Reviewerly/exHarmony huggingface dataset:
Robust source camera identification against adversarial attacks

Application of Deep Neural Networks (DNN) has dramatically improved the performance of Source Camera Identification (SCI), but easily suffers from adversarial attacks. These attacks raise security problems by tampering the identified outcomes with imperceptible noise. To address this issue, we analyze the feature extraction mapping for DNN-based SCI models on manifolds and discover that the vulnerability comes from the oscillation of the mapping. In light of this, we take that the feature extraction mapping should satisfy locally smooth and information monotonicity as a new design principle for robust SCI, and accordingly developed a defensive scheme. The proposed scheme constructs local smooth mapping that guarantees information monotonicity and achieves sufficient statistics by minimizing Kullback Leibler Divergence (KLD) between the local statistic coordinates on two manifolds. To enhance the usability of our method, we implement it with a Pre-Defense Network (PDN) trained by a two-phase training strategy, which ensures robustness, accuracy, and portability. Experiments on Dresden Image Dataset demonstrate that the proposed defense method offers not only strong robustness for the DNN-based SCI model against adversarial attacks, but also yields comparable or even superior identification performance over existing defense methods. Moreover, PDN also shows defense effect when migrated to other DNN-based SCI models, without extra retraining.
Combined Convolutional and Perceptron Neural Networks for Handwritten Digits Recognition

The use of a combination of a convolutional neural network and multilayer perceptrons for recognizing handwritten digits is considered. Recognition is carried out by two sets of networks following each other. The first neural network selects two digits with maximum activation functions. Depending on the winners, the following network is activated (multilayer perceptron), which selects one digit from two. The proposed algorithm is tested on the data from MNIST. The recognition error is 0.75%. Obtained results demonstrate that the minimum error with this approach is 0.68%, and the accuracy of the F-metric is about 0.99 for each digit. The main feature of the proposed solution is dealt with the fact that the proposed cascaded combination of neural networks provides a sufficiently high accuracy with a simple architecture.
When Automatic Voice Disguise Meets Automatic Speaker Verification

The technique of transforming voices in order to hide the real identity of a speaker is called voice disguise, among which automatic voice disguise (AVD) by modifying the spectral and temporal characteristics of voices with miscellaneous algorithms are easily conducted with softwares accessible to the public. AVD has posed great threat to both human listening and automatic speaker verification (ASV). In this paper, we have found that ASV is not only a victim of AVD but could be a tool to beat some simple types of AVD. Firstly, three types of AVD, pitch scaling, vocal tract length normalization (VTLN) and voice conversion (VC), are introduced as representative methods. State-of-the-art ASV methods are subsequently utilized to objectively evaluate the impact of AVD on ASV by equal error rates (EER). Moreover, an approach to restore disguised voice to its original version is proposed by minimizing a function of ASV scores w.r.t. restoration parameters. Experiments are then conducted on disguised voices from Voxceleb, a dataset recorded in real-world noisy scenario. The results have shown that, for the voice disguise by pitch scaling, the proposed approach obtains an EER around 7% comparing to the 30% EER of a recently proposed baseline using the ratio of fundamental frequencies. The proposed approach generalizes well to restore the disguise with nonlinear frequency warping in VTLN by reducing its EER from 34.3% to 18.5%. However, it is difficult to restore the source speakers in VC by our approach, where more complex forms of restoration functions or other paralinguistic cues might be necessary to restore the nonlinear transform in VC. Finally, contrastive visualization on ASV features with and without restoration illustrate the role of the proposed approach in an intuitive way.
An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree

The Time-based One-Time Password (TOTP) algorithm is commonly used for two-factor authentication. In this algorithm, a shared secret is used to derive a One-Time Password (OTP). However, in TOTP, the client and the server need to agree on a shared secret (i.e., a key). As a consequence, an adversary can construct an OTP through the compromised key if the server is hacked. To solve this problem, Kogan et al. proposed T/Key, an OTP algorithm based on a hash chain. However, the efficiency of OTP generation and verification is low in T/Key. In this article, we propose a novel and efficient Merkle tree-based One-Time Password (MOTP) algorithm to overcome such limitations. Compared to T/Key, this proposal reduces the number of hash operations to generate and verify the OTP, at the cost of small server storage and tolerable client storage. Experimental analysis and security evaluation show that MOTP can resist leakage attacks against the server and bring a tiny delay to two-factor authentication and verification time.
Non-uniform DNN Structured Subnets Sampling for Dynamic Inference

With the success of Deep Neural Networks (DNN), many recent works have been focusing on developing hardware accelerator for power and resource-limited system via model compression techniques, such as quantization, pruning, low-rank approximation and etc. However, almost all existing compressed DNNs are fixed after deployment, which lacks run-time adaptive structure to adapt to its dynamic hardware resource allocation, power budget, throughput requirement, as well as dynamic workload. As the countermeasure, to construct a novel run-time dynamic DNN structure, we propose a novel DNN sub-network sampling method via non-uniform channel selection for subnets generation. Thus, user can trade off between power, speed, computing load and accuracy on-the-fly after the deployment, depending on the dynamic requirements or specifications of the given system. We verify the proposed model on both CIFAR-10 and ImageNet dataset using ResNets, which outperforms the same sub-nets trained individually and other related works. It shows that, our method can achieve latency trade-off among 13.4, 24.6, 41.3, 62.1(ms) and 30.5, 38.7, 51, 65.4(ms) for GPU with 128 batch-size and CPU respectively on ImageNet using ResNet18.
A spam worker detection approach based on heterogeneous network embedding in crowdsourcing platforms

Due to the popularity of crowdsourcing, more crowds are participating in crowdsourcing tasks. However, the proportion of spam workers is continuously increasing due to the openness of crowdsourcing platforms and their incentive mechanisms. To defend against threats from spam workers, researchers have proposed reputation-based and verification-based detection methods, but they either cannot address various collusion patterns or are costly when facing a large number of spam workers with "good" reputations due to collusion. Therefore, we propose a spam worker detection approach based on heterogeneous network embedding. We first model three collusion patterns and analyze the characteristics of spam workers to provide a theoretical basis for detecting spam workers. We then transform the problem of spam worker detection into a node classification problem in a crowdsourcing heterogeneous network in which the vectors of worker nodes are learned using network embedding. To improve the efficiency of network embedding, we propose an improved variable-length random walk algorithm based on node centrality. Finally, based on the obtained vectors of worker nodes, a one-class SVM is used to detect spam workers. The experiments demonstrate that our proposed approach can effectively detect spam workers in different collusion patterns and that the proposed random walk algorithm can reduce the time spent on model training while improving the efficiency of network embedding.
Deep Local Feature Descriptor Learning With Dual Hard Batch Construction

Local feature descriptor learning aims to represent distinctive images or patches with the same local features, where their representation is invariant under different types of deformation. Recent studies have demonstrated that descriptor learning based on Convolutional Neural Network (CNN) is able to improve the matching performance significantly. However, they tend to ignore the importance of sample selection during the training process, leading to unstable quality of descriptors and learning efficiency. In this paper, a dual hard batch construction method is proposed to sample the hard matching and non-matching examples for training, improving the performance of the descriptor learning on different tasks. To construct the dual hard training batches, the matching examples with the minimum similarity are selected as the hard positive pairs. For each positive pair, the most similar non-matching example is then sampled from the generated hard positive pairs in the same batch as the corresponding negative. By sampling the hard positive pairs and the corresponding hard negatives, the hard batches are produced to force the CNN model to learn the descriptors with more efforts. In addition, based on the above dual hard batch construction, an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> triplet loss function is built for optimizing the training model. Specifically, we analyze the superiority of the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> loss function when dealing with hard examples, and also demonstrate it in the experiments. With the benefits of the proposed sampling strategy and the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> triplet loss function, our method achieves better performance compared to state-of-the-art on the reference benchmarks for different matching tasks.
Towards Data Sovereignty of Asset Administration Shells across Value Added Chains

Major benefit of Digital Twins lies in collaboration across value added chains based on semantic interoperability. The concept of the Asset Administration Shell (AAS) enables sharing of semantically enriched information in Industrie 4.0 application scenarios and beyond. Nevertheless, a technical infrastructure is needed that can ensure data sovereignty, i.e. integrity, confidentiality and availability of information shared across business partners in value added chains. This paper presents the current state of work on such a technical infrastructure for Asset Administration Shells (AAS). It combines three technologies: a blockchain books who changed an AAS and when, a peer-to-peer (P2P) distributed file system encrypts and stores each version of an AAS and a version-control system tracks changes to an AAS in detail. An exemplary value added chain is defined, which will be implemented in a demonstrator. This demonstrator will be used to evaluate and improve the proposed technical infrastructure for collaboration of AAS across value added chains.
Age-aware Fairness in Blockchain Transaction Ordering

In blockchain applications, transaction latency is crucial for determining the quality of service (QoS). Transaction latency is measured as the time between its issuance and its inclusion in a block in the chain. When different applications use the same blockchain network, a block proposer often prioritizes its own application transactions over other applications transactions to minimize its own latency. To maintain fairness, a block proposer is typically supposed to select the included transactions randomly providing each transaction similar chances to be included. The random selection might cause some transactions to experience high latency since this selection implies a high variance in the time a transaction waits until it is selected. We suggest an alternative, age-aware approach towards fairness so that transaction priority is increased upon observing a large waiting time. The challenge with this approach is that the age of a transaction is not absolute due to transaction propagation. Moreover, a node might present its transactions as older to obtain priority. We consider three network restrictions on transaction propagation and explain how to enhance fairness in each one of them. We describe three declaration schemes in which a node declares its pending transactions providing the ability to validate transaction age. We demonstrate the advantages of the solutions on Ethereum and synthetic data in reducing tail latency.
Few Is Enough: Task-Augmented Active Meta-learning for Brain Cell Classification

Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat this issue poses a significant cost. Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process. In this paper, we propose a tAsk-auGmented actIve meta-LEarning (AGILE) method to efficiently adapt DNNs to new tasks by using a small number of training examples. AGILE combines a meta-learning algorithm with a novel task augmentation technique which we use to generate an initial adaptive model. It then uses Bayesian dropout uncertainty estimates to actively select the most difficult samples when updating the model to a new task. This allows AGILE to learn with fewer tasks and a few informative samples, achieving high performance with a limited dataset. We perform our experiments using the brain cell classification task and compare the results to a plain meta-learning model trained from scratch. We show that the proposed task-augmented meta-learning framework can learn to classify new cell types after a single gradient step with a limited number of training samples. We show that active learning with Bayesian uncertainty can further improve the performance when the number of training samples is extremely small. Using only 1% of the training data and a single update step, we achieved 90% accuracy on the new cell type classification task, a 50% points improvement over a state-of-the-art meta-learning algorithm.
UAV-Borne 2-D and 3-D Radar-Based Grid Mapping

For unmanned aerial vehicles (UAVs), grid maps can be a versatile tool for navigation and self-localization. In general, payload is critical for UAVs and every additional sensor limits the flight duration. Due to its robustness and the ability to directly measure velocities, radar sensors are well suited for sense and avoid applications (SAAs) for UAVs. It would be advantageous if these sensor data could be used to generate grid maps instead of mounting additional sensors such as light detection and ranging (LiDAR). This letter demonstrates that using the data from high-resolution multiple-input–multiple-output (MIMO) imaging radars, high-resolution 2-D and 3-D radar grid maps can be created. The necessary adaption of the sensors free-space model for MIMO radar-based occupancy grid maps is presented in detail. UAV-borne measurements resulting in 2-D and 3-D grid maps with an adequate representation of the environment validate this approach.
Anisotropic diffusion filtering through multi-objective optimization

Images are often corrupted by noise during their process of acquisition, storage and transmission. This alteration usually deteriorates the perception quality of the image. The central challenge in image denoising corresponds to eliminating the corrupted information while it is maintained the fine features of the original image. Anisotropic Diffusion (AD) is considered a well-established scheme for removing noise in digital images without deteriorating their edges. The selection of parameters that define the AD operation presents a very decisive implication in the filtering results. In spite of the importance of the AD operation, the problem of automatically calculating its parameter according to the image requirements has been scarcely considered in the literature. In this paper, a new multi-objective methodology to obtain the AD parameters for effective filter results is presented. In the proposed approach, the appropriate AD parameter values are obtained as the solution that involves the best possible trade-off between two conflicting scenarios: To minimize the noise content in the image while the contrast is maximized. Both objectives cannot be individually improved without worsening another. The proposed scheme uses to solve this formulation of the Non-dominated sorting genetic algorithm based on reference points (NSGA-III), which represent one of the most robust and powerful algorithms for multi-objective optimization. Then, the final solution is obtained from an analysis of the Optimal Pareto front. Experimental results demonstrate that the proposed method presents better results than existing filtering algorithms in terms of visual quality and standard performance metrics.
Feedback generation using Fuzzy Cognitive Maps to reduce dropout in situation-aware e-Learning systems

E-learning is becoming increasingly popular among learners of any age, thanks to its many advantages related to the possibility to learn anywhere and anytime and the low costs. Despite this success and diffusion, the student dropout rate of e-learning systems is very high. The main reason is the lack of motivation and engagement of the students with the online course. An adequate design of the e-learning system and its capability to adapt to the characteristics of the learners can reduce the dropout rate. In this work, we propose an approach, based on Fuzzy Cognitive Map, to identify the situation of the learner (mainly in terms of motivation and engagement) and to provide the learners with a set of feedback aiming at improving the retention of the learners in a situation-aware adaptive learning system. A prototypical system is proposed to preliminary verify the feasibility and utility of the approach.
Research on Data Augmentation for Object Detection Based on X- ray Security Inspection Picture

The deep learning model has a large number of parameters that need to be adjusted during the training process, so training a model that can perform well in object detection requires a large amount of valid data. And the accuracy and generalization ability of the model are guaranteed by both quantity and diversity of data. However, it's a huge expense to get pictures and then annotate them, and we can hardly ever get all types of data that exist in reality. Apparently, data augmentation is an effective way to solve this problem. In our paper, the data set we use is composed of x-ray pictures come from subway security inspection. Based on the characteristics of x- ray pictures, we conducted experiments on a variety of augmentation methods, aimed at finding suitable data augmentation methods for this data set. After the experiment, it is concluded that the augmentation method that conforms to the actual form of the data set is more effective. Compared with the monotonous data augmentation, the hybrid way can be more effective, which can improve the accuracy and generalization ability of the model better. Meanwhile the complexity of backbone can also affect data augmentation, more complex backbone can be more sensitive to data augmentation.
An Enhanced Sentiment Analysis Framework Based on Pre-Trained Word Embedding

Sentiment analysis (SA) is a technique that lets people in different fields such as business, economy, research, government, and politics to know about people’s opinions, which greatly affects the process of decision-making. SA techniques are classified into: lexicon-based techniques, machine learning techniques, and a hybrid between both approaches. Each approach has its limitations and drawbacks, the machine learning approach depends on manual feature extraction, lexicon-based approach relies on sentiment lexicons that are usually unscalable, unreliable, and manually annotated by human experts. Nowadays, word-embedding techniques have been commonly used in SA classification. Currently, Word2Vec and GloVe are some of the most accurate and usable word embedding techniques, which can transform words into meaningful semantic vectors. However, these techniques ignore sentiment information of texts and require a huge corpus of texts for training and generating accurate vectors, which are used as inputs of deep learning models. In this paper, we propose an enhanced ensemble classifier framework. Our framework is based on our previously published lexicon-based method, bag-of-words, and pre-trained word embedding, first the sentence is preprocessed by removing stop-words, POS tagging, stemming and lemmatization, shortening exaggerated word. Second, the processed sentence is passed to three modules, our previous lexicon-based method (Sum Votes), bag-of-words module and semantic module (Word2Vec and Glove) and produced feature vectors. Finally, the previous features vectors are fed into 11 different classifiers. The proposed framework is tested and evaluated over four datasets with five different lexicons, the experiment results show that our proposed model outperforms the previous lexicon based and the machine learning methods individually.
Meta Corrupted Pixels Mining for Medical Image Segmentation

Deep neural networks have achieved satisfactory performance in piles of medical image analysis tasks. However the training of deep neural network requires a large amount of samples with high-quality annotations. In medical image segmentation, it is very laborious and expensive to acquire precise pixel-level annotations. Aiming at training deep segmentation models on datasets with probably corrupted annotations, we propose a novel Meta Corrupted Pixels Mining (MCPM) method based on a simple meta mask network. Our method is targeted at automatically estimate a weighting map to evaluate the importance of every pixel in the learning of segmentation network. The meta mask network which regards the loss value map of the predicted segmentation results as input, is capable of identifying out corrupted layers and allocating small weights to them. An alternative algorithm is adopted to train the segmentation network and the meta mask network, simultaneously. Extensive experimental results on LIDC-IDRI and LiTS datasets show that our method outperforms state-of-the-art approaches which are devised for coping with corrupted annotations.