Reviewerly/exHarmony huggingface dataset:
Decryption Failure Attacks on IND-CCA Secure Lattice-Based Schemes

In this paper we investigate the impact of decryption failures on the chosen-ciphertext security of lattice-based primitives. We discuss a generic framework for secret key recovery based on decryption failures and present an attack on the NIST Post-Quantum Proposal ss-ntru-pke. Our framework is split in three parts: First, we use a technique to increase the failure rate of lattice-based schemes called failure boosting. Based on this technique we investigate the minimal effort for an adversary to obtain a failure in three cases: when he has access to a quantum computer, when he mounts a multi-target attack or when he can only perform a limited number of oracle queries. Secondly, we examine the amount of information that an adversary can derive from failing ciphertexts. Finally, these techniques are combined in an overall analysis of the security of lattice based schemes under a decryption failure attack. We show that an attacker could significantly reduce the security of lattice based schemes that have a relatively high failure rate. However, for most of the NIST Post-Quantum Proposals, the number of required oracle queries is above practical limits. Furthermore, a new generic weak-key (multi-target) model on lattice-based schemes, which can be viewed as a variant of the previous framework, is proposed. This model further takes into consideration the weak-key phenomenon that a small fraction of keys can have much larger decoding error probability for ciphertexts with certain key-related properties. We apply this model and present an attack in detail on the NIST Post-Quantum Proposal – ss-ntru-pke – with complexity below the claimed security level.
Culture-based Artificial Bee Colony with heritage mechanism for optimization of Wireless Sensors Network

In this paper, a hybridization model based on culture algorithm and Artificial Bee Colony is proposed. The objective of the hybrid model is mainly to get benefit of the previous knowledge gained by predecessor foragers which help bees searching for food sources in potential positions. The proposed CB-ABC focuses on the kind of information in the belief space that should be stored to reduce promising solutions' area. Moreover, CB-ABC divides the population into two groups of individuals, one group updates by the heritage of best previous solutions and the other group updates by self-adaptive information. The performance of the new algorithm has been validated on a variety of numerical testbench functions (ranging from CEC 2005 and CEC 2017) and compared to standard ABC and other variants named ABCM, HPA and CABCA as well. The proposed algorithm proves its success as well when applied on Wireless Sensor Network (WSN) to locate them accurately. To validate the performance of CB-ABC it is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search (CS), Firefly Algorithm (FA) and Parallel Firefly Algorithm (PFA). CB-ABC shows the least average error value compared to the standard ABC as well as the other algorithms previously mentioned in the comparison. Moreover, CB-ABC succeeds in reducing the number of iterations as well as function evaluations to 17% of those of the standard ABC to 20% of those obtained by ABCM during solving WSN localization problem. CB-ABC's parametric study (heritage size and offspring ratio) has been carried out as well.
Asymmetric multi-user optical cryptosystem based on polar decomposition and Shearlet transform

In this paper, we have proposed an asymmetric optical cryptosystem using polar decomposition and Shearlet transform (ST). Polar decomposition is employed to generate multiple private keys for decryption. Gyrator transform (GT) with angle α, is used for optical propagation. Multi-scale directional property of ST is utilized to get Shearlet coefficients from the complex output after GT. The first coefficient is then replaced by a random phase mask before performing inverse ST. The detailed robustness analysis of the proposed method in terms of various potential attacks, such as, noise, occlusion, brute force, special attack, and plaintext attacks is performed and compared with the traditional asymmetric method. Results are presented to demonstrate the validity and effectiveness of the proposed technique.
An Efficient Algorithm for Confidentiality, Integrity and Authentication Using Hybrid Cryptography and Steganography

Data/information is the most valuable asset for the modern electronic communication system. To secure data or information has become a challenge in this competitive world. There are many techniques for securing data/information such as cryptography, steganography etc. In this paper, hybrid cryptography has been applied using AES and RSA. In this hybrid cryptography, the symmetric key used for message encryption is also encrypted, which ensures a better security. An additional feature of this paper is to create a digital signature by encrypting the hash value of message. At the receiving side this digital signature is used for integrity checking. Then the encrypted message, encrypted symmetric key and encrypted digest are combined together to form a complete message. This complete message again has been secured using the steganography method, LSB. Here hybrid cryptography provides a better security, steganography strengthens the security. Message integrity checking is a special feature of this algorithm. Successful simulations have been shown to support the feasibility of this algorithm.
Implementing Prosodic Phrasing in Chinese End-to-end Speech Synthesis

Text-to-Speech (TTS) systems have been evolving rapidly in recent years. With the great modelling power of deep neural networks, researchers have achieved end-to-end conversion from raw text to speech. It has been shown by various research projects that end-to-end TTS systems are able to generate speech that sounds akin to human voice for English and other languages. However, for languages like Chinese, there are two problems to deal with. Firstly, due to the large character set, a small input set comparable to the English character set is needed for the end-to-end solution. Secondly, there are serious prosodic phrasing mistakes when the end-to-end method is applied to Chinese. In this paper, we will propose a solution for an end-to-end Chinese TTS system on the basis of Tacotron 2 and Wavenet vocoder. We will then add extra contextual information to improve the performance of prosodic phrasing. Our experiments have demonstrated the effectiveness of this proposal.
Echo state networks regulated by local intrinsic plasticity rules for regression

Intrinsic plasticity, as a biologically inspired unsupervised learning rule, is used for adapting the intrinsic excitability of the reservoir neurons. Existing intrinsic plasticity rules can only select a set of fixed rule parameters for the whole reservoir neurons, which affects the learning performance due to the lack of flexibility in providing intrinsic plasticity. In this paper, we present an echo state network (ESN) with local intrinsic plasticity rule built by different reservoir neurons which can adopt the intrinsic plasticity rule with different rule parameters to adjust its intrinsic excitability. And the covariance matrix adaptation evolution strategy is used to search and select the rule parameters corresponding to different reservoir neurons. Compared with several state-of-the-art ESN models and an ESN with the global plasticity rule, the proposed local intrinsic plasticity rule is able to achieve much better performance in some benchmark prediction tasks.
Hybrid SAR Speckle Reduction Using Complex Wavelet Shrinkage and Non-Local PCA-Based Filtering

In this paper, a new hybrid despeckling method, based on Undecimated Dual-Tree Complex Wavelet Transform (UDT-CWT) using maximum a posteriori (MAP) estimator and non-local Principal Component Analysis (PCA)-based filtering with local pixel grouping (LPG-PCA), was proposed. To achieve a heterogeneous-adaptive speckle reduction, SAR image is classified into three classes of point targets, details, or homogeneous areas. The despeckling is done for each pixel based on its class of information. Logarithm transform was applied to the SAR image to convert the multiplicative speckle into additive noise. Our proposed method contains two principal steps. In the first step, denoising was done in the complex wavelet domain via MAP estimator. After performing UDT-CWT, the noise-free complex wavelet coefficients of the log-transformed SAR image were modeled as a two-state Gaussian mixture model. Furthermore, the additive noise in the complex wavelet domain was considered as a zero-mean Gaussian distribution. In the second step, after applying inverse UDT-CWT, an iterative LPG-PCA method was used to smooth the homogeneous areas and enhance the details. The proposed method was compared with some state-of-the-art despeckling methods. The experimental results showed that the proposed method leads to a better speckle reduction in homogeneous areas while preserving details.
Is Your Software Valueless?

Software development ignores human values. As a society, we rely on software systems that neither align with nor respect our core values, such as transparency, gender diversity, social justice, and personal integrity. The past 50 years of software engineering have focused on functionality, cost, safety, availability, and security. But what about broader human values (Figure 1) such as compassion, social responsibility, and justice? The way we design software fundamentally influences society, yet human values-which we would all claim to care about-have been a side concern in software engineering. (See "Where Are the Values in Software?")
Noise correction to improve data and model quality for crowdsourcing

In supervised learning, obtaining expert labeling of data is expensive and time-consuming in many cases. Crowdsourcing services provide a cheap and efficient way to acquire the labels of data. In crowdsourcing scenario, each instance obtains a multiple noisy label set from multiple different labelers, then ground truth inference algorithms are employed to obtain integrated labels of instances. In spite of the effectiveness of ground truth inference methods, a certain level of class (label) noise still exists in integrated labels, and thus class noise correction methods are dedicated to mitigate the effects of the class noise. However, to our best knowledge, there is little work, up to the present, on exploiting the information in the crowdsourcing scenario. This paper proposes a novel class noise correction method which takes advantage of the information of the multiple noisy label sets. We call our method between-class margin-based noise correction (BMNC). In BMNC, a preliminary filtering is performed before building filters to filter class noise. By the preliminary filtering, some potentially noise instances are first removed. Afterwards, a filter is built to further filter out noise instances. After these two filtering steps, a clean set and a noise set are obtained, and then a classifier is built on the clean set to relabel the instances in noise set. Experimental results on 22 simulated benchmark data sets and eight real-world crowdsourced data sets show that BMNC can significantly reduce the class noise level in integrated labels and thereby enhance the performance of target classifiers.
Classification of sports videos with combination of deep learning models and transfer learning

Sports classification has considerable importance for digital content archiving in broadcasting companies. It is also a subdivision of human action recognition, which further contributes to understand the context of video scenes. In this work, deep neural networks are used, combining convolutional and recurrent networks to classify 15 individual sports classes. The sports dataset is hand-crafted to focus on sports action-based classification. CNN extracted features are combined with temporal information from RNN to formulate the general model to solve the problem. Later, transfer learning is applied with the VGG-16 model which was able to achieve 94% and 92 % test accuracy for 10 and 15 sports classes respectively.
Biomedical image classification based on a feature concatenation and ensemble of deep CNNs

Deep learning and more specifically Convolutional Neural Network (CNN) is a cutting edge technique which has been applied to many fields including biomedical image classification. To further improve the classification performance for biomedical images, in this paper, a feature concatenation method and a feature concatenation and ensemble method are proposed to combine several CNNs with different depths and structures. Three datasets, namely 2D Hela dataset, PAP smear dataset, and Hep-2 cell image dataset, are used as benchmarks for testing the proposed methods. It is shown from experiments that the feature concatenation and ensemble method outperforms each individual CNN, and the feature concatenation method, as well as several state-of-the-art methods in terms of classification accuracy.
End-to-end Dysarthric Speech Recognition Using Multiple Databases

We present in this paper an end-to-end automatic speech recognition (ASR) system for a person with an articulation disorder resulting from athetoid cerebral palsy. In the case of a person with this type of articulation disorder, the speech style is quite different from that of a physically unimpaired person, and the amount of their speech data available to train the model is limited because their burden is large due to strain on the speech muscles. Therefore, the performance of ASR systems for people with an articulation disorder degrades significantly. In this paper, we propose an end-to-end ASR framework trained by not only the speech data of a Japanese person with an articulation disorder but also the speech data of a physically unimpaired Japanese person and a non-Japanese person with an articulation disorder to relieve the lack of training data of a target speaker. An end-to-end ASR model encapsulates an acoustic and language model jointly. In our proposed model, an acoustic model portion is shared between persons with dysarthria, and a language model portion is assigned to each language regardless of dysarthria. Experimental results show the merit of our proposed approach of using multiple databases for speech recognition.
Controlled secure social cloud data sharing based on a novel identity based proxy re-encryption plus scheme

Currently we are witnessing a rapid integration of social networks and cloud computing, especially on storing social media contents on cloud storage due to its cheap management and easy accessing at any time and from any place. However, how to securely store and share social media contents such as pictures/videos among social groups is still a very challenging problem. In this paper, we try to tackle this problem by using a new cryptographic primitive: identity based proxy re-encryption plus (IBPRE +), which is a variant of proxy re-encryption (PRE). In PRE, by using re-encryption keys, a ciphertext computed for Alice can be transferred to a new one for Bob. Recently, the concept of PRE plus (PRE+) was introduced by Wang et al. In PRE+, all the algorithms are almost the same as traditional PRE, except the re-encryption keys are generated by the encrypter instead of the delegator. The message-level based fine-grained delegation property and the weak non-transferable property can be easily achieved by PRE +, while traditional PRE cannot achieve them. Based on the 3-linear map, we first propose a new IBE scheme and a new IBPRE+ scheme, we prove the security of these schemes and give the properties and performance analysis of the new IBPRE+ scheme. Finally, we propose a new framework based on this new primitive for secure cloud social data sharing.
A NSGA-II-Based Calibration Algorithm for Underwater Binocular Vision Measurement System

In this paper, a nondominated sorting genetic algorithm (NSGA)-II-based calibration algorithm for the underwater binocular vision measurement system is proposed. The underwater measurement results will be incorrect with the binocular camera model in air. To deal with this problem, a refractive camera model and an akin triangulation are proposed to establish the nonlinear relationship with housing parameters between the object and its corresponding image plain points. A novel usage of checkerboard based on the relative position relationship of corners is employed to set three optimal goals, i.e., the distance difference, the vertical direction difference, and the parallel direction difference. The process of calibration is regarded as a multiobjective optimization and solved by NSGA-II. Finally, experimental results demonstrate the validity and effectiveness of the proposed calibration algorithm.
The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review

The popularity of deep learning (DL) in the machine learning community has been dramatically increasing since 2012. The theoretical foundations of DL are well rooted in the classical neural network (NN). Rule extraction is not a new concept, but was originally devised for a shallow NN. For about the past 30 years, extensive efforts have been made by many researchers to resolve the “black box” problem of trained shallow NNs using rule extraction technology. A rule extraction technology that is well balanced between accuracy and interpretability has recently been proposed for shallow NNs as a promising means to address this black box problem. Recently, we have been confronting a “new black box” problem caused by highly complex deep neural networks (DNNs) generated by DL. In this paper, we first review four rule extraction approaches to resolve the black box problem of DNNs trained by DL in computer vision. Next, we discuss the fundamental limitations and criticisms of current deep learning approaches in radiology, pathology, and ophthalmology from the black box point of view. We also review the conversion methods from DNNs to decision trees and point out their limitations. Furthermore, we describe a transparent approach for resolving the black box problem of DNNs trained by a deep belief network. Finally, we provide a brief description to realize the transparency of DNNs generated by a convolutional NN and discuss a practical way to realize the transparency of DL in radiology, pathology, and ophthalmology.
QUICker or not? -an Empirical Analysis of QUIC vs TCP for Video Streaming QoE Provisioning

The introduction of the QUIC (Quick UDP Internet Connections) transport protocol by Google aimed to improve the Quality of Experience (QoE) with web services compared to the prevailing Transport Control Protocol (TCP). Nowadays, QUIC has become the default protocol to communicate between the Google Chrome browser and Google servers and accounts for an increasing share of the Internet traffic. This work investigates whether the promised QoE benefits of QUIC are indeed noticeable for end users or not. A measurement study was conducted for YouTube video streaming in two mobile and two fixed access networks, in which a defined set of videos was streamed back-to-back with QUIC and TCP in randomized order. QoE factors of video streaming (such as initial delay, the visual quality of the video, and stalling) were compared statistically to find significant differences between the streaming over QUIC and the streaming over TCP. Surprisingly, no evidence for any QoE improvement of QUIC over TCP in the context of YouTube streaming could be found.
A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

In this paper, we propose a hybrid approach for sentence paraphrase identification. The proposal addresses the problem of evaluating sentence-to-sentence semantic similarity when the sentences contain a set of named-entities. The essence of the proposal is to distinguish the computation of the semantic similarity of named-entity tokens from the rest of the sentence text. More specifically, this is based on the integration of word semantic similarity derived from WordNet taxonomic relations, and named-entity semantic relatedness inferred from Wikipedia entity co-occurrences and underpinned by Normalized Google Distance. In addition, the WordNet similarity measure is enriched with word part-of-speech (PoS) conversion aided with a Categorial Variation database (CatVar), which enhances the lexico-semantics of words. We validated our hybrid approach using two different datasets; Microsoft Research Paraphrase Corpus (MSRPC) and TREC-9 Question Variants. In our empirical evaluation, we showed that our system outperforms baselines and most of the related state-of-the-art systems for paraphrase detection. We also conducted a misidentification analysis to disclose the primary sources of our system errors.
The bifurcation of the Nigerian cybercriminals: Narratives of the Economic and Financial Crimes Commission (EFCC) agents

While this article sets out to advance our knowledge about the characteristics of Nigerian cybercriminals (Yahoo-Boys), it is also the first study to explore the narratives of the Economic and Financial Crimes Commission (EFCC) officers concerning them. It appraises symbolic interactionist insights to consider the ways in which contextual factors and worldview may help to illuminate officers’ narratives of cybercriminals and the interpretations and implications of such accounts. Semi-structured interviews of forty frontline EFCC officers formed the empirical basis of this study and were subjected to a directed approach of qualitative content analysis. While prior studies, for example, indicated that only a group of cybercriminals deploy spiritual and magical powers to defraud victims (i.e. modus operandi), our data analysis extended this classification into more refined levels involving multiple features. In particular, analysis bifurcates cybercriminals and their operations based on three factors: educational-attainment, modus-operandi, and networks-collaborators. Results also suggest that these cybercriminals and their operations are embedded in “masculinity-and-material-wealth”. These contributions thus have implications for a range of generally accepted viewpoints about these cybercriminals previously taken-for-granted. Since these criminals have victims all over the world, insights from our study may help various local and international agencies [a] to understand the actions/features of these two groups of cybercriminals better and develop more effective response strategies. [b] to appreciate the vulnerabilities of their victims better and develop more adequate support schemes. We also consider the limitations of social control agents’ narratives on criminals.
Vistribute

We present Vistribute, a framework for the automatic distribution of visualizations and UI components across multiple heterogeneous devices. Our framework consists of three parts: (i) a design space considering properties and relationships of interactive visualizations, devices, and user preferences in multi-display environments; (ii) specific heuristics incorporating these dimensions for guiding the distribution for a given interface and device ensemble; and (iii) a web-based implementation instantiating these heuristics to automatically generate a distribution as well as providing interaction mechanisms for user-defined adaptations. In contrast to existing UI distribution systems, we are able to infer all required information by analyzing the visualizations and devices without relying on additional input provided by users or programmers. In a qualitative study, we let experts create their own distributions and rate both other manual distributions and our automatic ones. We found that all distributions provided comparable quality, hence validating our framework.