Reviewerly/exHarmony huggingface dataset:
Meta-Prototypical Learning for Domain-Agnostic Few-Shot Recognition

Few-shot learning (FSL) aims to classify novel images based on a few labeled samples with the help of meta-knowledge. Most previous works address this problem based on the hypothesis that the training set and testing set are from the same domain, which is not realistic for some real-world applications. Thus, we extend FSL to domain-agnostic few-shot recognition, where the domain of the testing task is unknown. In domain-agnostic few-shot recognition, the model is optimized on data from one domain and evaluated on tasks from different domains. Previous methods for FSL mostly focus on learning general features or adapting to few-shot tasks effectively. They suffer from inappropriate features or complex adaptation in domain-agnostic few-shot recognition. In this brief, we propose meta-prototypical learning to address this problem. In particular, a meta-encoder is optimized to learn the general features. Different from the traditional prototypical learning, the meta encoder can effectively adapt to few-shot tasks from different domains by the traces of the few labeled examples. Experiments on many datasets demonstrate that meta-prototypical learning performs competitively on traditional few-shot tasks, and on few-shot tasks from different domains, meta-prototypical learning outperforms related methods.
Classification of Deepfake Videos Using Pre-trained Convolutional Neural Networks

The advancement of Artificial Intelligence (AI) has brought a revolution in the field of information technology. Furthermore, AI has empowered the new applications to run with minimum resources and computational cost. One of such applications is Deepfakes, which produces extensively altered and modified multimedia content. However, such manipulated visual data imposed a severe threat to the security and privacy of people and can cause massive sect, religious, political, and communal stress around the globe. Now, the face-swapped base visual content is difficult to recognizable by humans through their naked eyes due to the advancement of Generative adversarial networks (GANs). Therefore, identifying such forgeries is a challenging task for the research community. In this paper, we have introduced a pipeline for identifying and detecting person faces from input visual samples. In the second step, several deep learning (DL) based approaches are employed to compute the deep features from extracted faces. Lastly, a classifier namely SVM is trained over these features to classify the data as real or manipulated. We have performed the performance comparison of various feature extractors and confirmed from reported results that DenseNet-169 along with SVM classifier outperforms the rest of the methods.
Harnessing high-level concepts, visual, and auditory features for violence detection in videos

In detecting sensitive media, violence is one of the hardest to define objectively, and thus, a significant challenge to detect automatically. While many studies were conducted in detecting aspects of violence, very few try to approach the general concept. We propose a method that aims to enable machines to understand a high-level concept of violence by first breaking it down into smaller, more objective ones, such as fights, explosions, blood, and gunshots, to combine them later, leading to a better understanding of the scene. For this, we leverage characteristics of each individual sub-concept of violence (relying upon custom-tailored convolutional neural networks) to guide how they should be described. A fight scene should incorporate temporal features that a scene with blood does not need to describe. A scene with explosions or gunshots should weigh more on its audio features. With this multimodal approach, we trained visual and auditory feature detectors and later combined them into a decision neural network to give us a violence detector that considers several different aspects of the problem. This robust and modular approach allows different cultures and users to adapt the detector to their specific needs.
Technological troubleshooting based on sentence embedding with deep transformers

Abstract In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine.
Data Augmentation Strategies for Human Activity Data Using Generative Adversarial Neural Networks

Previous studies have shown that available benchmark datasets from the field of Human Activity Recognition are of limited use for Deep Learning applications. This can be traced back to issues in the quality, the scope, as well as in the variability of the datasets. These limitations often lead to overfitting of networks and thus to results that are only conditionally generalizable. One way to counteract this problem is to extend the data by using data augmentation techniques. This paper presents an algorithm and compares two augmentation strategies: (1) user-wise augmentation and (2) fold-wise augmentation to extend the size of a dataset here shown on the PAMAP2 dataset, with an arbitrary number of synthetic samples. These synthesized data resemble the user- and activity-specific characteristics and fit seamlessly into the dataset. They are created by a recurrent Generative Adversarial Network, with both the generator and discriminator modeled by a set of LSTM cells to produce the synthetic time-series data. In our evaluation, we trained four DeepConvLSTM models with supervised learning, three times with a LOSO cross-validation: one baseline model and two times with additional data but different augmentation strategies, as well as one model without cross-validation that monitors the synthesized data quality. The compared augmentation strategies demonstrate the impact as well as the generalized nature of the augmented data. By increasing the size of the dataset by factor 5, we improved the F1-Score by 11.0% with strategy (1) and 5.1% with strategy (2).
Analytical Review of Data Security in Cloud Computing

All the organization wants to implement the same security parameters as they are already using with their internal data/resources. It is mandatory to understand and find the data protection challenges before outsourcing the data security in cloud computing. In present research we are discussing about the impact of security in cloud computing including all the challenges associated with it.
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models

Benefiting from large-scale pre-training, we have witnessed significant performance boost on the popular Visual Question Answering (VQA) task. Despite rapid progress, it remains unclear whether these state-of-the-art (SOTA) models are robust when encountering examples in the wild. To study this, we introduce Adversarial VQA, a new large-scale VQA benchmark, collected iteratively via an adversarial human-and-model-in-the-loop procedure. Through this new benchmark, we discover several interesting findings. (i) Surprisingly, we find that during dataset collection, non-expert annotators can easily attack SOTA VQA models successfully. (ii) Both large-scale pre-trained models and adversarial training methods achieve far worse performance on the new benchmark than over standard VQA v2 dataset, revealing the fragility of these models while demonstrating the effectiveness of our adversarial dataset. (iii) When used for data augmentation, our dataset can effectively boost model performance on other robust VQA benchmarks. We hope our Adversarial VQA dataset can shed new light on robustness study in the community and serve as a valuable benchmark for future work.
Elastic Net Nonparallel Hyperplane Support Vector Machine and Its Geometrical Rationality

Twin support vector machine (TWSVM), which constructs two nonparallel classifying hyperplanes, is widely applied to various fields. However, TWSVM solves two quadratic programming problems (QPPs) separately such that the final classifiers lack consistency and enough prediction accuracy. Moreover, by reason of only considering the 1-norm penalty for slack variables, TWSVM is not well defined in the geometrical view. In this article, we propose a novel elastic net nonparallel hyperplane support vector machine (ENNHSVM), which adopts elastic net penalty for slack variables and constructs two nonparallel separating hyperplanes simultaneously. We further discuss the properties of ENNHSVM theoretically and derive the violation tolerance upper bound to better demonstrate the relative violations of training samples in the same class. In particular, we design a safe screening rule for ENNHSVM to speed up the calculations. We finally compare the performance of ENNHSVM on both synthetic datasets and benchmark datasets with the Lagrangian SVM, the twin parametric-margin SVM, the elastic net SVM, the TWSVM, and the nonparallel hyperplane SVM.
Self-Guided Body Part Alignment With Relation Transformers for Occluded Person Re-Identification

Person re-identification in the wild is often challenged by occlusion. Existing methods mainly rely on learned external cues like pose or parsing to ease occlusion distraction. This knowledge highly related to body semantics may introduce alignment effects, leading to additional requirements for dedicated training data and inference computation. We propose the Self-guided Body Part Alignment method that learns cue-free semantic-aligned local prediction for feature representations to avoid high-cost dependence on external cues. First, scale-wise global spatial attention is utilized to determine essential body parts automatically. A relation transformer network is then employed to predict semantic-aligned local parts, guided with anchored global information by constraint loss. Similarity metrics for all parts are merged with threshold conditions to filter invisible body parts comprehensively. Experimental results on occluded and holistic person reID benchmarks show the proposed method outperforms other cue-relied and cue-free methods. As far as we know, this is the first method that applies transformer networks on local predictions for occluded reID tasks.
Blockchain Technology in Healthcare: Opportunities and Challenges

There are a few issues found because of the absence of traceability of transactions in healthcare services. Healthcare information split across numerous silos adversely influences research activities and administrations. There were no reported cases of about half of the clinical preliminaries. The expense of medication disclosure is ever-expanding, and unsatisfactory and fake medicines are as yet an enormous issue. Blockchain has the potential to address such substantial problems. Blockchain technology is a distributed and secure database organized by various groups to store and offers a permanent online transaction record. Blockchain has numerous medicinal services applications and can improve mobile health applications, preliminary clinical information, and insurance information storage. Each of the blocks has an independent unit holding its report and a dependent link that makes regulated by participants who store and share the data without third-party intervention. The blockchain technology permits members to transfer information progressively without presenting the channels to theft, malice, and forgery. In this work, we review major blockchain healthcare applications in some areas of patient information management, supply chain management of clinical goods, pharmaceutical investigation, and telemedicine related to healthcare activities.
From Design Requirements to Effective Privacy Notifications: Empowering Users of Online Services to Make Informed Decisions

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Device-Cloud Collaborative Learning for Recommendation

With the rapid development of storage and computing power on mobile devices, it becomes critical and popular to deploy models on devices to save onerous communication latencies and to capture real-time features. While quite a lot of works have explored to facilitate on-device learning and inference, most of them focus on dealing with response delay or privacy protection. Little has been done to model the collaboration between the device and the cloud modeling and benefit both sides jointly. To bridge this gap, we are among the first attempts to study the Device-Cloud Collaborative Learning (DCCL) framework. Specifically, we propose a novel MetaPatch learning approach on the device side to efficiently achieve "thousands of people with thousands of models'' given a centralized cloud model. Then, with billions of updated personalized device models, we propose a "model-over-models'' distillation algorithm, namely MoMoDistill, to update the centralized cloud model. Our extensive experiments over a range of datasets with different settings demonstrate the effectiveness of such collaboration on both cloud and devices, especially its superiority to model long-tailed users.
Unitary channel discrimination beyond group structures: Advantages of sequential and indefinite-causal-order strategies

For minimum-error channel discrimination tasks that involve only unitary channels, we show that sequential strategies may outperform the parallel ones. Additionally, we show that general strategies that involve indefinite causal order are also advantageous for this task. However, for the task of discriminating a uniformly distributed set of unitary channels that forms a group, we show that parallel strategies are, indeed, optimal, even when compared to general strategies. We also show that strategies based on the quantum switch cannot outperform sequential strategies in the discrimination of unitary channels. Finally, we derive an absolute upper bound for the maximal probability of successfully discriminating any set of unitary channels with any number of copies for the most general strategies that are suitable for channel discrimination. Our bound is tight since it is saturated by sets of unitary channels forming a group k-design.
Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization

Partial label learning deals with training examples each associated with a set of candidate labels, among which only one is valid. Most existing works focus on manipulating the label space by estimating the labeling confidences of candidate labels, while the task of manipulating the feature space by dimensionality reduction has been rarely investigated. In this paper, a novel partial label dimensionality reduction approach named CENDA is proposed via confidence-based dependence maximization. Specifically, CENDA adapts the Hilbert-Schmidt Independence Criterion (HSIC) to help identify the projection matrix, where the dependence between projected feature information and confidence-based labeling information is maximized iteratively. In each iteration, the projection matrix admits closed-form solution by solving a tailored generalized eigenvalue problem, while the labeling confidences of candidate labels are updated by conducting kNN aggregation in the projected feature space. Extensive experiments over a broad range of benchmark data sets show that the predictive performance of well-established partial label learning algorithms can be significantly improved by coupling with the proposed dimensionality reduction approach.
Solving archaeological puzzles

This paper focuses on the re-assembly of an archaeological artifact, given images of its fragments. This problem can be considered as a special challenging case of puzzle solving. The restricted case of re-assembly of a natural image from square pieces has been investigated extensively and was shown to be a difficult problem in its own right. Likewise, the case of matching “clean” 2D polygons/splines based solely on their geometric properties has been studied. But what if these ideal conditions do not hold? This is the problem addressed in the paper. Three unique characteristics of archaeological fragments make puzzle solving extremely difficult: (1) The fragments are of general shape; (2) They are abraded, especially at the boundaries (where the strongest cues for matching should exist); and (3) The domain of valid transformations between the pieces is continuous. The key contribution of this paper is a fully-automatic and general algorithm that addresses puzzle solving in this intriguing domain. We show that our approach manages to correctly reassemble dozens of broken artifacts and frescoes.
Variance aware reward smoothing for deep reinforcement learning

A Reinforcement Learning (RL) agent interacts with the environment to learn a policy with high accumulated rewards through attempts and failures. However, RL suffers from its own trial-and-error learning nature, which results in an unstable learning process. In this paper, we investigate a common phenomenon called rewards drop at the late-stage RL training session, where the rewards trajectory oscillates dramatically. In order to solve such a problem, we propose a novel rewards shaping technique named Variance Aware Rewards Smoothing (VAR). We show that the proposed method reduces the variance of rewards and mitigates the rewards drop problem without changing the formulation of the value function. Furthermore, the theoretical analysis of convergence of VAR is provided, which is derived from the γ-contraction operator and the fixed point attribute of the value function. Finally, the theoretical results are illustrated by extensive results on various benchmarks and advanced algorithms across different random seeds to demonstrate the effectiveness and the compatibility of VAR.
Unsupervised Video Person Re-Identification via Noise and Hard Frame Aware Clustering

Unsupervised video-based person re-identification (re-ID) methods extract richer features from video tracklets than image-based ones. The state-of-the-art methods utilize clustering to obtain pseudo-labels and train the models iteratively. However, they underestimate the influence of two kinds of frames in the tracklet: 1) noise frames caused by detection errors or heavy occlusions exist in the tracklet, which may be allocated with unreliable labels during clustering; 2) the tracklet also contains hard frames caused by pose changes or partial occlusions, which are difficult to distinguish but informative. This paper proposes a Noise and Hard frame Aware Clustering (NHAC) method. NHAC consists of a graph trimming module and a node re-sampling module. The graph trimming module obtains stable graphs by removing noise frame nodes to improve the clustering accuracy. The node re-sampling module enhances the training of hard frame nodes to learn rich tracklet information. Experiments conducted on two video-based datasets demonstrate the effectiveness of the proposed NHAC under the unsupervised re-ID setting.
Blockchain-based Privacy-Preserving Record Linkage: enhancing data privacy in an untrusted environment

Privacy-Preserving Record Linkage (PPRL) intends to integrate private data from several data sources held by different parties. Due to recent laws and regulations (e.g, General Data Protection Regulation), PPRL approaches are increasingly demanded in real-world application areas such as health-care, credit analysis, public policy evaluation, and national security. However, the majority of the PPRL approaches consider an unrealistic adversary model, particularly the Honest but Curious (HBC) model, which assumes that all PPRL parties will follow a pre-agreed data integration protocol, and will not try to break the confidentiality of the data handled during the process. The HBC model is hard to employ in real-world applications, mainly because of the need to trust other parties fully. To overcome the limitations associated with the majority of the adversary models considered by PPRL approaches, we propose a protocol that considers covert adversaries, i.e., adversaries that may deviate arbitrarily from the protocol specification in an attempt to cheat. In such protocol, however, the honest parties are able to detect this misbehavior with a high probability. To provide a proof-of-concept implementation of this protocol, we employ the Blockchain technology and propose an improvement in the most used anonymization technique for PPRL, the Bloom Filter. The evaluation carried out using several real-world data sources has demonstrated the effectiveness (linkage quality) obtained by our contributions, as well as the ability to detect the misbehavior of a malicious adversary during the PPRL execution.
Conditional Sentence Generation and Cross-Modal Reranking for Sign Language Translation

Sign Language Translation (SLT) aims to generate spoken language translations from sign language videos. Currently, the available sign language datasets are relatively too small to learn the linguistic properties of spoken language. In this paper, towards effective SLT, we propose a novel framework which takes the advantage of the spoken language grammar learnt from a large corpus of text sentences. Our framework consists of three key modules: word existence verification, conditional sentence generation and cross-modal re-ranking. We first check the existence of words in the vocabulary by a series of binary classification in parallel. After that, the appearing words are assembled and guided by a pretrained spoken language generator to produce multiple candidate sentences in spoken language manner. Last but not least, we select the sentence most semantically similar to the input sign video as the translation result with a crossmodal re-ranking model. We evaluate our framework on two large scale continuous SLT benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , CSL and RWTHPHOENIX-Weather 2014 T. Experimental results demonstrate that the proposed framework achieves promising performance on both datasets.