Reviewerly/exHarmony huggingface dataset:
Learning More Universal Representations for Transfer-Learning

A representation is supposed universal if it encodes any element of the visual world (e.g., objects, scenes) in any configuration (e.g., scale, context).While not expecting pure universal representations, the goal in the literature is to improve the universality level, starting from a representation with a certain level.To do so, the state-of-the-art consists in learning CNN-based representations on a diversified training problem (e.g., ImageNet modified by adding annotated data).While it effectively increases universality, such approach still requires a large amount of efforts to satisfy the needs in annotated data.In this work, we propose two methods to improve universality, but pay special attention to limit the need of annotated data.We also propose a unified framework of the methods based on the diversifying of the training problem.Finally, to better match Atkinson's cognitive study about universal human representations, we proposed to rely on the transfer-learning scheme as well as a new metric to evaluate universality.This latter, aims us to demonstrates the interest of our methods on 10 target-problems, relating to the classification task and a variety of visual domains.
Towards a more reliable privacy-preserving recommender system

This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept private in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacrifice too much accuracy for privacy.
How to Improve Your Speaker Embeddings Extractor in Generic Toolkits

Recently, speaker embeddings extracted with deep neural networks became the state-of-the-art method for speaker verification. In this paper we aim to facilitate its implementation on a more generic toolkit than Kaldi, which we anticipate to enable further improvements on the method. We examine several tricks in training, such as the effects of normalizing input features and pooled statistics, different methods for preventing overfitting as well as alternative non-linearities that can be used instead of Rectifier Linear Units. In addition, we investigate the difference in performance between TDNN and CNN, and between two types of attention mechanism. Experimental results on Speaker in the Wild, SRE 2016 and SRE 2018 datasets demonstrate the effectiveness of the proposed implementation.
Big Data: Hadoop framework vulnerabilities, security issues and attacks

Big Data is a collection of different hardware and software technologies, which have heterogeneous infrastructure. Hadoop framework plays a leading role in storing and processing Big Data. It offers fast and cost-effective solution for Big Data and is used in different sectors like healthcare, insurance and social media. Hadoop is an open source framework based on a distributed computing model and is applied for processing and storing data on a cluster of commodity computers. Due to the flexibility of framework, some vulnerabilities arise. These vulnerabilities are threats to the data and lead to attacks. In this paper, different types of vulnerabilities are discussed and possible solutions are provided to reduce or eliminate these vulnerabilities. The experiment setup used to perform common attacks to understand the concept and implementation of a solution to avoid those attacks is presented. The results show the effect of attacks on the performance. According to results, there is need to protect data using defense-in-depth to security.
Redesigning Bitcoin's fee market

The Bitcoin payment system involves two agent types: Users that transact with the currency and pay fees and miners in charge of authorizing transactions and securing the system in return for these fees. Two of Bitcoin's challenges are (i) securing sufficient miner revenues as block rewards decrease, and (ii) alleviating the throughput limitation due to a small maximal block size cap. These issues are strongly related as increasing the maximal block size may decrease revenue due to Bitcoin's pay-your-bid approach. To decouple them, we analyze the “monopolistic auction” [8], showing: (i) its revenue does not decrease as the maximal block size increases, (ii) it is resilient to an untrusted auctioneer (the miner), and (iii) simplicity for transaction issuers (bidders), as the average gain from strategic bid shading (relative to bidding one's true maximal willingness to pay) diminishes as the number of bids increases.
Joint Transmission Map Estimation and Dehazing using Deep Networks

Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved visually appealing results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two-step procedure involving prior-based methods for estimating transmission map followed by calculation of dehazed image using the closed form solution. In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing. In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned jointly from the loss function. The extensive experiments evaluated on synthetic and real datasets with challenging hazy images demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.
A Platform Architecture for Multi-Tenant Blockchain-Based Systems

Blockchain has attracted a broad range of interests from start-ups, enterprises and governments to build next generation applications in a decentralized manner. Similar to cloud platforms, a single blockchain-based system may need to serve multiple tenants simultaneously. However, design of multi-tenant blockchain-based systems is challenging to architects in terms of data and performance isolation, as well as scalability. First, tenants must not be able to read other tenants' data and tenants with potentially higher workload should not affect read/write performance of other tenants. Second, multi-tenant blockchain-based systems usually require both scalability for each individual tenant and scalability with number of tenants. Therefore, in this paper, we propose a scalable platform architecture for multi-tenant blockchain-based systems to ensure data integrity while maintaining data privacy and performance isolation. In the proposed architecture, each tenant has an individual permissioned blockchain to maintain their own data and smart contracts. All tenant chains are anchored into a main chain, in a way that minimizes cost and load overheads. The proposed architecture has been implemented in a proof-of-concept prototype with our industry partner, Laava ID Pty Ltd (Laava). We evaluate our proposal in a three-fold way: fulfilment of the identified requirements, qualitative comparison with design alternatives, and quantitative analysis. The evaluation results show that the proposed architecture can achieve data integrity, performance isolation, data privacy, configuration flexibility, availability, cost efficiency and scalability.
Opinion fraud detection via neural autoencoder decision forest

Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks

Real-time semantic image segmentation on platforms subject to size, weight, and power constraints is a key area of interest for air surveillance and inspection. In this letter, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro aerial vehicles (MAVs). MAVNet, inspired by ERFNet [E. Romera, J. M. lvarez, L. M. Bergasa, and R. Arroyo, “ErfNet: Efficient residual factorized convnet for real-time semantic segmentation,” <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Trans. Intell. Transp. Syst.</i> , vol. 19, no. 1, pp. 263–272, Jan. 2018.], features 400 times fewer parameters and achieves comparable performance with some reference models in empirical experiments. Additionally, we provide two novel datasets that represent challenges in semantic segmentation for real-time MAV tracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.
Source code properties of defective infrastructure as code scripts

Context: In continuous deployment, software and services are rapidly deployed to end-users using an automated deployment pipeline. Defects in infrastructure as code (IaC) scripts can hinder the reliability of the automated deployment pipeline. We hypothesize that certain properties of IaC source code such as lines of code and hard-coded strings used as configuration values, show correlation with defective IaC scripts. Objective: The objective of this paper is to help practitioners in increasing the quality of infrastructure as code (IaC) scripts through an empirical study that identifies source code properties of defective IaC scripts. Methodology: We apply qualitative analysis on defect-related commits mined from open source software repositories to identify source code properties that correlate with defective IaC scripts. Next, we survey practitioners to assess the practitioner's agreement level with the identified properties. We also construct defect prediction models using the identified properties for 2,439 scripts collected from four datasets. Results: We identify 10 source code properties that correlate with defective IaC scripts. Of the identified 10 properties we observe lines of code and hard-coded string to show the strongest correlation with defective IaC scripts. Hard-coded string is the property of specifying configuration value as hard-coded string. According to our survey analysis, majority of the practitioners show agreement for two properties: include, the property of executing external modules or scripts, and hard-coded string. Using the identified properties, our constructed defect prediction models show a precision of 0.70~0.78, and a recall of 0.54~0.67.
Micro-expression detection in long videos using optical flow and recurrent neural networks

Facial micro-expressions are subtle and involuntary expressions that can reveal concealed emotions. Micro-expressions are an invaluable source of information in application domains such as lie detection, mental health, sentiment analysis and more. One of the biggest challenges in this field of research is the small amount of available spontaneous micro-expression data. However, spontaneous data collection is burdened by time-consuming and expensive annotation. Hence, methods are needed which can reduce the amount of data that annotators have to review. This paper presents a novel micro-expression spotting method using a recurrent neural network (RNN) on optical flow features. We extract Histogram of Oriented Optical Flow (HOOF) features to encode the temporal changes in selected face regions. Finally, the RNN spots short intervals which are likely to contain occurrences of relevant facial micro-movements. The proposed method is evaluated on the SAMM database. Any chance of subject bias is eliminated by training the RNN using Leave-One-Subject-Out cross-validation. Comparing the spotted intervals with the labeled data shows that the method produced 1569 false positives while obtaining a recall of 0.4654. The initial results show that the proposed method would reduce the video length by a factor of 3.5, while still retaining almost half of the relevant micro-movements. Lastly, as the model gets more data, it becomes better at detecting intervals, which the proposed method suitable for supporting the annotation process.
Research and Application of AdaBoost Algorithm Based on SVM

In this paper, we use AdaBoost algorithm as a framework to design a new classification algorithm. The algorithm combines the characteristics of SVM and AdaBoost algorithms. The SVM algorithm is used as a weak classifier algorithm. AdaBoost is used to cascade multiple weak classifiers to form a strong classifier, thus achieving more accurate classification. In the experiment, this paper uses SVM algorithm, AdaBoost algorithm and the algorithm of this paper to carry out several comparison experiments. The experimental results show that the algorithm can achieve higher accuracy classification in data sets of various sizes. At the same time, the algorithm is moderate in calculation speed, so the new algorithm proposed in this paper is better than the traditional SVM or AdaBoost classification algorithm.
A Comparitive Analysis on E-Voting System Using Blockchain

In a democratic country like India (which is the largest democracy in the world), Voting plays a major role in the selection of government officials as well as showing our opinion how the governing body to be formed. Time to time, researches are conducted in order to tackle the difficulties in the centralized voting system to make it more anonymous, reliable and secure while preventing any kind of frauding. Even though the use of e-voting through the electronic medium, we have to face well-known problems of maintenance and fraud. Currently, various researches are conducting in-order to make secure and reliable voting system while tackling issues of anonymity and security. Through Decentralized System, focus is drifting towards making Voting Process simple, secure and anonymity in the hand of the public. This paper presents a literature review on the papers and the techniques used to tackle voting challenges.
MVO-based path planning scheme with coordination of UAVs in 3-D environment

The path planning of UAV deals with the process of figuring the most optimal path from the source to the destination and avoiding obstacles on course to avoid collisions in a given environment. This particular problem is classified as an optimisation problem in high dimension and comes under the category of NP-Hard. Due to the usage of UAVs in many domains like the army, communications, security, disaster management, survey mapping etc., the provisioning of time efficiency as well as accuracy is a critical and challenging task in a 3-D environment. Moreover, the ability of the UAVs can be enhanced if there is coordination between them to find the best cost-effective target in a dynamic manner. However, a majority of the solutions reported theoretically are not efficient concerning time efficiency, accuracy and coordination simultaneously for many applications. Drawing motivation from this, the paper gives a novel way of solving the problem of planning of path for UAVs in 3-D while maintaining the coordination for choosing the target. A new algorithm based on Multiverse optimizer (MVO) algorithm is applied to the problem. Munkres algorithm is applied for coordination of UAVs in case of multiple targets. By carrying out the simulations, on three different maps over 50 iterations the results shows that MVO algorithm perform better in the most of the cases for finding optimized path cost with minimum average execution time when compared to meta-heuristics GSO and BBO.
On Sigma-Point Update of Cubature Kalman Filter for GNSS/INS Under GNSS-Challenged Environment

The sampling-based Kalman filters have been widely used for GNSS/INS of land vehicle navigation. Although many nonlinear filters are proposed by employing different approximate algorithms, most of the filters update sigma points utilize the moments match and Bayesian filter update framework. In this paper, cubature Kalman filters (CKFs) based on a class of novel sigma-point update framework (NSUF) are developed and compared in terms of accuracy and convergence rate. In order to enhance the reliability of GNSS/INS, an improved CKF, named as ArtCKF is proposed by merging the moments generated by different NSUFs, which not only addresses the problem of observation missing but also improves the efficiency of Bayesian filter update. Numerical simulation based on field test data is employed to verify the superiority of ArtCKF in land vehicle navigation. Our results indicate that the ArtCKF improves the heading of land vehicle by 71.3% compared with CKF, and makes a tradeoff between direct observable and non-direct observable state estimation under GNSS-challenged environment.
An encryption system for color image based on compressive sensing

This paper proposes a new color image encryption system based on compressed sensing and multi-image cross pixel scrambling approach. In the encryption process, firstly, the color image is decomposed into three sub-images according to tricolor theory; then the three decomposed images are sparsely processed by the discrete wavelet transform; finally, the different Gaussian random matrices are used to observe them. In order to enhance the security of the system, the multi-image cross pixel scrambling approach is used in the encryption process. The system can achieve accurate recovery of original information from a small amount of observations, which saves transmission bandwidth and storage space of information. During the decryption process, the images are processed by a modified regularization adaptive matching pursuit algorithm according to the corresponding Gaussian random matrix to recover the three grayscale sub-images. Theoretical analysis and simulation results prove the feasibility and safety of the encryption system.
DNA key based visual chaotic image encryption

With the exponential growth of Internet technologies, digital information exchanged over the Internet is also significantly increased. In order to ensure the security of multimedia contents over the open natured Internet, data should be encrypted. In this paper, the quantum chaotic map is utilized for random vectors generation. Initial conditions for the chaos map are computed from a DNA (Deoxyribonucleic acid) sequence along with plaintext image through Secure Hash Algorithm-512 (SHA-512). The first two random vectors break the correlation among pixels of the original plaintext image via row and column permutation, respectively. For the diffusion characteristics, the permuted image is bitwise XORed with a random matrix generated through the third random vectors. The diffused image is divided into Least Significant Bit (LSB) and Most Significant Bits (MSBs) and Discrete Wavelet Transform (DWT) is applied to the carrier image. The HL and HH blocks of the carrier image are replaced with LSBs and MSBs of the diffused image for the generation of a visually encrypted image. The detailed theoretical analysis and experimental simulation of the designed scheme show that the proposed encryption algorithm is highly secured. Efficiency and robustness of the proposed visually image encryption scheme is also verified via a number of attack analyses, i.e., sensitivity attack analysis (> 99%), differential attack analysis (NPCR > 99, UACI > 33), brute force attack (almost 7.9892), statistical attack (correlation coefficient values are almost 0 or less than zero), noise tolerance, and cropping attack. Further security analyses such as encryption quality (ID ≅ 1564, DH = 3.000), homogeneity (0.3798), contrast (10.4820) and energy (0.0144) of the scheme are also evaluated.
Limits on Correlations in Networks for Quantum and No-Signaling Resources

A quantum network consists of independent sources distributing entangled states to distant nodes which can then perform entangled measurements, thus establishing correlations across the entire network. But how strong can these correlations be? Here we address this question, by deriving bounds on possible quantum correlations in a given network. These bounds are nonlinear inequalities that depend only on the topology of the network. We discuss in detail the notably challenging case of the triangle network. Moreover, we conjecture that our bounds hold in general no-signaling theories. In particular, we prove that our inequalities for the triangle network hold when the sources are arbitrary no-signaling boxes which can be wired together. Finally, we discuss an application of our results for the device-independent characterization of the topology of a quantum network.
Sending-or-not-sending twin-field protocol for quantum key distribution with asymmetric source parameters

The sending-or-not-sending (SNS) protocol of the twin-field quantum key distribution (TFQKD) can tolerant large misalignment error and its key rate can exceed the bound of repeaterless QKD. But the original SNS protocol requires the two users to use the same source parameters. Here we propose a general protocol with asymmetric source parameters and give the security proof of this protocol. Our general protocol has a much better performance than that of the original SNS protocol when the channel of the system is asymmetric.
PyBIDS: Python tools for BIDS datasets

Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets.Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures.The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse.
Privacy-Preserving Action Recognition Using Coded Aperture Videos

The risk of unauthorized remote access of streaming video from networked cameras underlines the need for stronger privacy safeguards. We propose a lens-free coded aperture camera system for human action recognition that is privacy-preserving. While coded aperture systems exist, we believe ours is the first system designed for action recognition without the need for image restoration as an intermediate step. Action recognition is done using a deep network that takes in as input, non-invertible motion features between pairs of frames computed using phase correlation and log-polar transformation. Phase correlation encodes translation while the log polar transformation encodes in-plane rotation and scaling. We show that the translation features are independent of the coded aperture design, as long as its spectral response within the bandwidth has no zeros. Stacking motion features computed on frames at multiple different strides in the video can improve accuracy. Preliminary results on simulated data based on a subset of the UCF and NTU datasets are promising. We also describe our prototype lens-free coded aperture camera system, and results for real captured videos are mixed.