Reviewerly/exHarmony huggingface dataset:
One enhanced secure access scheme for outsourced data

The popularity of apps makes smart phones rapidly become the most widespread form of communication. Due to the impact of resource constraints in mobile phones, users prefer to outsource data from the local device to the cloud. Access control of outsourced data drives the researches for protecting sensitive data from all the possibly malicious software access or from cloud service provider misbehavior. The unexpected attacks from the local device or the cloud that are trying to breach the data access policy imposed by data owner has resulted in inadequate current access control solutions. Therefore, this paper proposes one access control scheme for Android devices to avoid authentication bypass attacks from both sides. Attribute-Based Encryption is used to design one app-level fine-grained data access for the purpose of data confidentiality in the local side. Moreover, Trusted Execution Environment is employed as a trusted computing environment which provides essential security services to protect encrypted data from unwanted access by cloud service providers or unauthorized apps from the local side. Finally, a prototype system is implemented and the performance is evaluated by the various operations used in the scheme. The experimental results show that the enhanced secure access model is flexible, efficient and secure for outsourcing data to the cloud.
Unsupervised Domain Adaptive Object Detection Using Forward-Backward Cyclic Adaptation

We present a novel approach to perform the unsupervised domain adaptation for object detection through forward-backward cyclic (FBC) training. Recent adversarial training based domain adaptation methods have shown their effectiveness on minimizing domain discrepancy via marginal feature distributions alignment. However, aligning the marginal feature distributions does not guarantee the alignment of class conditional distributions. This limitation is more evident when adapting object detectors as the domain discrepancy is larger compared to the image classification task, e.g., various number of objects exist in one image and the majority of content in an image is the background. This motivates us to learn domain-invariance for category-level semantics via gradient alignment for instance-level adaptation. Intuitively, if the gradients of two domains point in similar directions, then the learning of one domain can improve that of another domain. We propose Forward- Backward Cyclic Adaptation to achieve gradient alignment, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing. In addition, we align low-level features for adapting image-level color/texture via adversarial training. However, the detector that performs well on both domains is not ideal for the target domain. As such, in each cycle, domain diversity is enforced by two regularizations: 1) maximum entropy regularization on the source domain to penalize confident source-specific learning and 2) minimum entropy regularization on target domain to intrigue target-specific learning. Theoretical analysis of the training process is provided, and extensive experiments on challenging cross-domain object detection datasets have shown our approach's superiority over the state-of-the-art.
Match them up: visually explainable few-shot image classification

Abstract Few-shot learning (FSL) approaches, mostly neural network-based, assume that pre-trained knowledge can be obtained from base (seen) classes and transferred to novel (unseen) classes. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper FSL application in some risk-sensitive areas. In this paper, we reveal a new way to perform FSL for image classification, using a visual representation from the backbone model and patterns generated by a self-attention based explainable module. The representation weighted by patterns only includes a minimum number of distinguishable features and the visualized patterns can serve as an informative hint on the transferred knowledge. On three mainstream datasets, experimental results prove that the proposed method can enable satisfying explainability and achieve high classification results. Code is available at https://github.com/wbw520/MTUNet .
Model Parallelism Optimization for Distributed Inference via Decoupled CNN Structure

It is promising to deploy CNN inference on local end-user devices for high-accuracy and time-sensitive applications. Model parallelism has the potential to provide high throughput and low latency in distributed CNN inference. However, it is non-trivial to use model parallelism as the original CNN model is inherently tightly-coupled structure. In this article, we propose DeCNN, a more effective inference approach that uses decoupled CNN structure to optimize model parallelism for distributed inference on end-user devices. DeCNN is novel consisting of three schemes. Scheme-1 is structure-level optimization. It exploits group convolution and channel shuffle to decouple the original CNN structure for model parallelism. Scheme-2 is partition-level optimization. It is based on channel group to partition the convolutional layers, and then leverages input-based method to partition the fully connected layers, further exposing high degree of parallelism. Scheme-3 is communication-level optimization. It uses inter-sample parallelism to hide communications for better performance and robustness, especially in the weak network connections. We use ImageNet classification task to evaluate the effectiveness of DeCNN on a distributed multi-ARM platform. Notably, when using the number of devices from 1 to 4, DeCNN can accelerate the inference of large-scale ResNet-50 by 3.21×, and reduce 65.3 percent memory footprint, with 1.29 percent accuracy improvement.
Use of Model Reparametrization to Improve Variational Bayes

Abstract We propose using model reparametrization to improve variational Bayes inference for hierarchical models whose variables can be classified as global (shared across observations) or local (observation-specific). Posterior dependence between local and global variables is minimized by applying an invertible affine transformation on the local variables. The functional form of this transformation is deduced by approximating the posterior distribution of each local variable conditional on the global variables by a Gaussian density via a second order Taylor expansion. Variational Bayes inference for the reparametrized model is then obtained using stochastic approximation. Our approach can be readily extended to large datasets via a divide and recombine strategy. Using generalized linear mixed models, we demonstrate that reparametrized variational Bayes (RVB) provides improvements in both accuracy and convergence rate compared to state of the art Gaussian variational approximation methods.
Post-Quantum Multi-Party Computation

We initiate the study of multi-party computation for classical functionalities in the plain model, with security against malicious quantum adversaries. We observe that existing techniques readily give a polynomial-round protocol, but our main result is a construction of constant-round post-quantum multi-party computation. We assume mildly super-polynomial quantum hardness of learning with errors (LWE), and quantum polynomial hardness of an LWE-based circular security assumption. Along the way, we develop the following cryptographic primitives that may be of independent interest: To prove the security of our protocol, we develop a new straight-line non-black-box simulation technique against parallel sessions that does not clone the adversary's state. This technique may also be relevant to the classical setting.
Intelligent Personal Assistant - Implementing Voice Commands enabling Speech Recognition

Intelligent personal assistant (IPA) is a software agent performing tasks on behalf of an human or individual I based on commands or questions which are similar to chat bots. They are also referred as Intelligent Virtual Assistant which interprets human speech and respond via synthesized voices. IPAs and IVAs finds their usage in various applications such as home automation, manage to-do tasks and media playback through voice. This paper aims to propose speech recognition systems and dealing with creating a virtual personal assistant. The existing system serves on the internet and is maintained by the third party. This application shall protect personal data from others and use the local database, speech recognition and synthesiser. A parser named SURR(Semantic Unification and Reference Resolution) is employed to recognise the speech. Synthesizer uses text to phoneme.
Domain Adaptation Through Task Distillation

Deep networks devour millions of precisely annotated images to build their complex and powerful representations. Unfortunately, tasks like autonomous driving have virtually no real-world training data. Repeatedly crashing a car into a tree is simply too expensive. The commonly prescribed solution is simple: learn a representation in simulation and transfer it to the real world. However, this transfer is challenging since simulated and real-world visual experiences vary dramatically. Our core observation is that for certain tasks, such as image recognition, datasets are plentiful. They exist in any interesting domain, simulated or real, and are easy to label and extend. We use these recognition datasets to link up a source and target domain to transfer models between them in a task distillation framework. Our method can successfully transfer navigation policies between drastically different simulators: ViZDoom, SuperTuxKart, and CARLA. Furthermore, it shows promising results on standard domain adaptation benchmarks.
Active learning for segmentation based on Bayesian sample queries

Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine.
AutoSimulate: (Quickly) Learning Synthetic Data Generation

Simulation is increasingly being used for generating large labelled datasets in many machine learning problems. Recent methods have focused on adjusting simulator parameters with the goal of maximising accuracy on a validation task, usually relying on REINFORCE-like gradient estimators. However these approaches are very expensive as they treat the entire data generation, model training, and validation pipeline as a black-box and require multiple costly objective evaluations at each iteration. We propose an efficient alternative for optimal synthetic data generation, based on a novel differentiable approximation of the objective. This allows us to optimize the simulator, which may be non-differentiable, requiring only one objective evaluation at each iteration with a little overhead. We demonstrate on a state-of-the-art photorealistic renderer that the proposed method finds the optimal data distribution faster (up to 50 $$\times $$ ), with significantly reduced training data generation and better accuracy on real-world test datasets than previous methods.
Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation

Argument Mining is defined as the task of automatically identifying and extracting argumentative components (e.g., premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, rephrase, no relation). One of the main issues when approaching this problem is the lack of data, and the size of the publicly available corpora. In this work, we use the recently annotated US2016 debate corpus. US2016 is the largest existing argument annotated corpus, which allows exploring the benefits of the most recent advances in Natural Language Processing in a complex domain like Argument (relation) Mining. We present an exhaustive analysis of the behavior of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT and ALBERT) when predicting argument relations. Finally, we evaluate the models in five different domains, with the objective of finding the less domain dependent model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus, and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus.
Using Medical Imaging in Disaster Medicine

The authors examined the features of the use of telemedicine imaging in special conditions in order to determine the potential ways to increase its effectiveness as part of modern medical information and analytical systems for emergency situations.The problem area was identified and the main directions for improving these systems based on the mathematical program analysis of the processing and presentation of medical image information were outlined.
A Novel Technique for Privacy Preservation Using K-Anonymization and Nature Inspired Optimization Algorithms

Thе nеw agе tеchniquеs of cloud computing for procеssing of data is gеnеrally scalablе and sеcurе and to a grеat еxtеnt attracts thе infrastructurе to support big data applications. Howеvеr, thе privacy issuеs posе hindrancе for using thе cloud platforms. Numеrous tеchniquеs arе lеarnt for prеsеrvation of privacy whеrеin data usability and data obfuscation is considеrеd but failеd in balancing thе data privacy and data utility. Naturе-inspirеd mеtahеuristic algorithms arе simplе and flеxiblе and thus now-a-days popular among rеsеarchеrs. Thеsе naturе-inspirеd algorithms arе analysеd in tеrms of thеir kеy fеaturеs likе thеir divеrsity and adaptation, еxploration and еxploitation, and attractions and diffusion mеchanisms. This papеr proposеs an anonymization basеd privacy prеsеrvation modеl using k-anonymization critеria and intеgration of two algorithms - Grеy wolf optimizеr and Cat Swarm Optimization, for attaining privacy prеsеrvation in big data bеforе providing thе data to thе cloud platform. Thе anonymization tеchniquе is procеssеd by adapting k- anonymization critеria for duplicating k-rеcords from thе original databasе. New technique will rеvеal only thе еssеntial dеtails to thе еnd usеrs by hiding thе confidеntial information to offеr a sеcurеd communication. To attain balancе bеtwееn privacy and utility, thе fitnеss function is formulatеd and thе proposеd tеchniquе is comparеd with various еxisting tеchniquеs basеd on thе pеrformancе mеtrics - Classification accuracy and Information loss.
Minimal linear codes from weakly regular plateaued balanced functions

Minimal linear codes have diverse applications in many areas such as secret sharing schemes and secure two-party computation. There are several construction methods for these codes, one of which is based on functions over finite fields. In this paper, to construct minimal codes with few weights, we make use of weakly regular plateaued balanced functions over Fp, where p is an odd prime, in the second generic construction method. We obtain several three-weight and four-weight minimal codes with desirable parameters from these functions. The weight distributions of the obtained codes are completely determined with the help of the Walsh distributions of these functions. We then derive projective three-weight punctured codes from some obtained codes, by deleting some coordinates of the defining sets. It is worth noting that they may include the (almost) optimal codes. We finally analyze the minimum Hamming distances of the dual codes of our minimal codes for secret sharing schemes and association schemes.
GSMNet: Global Semantic Memory Network for Aspect-Level Sentiment Classification

Aspect-level sentiment classification determines the sentiment polarity of a targeted aspect. To solve this task, attention-based neural networks are typically adopted to explore the interaction between the aspect and its context in a single sentence. However, such approaches ignore the rich semantic information that can be obtained from other sentences. This article shows that the contexts of aspects with similar meanings should be considered global semantic information that can be incorporated as domain knowledge. Then, a novel global semantic memory network (GSMNet) is proposed to share the global semantic information of various aspects and generate a domain-specific representation. With the help of domain knowledge, crucial words can be focused on more precisely. Moreover, instead of employing the concatenating operation for vectors before classification, GSMNet adopts a fine-grained information fusion layer to capture the importance of representations for efficiently extracting the valid parts of each dimension. The experimental results demonstrate the effectiveness of our model.
Feedback Recurrent Autoencoder for Video Compression

Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems. Specifically, autoencoder based learned image or video compression solutions are emerging as strong competitors to traditional approaches. In this work, We propose a new network architecture, based on common and well studied components, for learned video compression operating in low latency mode. Our method yields competitive MS-SSIM/rate performance on the high-resolution UVG dataset, among both learned video compression approaches and classical video compression methods (H.265 and H.264) in the rate range of interest for streaming applications. Additionally, we provide an analysis of existing approaches through the lens of their underlying probabilistic graphical models. Finally, we point out issues with temporal consistency and color shift observed in empirical evaluation, and suggest directions forward to alleviate those.
Personalized Course Recommendation Based on Eye-Tracking Technology and Deep Learning

With the rapid development of online courses, the requirements of personalized course recommendation have been increasing. The traditional collaborative filtering algorithm confronts with the challenge of cold start, which is difficult to settle on online course recommendation effectively. In this paper, we propose a novel click through rate (CTR) model for personalized online course recommendation, with discriminative user features, item features and cross features. The feature representation ability of the CTR model is improved and the serious challenge of cold start is alleviated. Furthermore, transfer learning is introduced to deal with the problem of insufficient data in models training. More specially, eye tracking technology is applied to capture the users' cognitive styles, which are visualized with the heat map and fixation point trajectory. Finally, the recommendation interface sent to the learners, according to the user's cognitive style. The experiments show that the novel CTR model improves the performance of the personalized online course recommendation.
Learn Distributed GAN with Temporary Discriminators

In this work, we propose a method for training distributed GAN with sequential temporary discriminators. Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators? We apply our proposed method to learn a self-adaptive generator with a series of local discriminators from multiple data centers. We show our design of loss function indeed learns the correct distribution with provable guarantees. The empirical experiments show that our approach is capable of generating synthetic data which is practical for real-world applications such as training a segmentation model. Our TDGAN Code is available at: https://github.com/huiqu18/TDGAN-PyTorch .
Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction

Abstract Time series forecasting problems are often addressed using linear techniques, especially the autoregressive (AR) models, due to their simplicity combined with good performances. It is possible to generalize a linear predictor by allowing infinite impulse response (IIR) through the addition of feedback loops, as occurs in the autoregressive and moving average (ARMA) models and IIR filters. However, the calculation of the free coefficients of these structures is more complex, as the optimization problem has no closed‐form solution. This work conducts an extensive investigation on the use of linear models to predict monthly seasonal streamflow series associated with Brazilian hydroelectric plants. The main goal is to reach the best achievable performance with linear approaches. We propose the application of recursive models, estimating their parameters with the aid of bioinspired metaheuristics: particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and two immune‐inspired algorithms, the CLONALG, and the artificial immune network for optimization (Opt‐aiNet). The AR model is also considered. The results to multistep ahead forecasting indicated that the insertion of feedback loops increased the performances, with ARMA being the best predictor. The DE, PSO, and GA led to the minimum values of mean‐squared error during the tests, while DE yielded the smallest dispersion.