demonstrates that it is possible for the attacker to avoid detection when operating the attacks at a lower speed while maintaining a reasonable attack success rate. Then, we show that, in order to resist evasion, the original detector must be enhanced by randomly switching between a set of detectors using different features and sampling periods so we can keep the detection accuracy above 80%.
Image-to-Video Generation via 3D Facial Dynamics

We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to “imagine” a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identity-preserving, and visually pleasant face video clips from a single source face image.
Multimodal biometric authentication for mobile edge computing

In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) system designed for Mobile Edge Computing (MEC) and multimodal biometrics . We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user’s secret parameters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead . Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing .
Adaptable automation with modular deep reinforcement learning and policy transfer

Future industrial automation systems are anticipated to be shaped by intelligent technologies that allow for the adaptability of machines to the variations and uncertainties in processes and work environments. This paper is motivated by the need for devising new intelligent methods that enable efficient and scalable training of collaborative robots on a variety of tasks that foster their adaptability to new tasks and environments. Recent advances in deep Reinforcement Learning (RL) provide new possibilities to realize this vision. The state-of-the-art in deep RL offers proven algorithms that enable autonomous learning and mastery of a variety of robotic manipulation tasks with minimal human intervention. However, current deep RL algorithms predominantly specialize in a narrow range of tasks, are sample inefficient, and lack sufficient stability, which hinders their adoption in real-life, industrial settings. This paper develops and tests a Hyper-Actor Soft Actor–Critic (HASAC) deep RL framework based on the notions of task modularization and transfer learning to tackle this limitation. The goal of the proposed HASAC is to enhance an agent’s adaptability to new tasks by transferring the learned policies of former tasks to the new task through a ”hyper-actor”. The HASAC framework is tested on the virtual robotic manipulation benchmark, Meta-World. Numerical experiments indicate superior performance by HASAC over state-of-the-art deep RL algorithms in terms of reward value, success rate, and task completion time.
Collective location statistics release with local differential privacy

Location statistics collective release provides essential information to understand crucial phenomena, including points of interest and movement patterns. Sharing location statistics without compromising users’ privacy is critical. The new standard method for calculating private statistical information is differential privacy. Several research works focus on the centralized environment where individuals share their real data with a trusted curator. To calculate private statistics, the trusted curator then adds carefully measured noise. However, this centralized approach is susceptible to privacy breaches in which, by targeting the trustworthy curator, an adversary accesses the true data. By enabling every individual to perturb their records before reaching the curator, local differential privacy overcomes this form of attack. However, the fundamental challenge is that existing local differential privacy algorithms degrade the privacy guarantee when data collection spans over time. Therefore, a methodology to allocate the privacy budget over multiple timestamps is needed to achieve a collective location statistics release. In this paper, using a sliding window approach, we tackle the issue of releasing location statistics with local differential privacy over multiple timestamps. We develop a privacy budget allocation methodology to release collective location statistics with formal local differential privacy proof. Then, we present an approximation strategy to share the closest private statistics to the current timestamp. This strategy optimizes the released statistics’ utility. We demonstrate our solution enables collective location statistics release with a robust privacy guarantee on two datasets (real-time counts of nearby users and historical counts of bike owners close to each bike station).
Why Does Explainability Matter in News Analytic Systems? Proposing Explainable Analytic Journalism

As the use of algorithms has emerged in journalism, analytic/algorithmic journalism (AJ) has seen rapid development in major news organizations. Despite this surging trend, little is known about the role and the effects of explainability on the process by which people perceive and make sense of trust in an algorithm-driven AI system. While AJ has greatly benefited from increasingly sophisticated algorithm technologies, AJ suffers from a lack of transparency and understandability for readers. We identify explainability as a heuristic cue of an algorithm and conceptualizes it in relation to trust by testing how it affects user emotion with AJ. Our experiments show that the addition of interpretable explanations leads to enhanced trust in the context of AJ and readers' trust hinges upon the perceived normative values that are used to assess algorithmic qualities. Explanations of why certain news articles are recommended give users emotional assurance and affirmation. Mediation analyses show that explanatory cues play a mediating role between trust and performance expectancy. The results have implications for the inclusion of explanatory cues in AJ, which help to increase credibility and help users to assess AJ value.
Eye state detection based on Weight Binarization Convolution Neural Network and Transfer Learning

Detection of eye state can assist the related work in the field of computer vision such as face recognition, expression recognition, pose estimation and human–computer interaction. This paper proposes an Weight Binarization Convolution Neural Network and Transfer Learning (WBCNNTL) based eye state detection method, in which the WBCNNTL is composed of deep convolution neural network and the weight is binarized. The human eye state features can be extracted by Convolutional Neural Network (CNN) effectively, and binary network not only speeds up the computation, but also helps to reduce the storage space and fewer parameters of the model. Transfer learning applies the knowledge or patterns learned from the source domain to different but related fields or problems, which improves the training efficiency of the new model. Experiments on eye state detection are performed using Closed Eyes in the wild (CEW), FER2013 and Zhejiang University Eyeblink (ZJU) Databases, from which the experiment results show the average accuracy obtained by our method are 97.41% on CEW and are 97.15% on ZJU, the computing speed of binary network is faster than non-binary network. Moreover, our method requires less storage space due to lightweight binary model, which maintains better detection capability on CEW compared with some state-of-the-art works.
Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma

Nuclear fusion is a promising alternative to address the problem of sustainable energy production. The tokamak is an approach to fusion based on magnetic plasma confinement, constituting a complex physical system with many control challenges. We study the characteristics and optimization of reservoir computing (RC) for real-time and adaptive prediction of plasma profiles in the DIII-D tokamak. Our experiments demonstrate that RC achieves comparable results to state-of-the-art (deep) convolutional neural networks (CNNs) and long short-term memory (LSTM) models, with a significantly easier and faster training procedure. This efficient approach allows for fast and frequent adaptation of the model to new situations, such as changing plasma conditions or different fusion devices.
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this paper, we propose SetVAE, a hierarchical variational autoencoder for sets. Motivated by recent progress in set encoding, we build SetVAE upon attentive modules that first partition the set and project the partition back to the original cardinality. Exploiting this module, our hierarchical VAE learns latent variables at multiple scales, capturing coarse-to-fine dependency of the set elements while achieving permutation invariance. We evaluate our model on point cloud generation task and achieve competitive performance to the prior arts with substantially smaller model capacity. We qualitatively demonstrate that our model generalizes to unseen set sizes and learns interesting subset relations without supervision. Our implementation is available at https://github.com/jw9730/setvae.
Deep robust multilevel semantic hashing for multi-label cross-modal retrieval

Hashing based cross-modal retrieval has recently made significant progress. But straightforward embedding data from different modalities involving rich semantics into a joint Hamming space will inevitably produce false codes due to the intrinsic modality discrepancy and noises. We present a novel deep Robust Multilevel Semantic Hashing (RMSH) for more accurate multi-label cross-modal retrieval. It seeks to preserve fine-grained similarity among data with rich semantics,i.e., multi-label, while explicitly require distances between dissimilar points to be larger than a specific value for strong robustness. For this, we give an effective bound of this value based on the information coding-theoretic analysis, and the above goals are embodied into a margin-adaptive triplet loss. Furthermore, we introduce pseudo-codes via fusing multiple hash codes to explore seldom-seen semantics, alleviating the sparsity problem of similarity information. Experiments on three benchmarks show the validity of the derived bounds, and our method achieves state-of-the-art performance.
An Integrated Transfer Learning Method for Power System Dynamic Security Assessment of Unlearned Faults With Missing Data

This letter proposes an integrated transfer learning (TL) method for pre-fault dynamic security assessment (DSA) of power systems, which aims to simultaneously achieve fault transfer and address missing data issue for unlearned faults. Moreover, this letter provides the tight mathematic proof for the guaranteed DSA performance of the proposed integrated TL method. Comprehensive simulation results on the benchmark testing system have shown that the proposed integrated TL method can achieve a high DSA accuracy for unknown faults with complete data and can also maintain a satisfactory DSA accuracy for unknown faults even with incomplete data inputs.
PCSS: Privacy Preserving Communication Scheme for SDN Enabled Smart Homes

Smart home technology, also known as home automation system, allows the homeowner and residents to control and monitor the smart devices like heating, ventilation, and air conditioning (HVAC), refrigerators, doors, cameras etc. These features facilitate the users by providing a safe and well-suited environment. However, at the same time these connected devices could be exploited by cybercriminals due to overlooked inbuilt security and privacy concerns of these devices. Because of no authentication and plain text data transmission, intruders can get user profiles, learn user behavior, and can even inject malwares in an unauthenticated devices. Therefore, authentication and privacy preserving user queries remain the key issues in wide adaptation of such technologies. Because of the above mentioned problems, the traditional security solutions cannot be deployed in the low processing devices. Therefore, to overcome the security issues of these low processing gadgets, a network level, lightweight cryptographic security mechanisms are necessitated where the processing is done at the network level middle box rather than low resources end devices. In this aspect, the evolving networking paradigm Software Defined Networking (SDN) offers such properties like programmability, agility, centralized management, and vendor neutrality that overcome the conventional networking control, management, and security problems. The controller of SDN at the control layer manages all the computation and complexities at the network level, rather than at the smart devices. Therefore, in this research, we present a privacy preserving communication scheme for SDN-enabled smart homes (PCSS), which aims at provisioning user and smart device authentication, privacy for data (rest and transit), and user queries. It hinders the learning and modification of data by an intruder during the transmission and features mutual authentication of user, controller, and smart device. PCSS, also offers privacy preserving user queries for the smart homes. This is achieved by proposing a symmetric key based lightweight authentication and searchable encrypted queries protocol. We further highlighted that the experimental results show the efficacy and usefulness of PCSS scheme as compared with the existing secure smart home/system protocols.
Adaptive Hyperspectral Mixed Noise Removal

This article proposes a new denoising method for hyperspectral images (HSIs) corrupted by mixtures (in a statistical sense) of stripe noise, Gaussian noise, and impulsive noise. The proposed method has three distinctive features: 1) it exploits the intrinsic characteristics of HSIs, namely, low-rank and self-similarity; 2) the observation noise is assumed to be additive and modeled by a mixture of Gaussian (MoG) densities; 3) the inference is performed with an expectation maximization (EM) algorithm, which, in addition to the clean HSI, also estimates the mixture parameters (posterior probability of each mode and variances). Comparisons of the proposed method with state-of-the-art algorithms provide experimental evidence of the effectiveness of the proposed denoising algorithm. A MATLAB demo of this work will be available at <uri>https://github.com/TaiXiangJiang</uri> for the sake of reproducibility.