Reviewerly/exHarmony huggingface dataset:
Fast Geometrically-Perturbed Adversarial Faces

The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications. However, it has recently been shown that a small and carefully crafted perturbation in the input space can completely fool a deep model. In this study, we explore the extent to which face recognition systems are vulnerable to geometrically-perturbed adversarial faces. We propose a fast landmark manipulation method for generating adversarial faces, which is approximately 200 times faster than the previous geometric attacks and obtains 99.86% success rate on the state-of-the-art face recognition models. To further force the generated samples to be natural, we introduce a second attack constrained on the semantic structure of the face which has the half speed of the first attack with the success rate of 99.96%. Both attacks are extremely robust against the state-of-the-art defense methods with the success rate of equal or greater than 53.59%. Code is available at https://github.com/alldbi/FLM.
How Deep Should be the Depth of Convolutional Neural Networks: a Backyard Dog Case Study

The work concerns the problem of reducing a pre-trained deep neuronal network to a smaller network, with just few layers, whilst retaining the network’s functionality on a given task. In this particular case study, we are focusing on the networks developed for the purposes of face recognition. The proposed approach is motivated by the observation that the aim to deliver the highest accuracy possible in the broadest range of operational conditions, which many deep neural networks models strive to achieve, may not necessarily be always needed, desired or even achievable due to the lack of data or technical constraints. In relation to the face recognition problem, we formulated an example of such a use case, the ‘backyard dog’ problem. The ‘backyard dog’, implemented by a lean network, should correctly identify members from a limited group of individuals, a ‘family’, and should distinguish between them. At the same time, the network must produce an alarm to an image of an individual who is not in a member of the family, i.e. a ‘stranger’. To produce such a lean network, we propose a network shallowing algorithm. The algorithm takes an existing deep learning model on its input and outputs a shallowed version of the model. The algorithm is non-iterative and is based on the advanced supervised principal component analysis. Performance of the algorithm is assessed in exhaustive numerical experiments. Our experiments revealed that in the above use case, the ‘backyard dog’ problem, the method is capable of drastically reducing the depth of deep learning neural networks, albeit at the cost of mild performance deterioration. In this work, we proposed a simple non-iterative method for shallowing down pre-trained deep convolutional networks. The method is generic in the sense that it applies to a broad class of feed-forward networks, and is based on the advanced supervise principal component analysis. The method enables generation of families of smaller-size shallower specialized networks tuned for specific operational conditions and tasks from a single larger and more universal legacy network.
Transfer Learning in Breast Mammogram Abnormalities Classification With Mobilenet and Nasnet

Breast cancer has an important incidence in women mortality worldwide. Currently, mammography is considered the gold standard for breast abnormalities screening examinations since it aids in the early detection and diagnosis of the illness. However, both identification of mass lesions and its malignancy classification is a challenging problem for artificial intelligence. Research has turned to the use of deep learning models in mammography which can enhance the performance of Computer Aided Diagnosis Systems (CADx). In this paper, we present our preliminary results on the use of transfer learning for malignancy classification of breast abnormality. We experiment with models that, according to our literature review, have not yet been explored thoroughly such as NasNet and MobileNet. Their performance is compared with InceptionV3 and Resnet50. The best results were obtained with Resnet50 and MobileNet with 78.4% and 74.3%, respectively. Also, some image pre-processing steps are studied in order to increase classification accuracy.
Optimal Pricing Mechanism for Data Market in Blockchain-Enhanced Internet of Things

With the rapid development of the Internet of Things (IoT) in the era of big data, the amount of collected data has increased dramatically. Data are one of the most important commodities in IoT. To maximize the utility of the collected data, it is crucial to design an open IoT data market that enables data owners and consumers to carry out data trading securely and efficiently. To address the challenge of security presented by an untrusted and nontransparent data market, we propose an edge/cloud-computing-assisted, blockchain-enhanced data market framework to support secure and efficient IoT data trading, with a particular focus on an optimal pricing mechanism. In this mechanism, an authorized market-agency works as a scheduler, determining the win-owner and its pricing strategy to the consumer. We formulate a two-stage Stackelberg game to solve the pricing and purchasing problem of the data consumer and the market-agency. In the first stage of the game, the market-agency gives the win-owner and its pricing strategy. In the second stage, the data consumer decides on its purchasing quantity of data. We consider competition between data owners and propose a competition-enhanced pricing scheme (CPS). We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and CPSs. Lastly, we validate the existence and uniqueness of Stackelberg equilibrium, and the numerical results show the efficiency of the CPS.
Personal Knowledge Graphs

Knowledge graphs, organizing structured information about entities, and their attributes and relationships, are ubiquitous today. Entities, in this context, are usually taken to be anyone or anything considered to be globally important. This, however, rules out many entities people interact with on a daily basis. In this position paper, we present the concept of personal knowledge graphs: resources of structured information about entities personally related to its user, including the ones that might not be globally important. We discuss key aspects that separate them for general knowledge graphs, identify the main challenges involved in constructing and using them, and define a research agenda.
Bridging Hamilton-Jacobi Safety Analysis and Reinforcement Learning

Safety analysis is a necessary component in the design and deployment of autonomous robotic systems. Techniques from robust optimal control theory, such as Hamilton-Jacobi reachability analysis, allow a rigorous formalization of safety as guaranteed constraint satisfaction. Unfortunately, the computational complexity of these tools for general dynamical systems scales poorly with state dimension, making existing tools impractical beyond small problems. Modern reinforcement learning methods have shown promising ability to find approximate yet proficient solutions to optimal control problems in complex and high-dimensional systems, however their application has in practice been restricted to problems with an additive payoff over time, unsuitable for reasoning about safety. In recent work, we introduced a time-discounted modification of the problem of maximizing the minimum payoff over time, central to safety analysis, through a modified dynamic programming equation that induces a contraction mapping. Here, we show how a similar contraction mapping can render reinforcement learning techniques amenable to quantitative safety analysis as tools to approximate the safe set and optimal safety policy. This opens a new avenue of research connecting control-theoretic safety analysis and the reinforcement learning domain. We validate the correctness of our formulation by comparing safety results computed through Q-learning to analytic and numerical solutions, and demonstrate its scalability by learning safe sets and control policies for simulated systems of up to 18 state dimensions using value learning and policy gradient techniques.
Erasing Data from Blockchain Nodes

It is a common narrative that blockchains are immutable and so it is technically impossible to erase data stored on them. For legal and ethical reasons, however, individuals and organizations might be compelled to erase locally stored data, be it encoded on a blockchain or not. The common assumption for blockchain networks like Bitcoin is that forcing nodes to erase data contained on the blockchain is equal to permanently restricting them from participating in the system in a full-node role. Challenging this belief, in this paper, we propose and demonstrate a pragmatic approach towards functionality-preserving local erasure (FPLE). FPLE enables full nodes to erase infringing or undesirable data while continuing to store and validate most of the blockchain. We describe a general FPLE approach for UTXO-based (i.e., Bitcoin-like) cryptocurrencies and present a lightweight proof-of-concept tool for safely erasing transaction data from the local storage of Bitcoin Core nodes. Erasing nodes continue to operate in tune with the network even when erased transaction outputs become relevant for validating subsequent blocks. Using only our basic proof-of-concept implementation, we are already able to safely comply with a significantly larger range of erasure requests than, to the best of our knowledge, any other full node operator so far.
Non-Negative Latent Factor Model Based on β-Divergence for Recommender Systems

Non-negative latent factor (NLF) models well represent high-dimensional and sparse (HiDS) matrices filled with non-negative data, which are frequently encountered in industrial applications like recommender systems. However, current NLF models mostly adopt Euclidean distance in their objective function, which represents a special case of a β-divergence function. Hence, it is highly desired to design a β-divergence-based NLF ( β-NLF) model that uses a β-divergence function, and investigate its performance in recommender systems as β varies. To do so, we first model β-NLF's learning objective with a β-divergence function. Subsequently, we deduce a general single latent factor-dependent, non-negative and multiplicative update scheme for β-NLF, and then design an efficient β-NLF algorithm. The experimental results on HiDS matrices from industrial applications indicate that by carefully choosing the value of β, β-NLF outperforms an NLF model with Euclidean distance in terms of accuracy for missing data prediction without increasing computational time. The research outcomes show the necessity of using an optimal β-divergence function in order to achieve the best performance of an NLF model on HiDS matrices. Hence, the proposed model has both theoretical and application significance.
A new class of efficient randomized benchmarking protocols

Abstract Randomized benchmarking is a technique for estimating the average fidelity of a set of quantum gates. However, if this gateset is not the multi-qubit Clifford group, robustly extracting the average fidelity is difficult. Here, we propose a new method based on representation theory that has little experimental overhead and robustly extracts the average fidelity for a broad class of gatesets. We apply our method to a multi-qubit gateset that includes the T -gate, and propose a new interleaved benchmarking protocol that extracts the average fidelity of a two-qubit Clifford gate using only single-qubit Clifford gates as reference.
Wavelet U-Net and the Chromatic Adaptation Transform for Single Image Dehazing

Deep learning has played an important role in various computer vision tasks, and so does in single image dehazing. We think edges and colors are two key factors to obtain better dehazed images. Clear edges and balanced color make the dehazed images look natural and detailed. Therefore, we establish a novel two-stage and end-to-end network that comprises two characteristics. First, we propose the wavelet U-net, which uses discrete wavelet transform and inverse discrete wavelet transform to replace down-sampling and up-sampling, to extract edge features. Second, we apply the chromatic adaptation transform which can be implemented by convolutional layers mathematically to enhance images. Our neural network combining two concepts outperforms the state-of-art algorithms in visual performance and quantitative aspects.
Unsupervised Single Image Deraining with Self-Supervised Constraints

Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality and practicality in real-world multimedia applications. Besides, due to lack of labeled-supervised constraints, directly applying existing unsupervised frameworks to the image deraining task will suffer from low-quality recovery. Therefore, we propose an Unsupervised Deraining Generative Adversarial Network (UD-GAN) to tackle above problems by introducing self-supervised constraints from the intrinsic statistics of unpaired rainy and clean images. Specifically, we design two collaboratively optimized modules, namely Rain Guidance Module (RGM) and Background Guidance Module (BGM), to take full advantage of rainy image characteristics. UD-GAN outperforms state-of-the-art methods on various benchmarking datasets in both quantitative and qualitative comparisons.
Similarity learning with joint transfer constraints for person re-identification

The inconsistency of data distributions among multiple views is one of the most crucial issues which hinder the accuracy of person re-identification. To solve the problem, this paper presents a novel similarity learning model by combining the optimization of feature representation via multi-view visual words reconstruction and the optimization of metric learning via joint discriminative transfer learning. The starting point of the proposed model is to capture multiple groups of multi-view visual words (MvVW) through an unsupervised clustering method (i.e. K-means) from human parts (e.g. head, torso, legs). Then, we construct a joint feature matrix by combining multi-group feature matrices with different body parts. To solve the inconsistent distributions under different views, we propose a method of joint transfer constraint to learn the similarity function by combining multiple common subspaces, each in charge of a sub-region. In the common subspaces, the original samples can be reconstructed based on MvVW under low-rank and sparse representation constraints, which can enhance the structure robustness and noise resistance. During the process of objective function optimization, based on confinement fusion of multi-view and multiple sub-regions, a solution strategy is proposed to solve the objective function using joint matrix transform. Taking all of these into account, the issue of person re-identification under inconsistent data distributions can be transformed into a consistent iterative convex optimization problem, and solved via the inexact augmented Lagrange multiplier (IALM) algorithm. Extensive experiments are conducted on three challenging person re-identification datasets (i.e., VIPeR, CUHK01 and PRID450S), which shows that our model outperforms several state-of-the-art methods.
Super-resolution of PROBA-V images using convolutional neural networks

European Space Aqency (ESA)’s PROBA-V Earth observation (EO) satellite enables us to monitor our planet at a large scale to study the interaction between vegetation and climate, and provides guidance for important decisions on our common global future. However, the interval at which high-resolution images are recorded spans over several days, in contrast to the availability of lower-resolution images which is often daily. We collect an extensive dataset of both high- and low-resolution images taken by PROBA-V instruments during monthly periods to investigate Multi Image Super-resolution, a technique to merge several low-resolution images into one image of higher quality. We propose a convolutional neural network (CNN) that is able to cope with changes in illumination, cloud coverage, and landscape features which are introduced by the fact that the different images are taken over successive satellite passages at the same region. Given a bicubic upscaling of low resolution images taken under optimal conditions, we find the Peak Signal to Noise Ratio of the reconstructed image of the network to be higher for a large majority of different scenes. This shows that applied machine learning has the potential to enhance large amounts of previously collected EO data during multiple satellite passes.
A Survey of DNN Methods for Blind Image Quality Assessment

Blind image quality assessment (BIQA) methods aim to predict quality of images as perceived by humans without access to a reference image. Recently, deep learning methods have gained substantial attention in the research community and have proven useful for BIQA. Although previous study of deep neural networks (DNN) methods is presented, some novelty DNN methods, which are recently proposed, are not summarized for BIQA. In this paper, we provide a survey covering various DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods according to the role of DNN. Then, we compare the prediction performance of various DNN methods on the synthetic databases (LIVE, TID2013, CSIQ, LIVE multiply distorted) and authentic databases (LIVE challenge), providing important information that can help understand the underlying properties between different DNN methods for BIQA. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future.
Controlled gliding and perching through deep-reinforcement-learning

Gliding is an energetically efficient mode of transportation. It is shown that reinforcement learning identifies gliding strategies with minimum energy expenditure and fastest time of arrival, with better performance than model-based optimal control, while being robust to perturbations.
An Automated System of Sentiment Analysis from Bangla Text using Supervised Learning Techniques

Sentiment analysis has become a leading context for scientific and commercial market research in the field of machine learning. Currently, it's a more prominent research field of Bangla language processing system as there are few research works regarding sentiment analysis for this language. In essence, sentiment analysis is an automated process of text mining to determine the emotion from a given text. By using sentiment analysis, a given text can be categorized into several emotions. This paper deals with six individual emotion classes-happy, sad, tender, excited, angry and scared. Here, we proposed two methods of machine learning techniques- Naïve Bayes Classification Algorithm and Topical approach to extract the emotion from any Bangla text. Proposed methods have been applied for both article and sentence level of scope. A comparative analysis of the performance between these two methods has been done, and the topical approach achieved the best performance for both levels of magnitude.
Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues

This paper presents a comprehensive investigation of authentication schemes for smart mobile devices. We start by providing an overview of existing survey articles published in the recent years that deal with security for mobile devices. Then, we give a classification of threat models in smart mobile devices in five categories, including, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulation-based attacks, and service-based attacks. This is followed by a description of multiple existing threat models. We also provide a classification of countermeasures into four types of categories, including, cryptographic functions, personal identification, classification algorithms, and channel characteristics. According to the characteristics of the countermeasure along with the authentication model iteself, we categorize the authentication schemes for smart mobile devices in four categories, namely, (1) biometric-based authentication schemes, (2) channel-based authentication schemes, (3) factors-based authentication schemes, and (4) ID-based authentication schemes. In addition, we provide a taxonomy and comparison of authentication schemes for smart mobile devices in form of tables. Finally, we identify open challenges and future research directions.
Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification

Neural networks lately have achieved a great success on sentiment classification due to their ability of feature extraction. However, it remains as an enormous challenge to model long texts in document-level sentiment classification as well as to explore semantics between sentences and dependencies between features. Moreover, most existing methods can hardly differentiate the importance of different contents when constructing a document representation. To tackle these problems, we propose a novel neural network model: AttDR-2DCNN, which mainly consists of two parts. The first part utilizes a two-layer compositional bidirectional Gated Recurrent Unit (GRU) to obtain the compositional semantics of the document, where the first layer learns the feature vector of the sentence, and the second layer learns the document matrix representation consisting of two dimensions of the time-step dimension and feature dimension, from the sentence vectors. The second part applies a two-dimensional convolution operation and two-dimensional max pooling to capture more dependencies between sentences features. We further utilize different types of attention mechanism in these two parts to distinguish the importance of words, sentences and features in the document. Experiments are conducted on four publicly available document-level review datasets and the result shows that the proposed model outperforms some existing models.
Motivation and E-Learning English as a foreign language: A qualitative study

The motivation to learn a foreign language is a complex process. According to the Ministry of National Education of Colombia -MEN-, speaking English is a generic competence to be developed at all educational levels. In this regard and based on the Theory of Self-Determination -SDT-, this qualitative phenomenological study aims to identify and analyse the aspects related to the motivation to learn English in undergraduate students of the Virtual and Distance modalities -E-Learning-. The main instrument used for data collection was a semi-structured individual interviews. The participants in the research were a group of 16 women and 3 men. The analysis was carried out through semantic categorizations and with the support of NVivo 11 software, which lets assume that motivation for learning English is strongly influenced by external factors.