Reviewerly/exHarmony huggingface dataset:
LEVER: Secure Deduplicated Cloud Storage With Encrypted Two-Party Interactions in Cyber--Physical Systems

Cloud envisioned cyber--physical systems (CCPS) is a practical technology that relies on the interaction among cyber elements like mobile users to transfer data in cloud computing. In CCPS, cloud storage applies data deduplication techniques aiming to save data storage and bandwidth for real-time services. In this infrastructure, data deduplication eliminates duplicate data to increase the performance of the CCPS application. However, it incurs security threats and privacy risks. For example, the encryption from independent users with different keys is not compatible with data deduplication. In this area, several types of research have been done. Nevertheless, they are suffering from a lack of security, high performance, and applicability. Motivated by this, in this article, we propose a message lock encryption with neVer-decrypt homomorphic encRyption (LEVER) protocol between the uploading CCPS user and cloud storage to reconcile the encryption and data deduplication. Interestingly, LEVER is the first brute-force resilient encrypted deduplication with only cryptographic two-party interactions. We perform several numerical analysis of LEVER and confirm that it provides high performance and practicality compared to the literature.
Decentralized Public Key Infrastructures atop Blockchain

The public key infrastructure (PKi) has been widely adopted to create, manage, distribute, store and revoke digital certificates, which plays an important role in bootstrapping secure communications. A PKi system authenticates entities with the corresponding public keys and it lays the security foundation for public-key cryptosystems in public-key encryption and digital signatures. However, traditional PKi systems suffer from security breaches, such as single-point-of-failure and man-in-the-middle attacks due to the existence of a centralized certificate authority. in this article, we review the traditional centralized PKi system as well as the subjected security concerns, and then we propose possible solutions to address these issues with the emerging blockchain technology. Two frameworks are presented where blockchain is utilized as a public bulletin board or trusted majority. We implement the functions to evaluate the off-chain time costs and on-chain gas costs of the proposal, which demonstrate the feasibility and practicality of the proposal.
Cyber-attacks and threats for healthcare – a multi-layer thread analysis

Due to the advent of novel technologies and digital opportunities allowing to simplify user lives, healthcare is increasingly evolving towards digitalization. This represent a great opportunity on one side but it also exposes healthcare organizations to multiple threats (both digital and not) that may lead an attacker to compromise the security of medial processes and potentially patients' safety. Today technical cybersecurity countermeasures are used to protect the confidentiality, integrity and availability of data and information systems - especially in the healthcare domain. This paper will report on the current state of the art about cyber security in the Healthcare domain with particular emphasis on current threats and methodologies to analyze and manage them. In addition, it will introduce a multi-layer attack model providing a new perspective for attack and threat identification and analysis.
English-Hindi Neural Machine Translation-LSTM Seq2Seq and ConvS2S

Language Translation connects people from all around the world, to collaborate, share information and build relationships. Neural Machine Translation has been able to achieve significant improvement over primitive techniques: Rule-based and Statistical Machine Translation. This paper analyzes and compares two Neural Machine Translation models on the basis of different parameters for English-Hindi language pair: Sequence to Sequence Learning architecture with both encoder and decoder implemented using (1) Long Short Term Memory (LSTM) and (2) Convolutional Neural Network (CNN) with attention mechanism applied in both models. The analysis presents insights that reveal the models and parameters best for this task.
Comparison of BPN, RBFN and wavelet neural network in induction motor modelling for speed estimation

This paper discusses the question of estimating a neural network induction motor speed. The neural network is trained in the relationship between stator currents and rotor speed. Rotor currents and speed are created using MATLAB from simulated model induction motor and from experimental real-time set-up using LABVIEW. With these two data sets, Induction motor is modelled using three neural network architectures, namely BPN, RBFN and Wavelet Neural Network, comparing the performance of each neural model in estimating speed. Results show the neural approach's ability to replace the speed sensor used in the closed loop speed control system to accurately estimate speed.
Breast Cancer Malignancy Prediction Using Deep Learning Neural Networks

Cancer is a disease that instils fear in many people across the globe because of its fatal nature. But in most cases, cancer can be cured if diagnosed in an early stage followed up with proper medical attention. Computer aided diagnosis is gaining popularity nowadays as it can be effectively used as a primary screening test for many diseases and especially in case of cancer. Deep learning is an artificial intelligence technique where the machine is equipped with intelligence by trying to mimic the thought process of a human brain. This paper focuses on building a deep neural network that can predict the malignancy of the breast cancer. The data is obtained from the Wisconsin breast cancer dataset provided by UCI. The neural network model is optimized against over fitting using the early stopping mechanism and dropout layers and attains a F1 score of over 98.
A Brief Review of the Recent Trends in Sign Language Recognition

Sign language (SL) is an art of conveying our ideas and emotions non-verbally. SL is the main communication means for the deaf / dumb community. They usually communicate through a multitude of hand gestures. The correct interpretation of SL is all the more important, since the deaf and dumb constitute nearly 90 million of the world population. Therefore sign language recognition (SLR) and translation is a fertile area of research. The two main approaches for SLR are (i) image based and (ii) sensor based. Image based approach involves one or more cameras to capture an image sequence of the signer performing the sign, and then uses image processing to recognize the sign. The sensor based method uses instrumental gloves assembled with sensors to track the hand articulates. This paper mainly describes various image or vision based SLR systems comprising feature extraction and classification. Translation of SL to speech is also described briefly. Overall, this paper is expected to be a complete introduction to automatic hand gesture recognition and sign language interpretation (SLI).
Hate Speech and Election Violence in Nigeria

Although research has considered how poor governance, exclusionary politics and electoral malpractice affect election violence, the effect of hate speech on election violence has not received adequate academic attention. Using a mixed methods approach with qualitative dominance, this study examines the effect of hate speech on election violence in Nigeria during the 2011, 2015 and 2019 presidential elections. The article demonstrates that an entrenched culture of hate speech is an oft-neglected major driver of election violence in Nigeria. The study concludes that the implementation of existing anti-hate speech laws presents an opportunity for protecting the rights of minority groups, promoting political inclusion and preventing election violence in Nigeria and beyond.
Intelligent medical heterogeneous big data set balanced clustering using deep learning

In order to address the clustering problem of intelligent medical data, the data sets were not preprocessed using the traditional method, leading to a large amount of calculation, low efficiency, and large data cluster center offset distance. We proposed a balanced clustering algorithm for intelligent medical heterogeneous big data set using deep learning. Firstly, a deep neural network model based on incremental updating was constructed, and adaptive training and adjustment were made according to data scale, and the multi-layer feature learning of heterogeneous big data sets of intelligent medical care. Secondly, under-sampling preprocessing was carried out on the data set so that the data of the heterogeneous big data set was in a balanced state, and on this basis, clustering calculation of the heterogeneous big data was conducted. Then, the clustering center was set according to the kernel density estimation results, and the data cluster center was updated iteratively until convergence by combining the data features obtained from deep learning and euclidean distance calculation, so as to complete the balanced clustering of the heterogeneous big data set of intelligent medical treatment. The results show that the proposed algorithm has the advantages of small data cluster center offset distance, short clustering time, low energy consumption, high Macro-F1 value and NMI value, and the accuracy of clustering can be as high as 95%, the calculational cost is low, which has certain advantages. 2020 Elsevier Ltd. All rights reserved.
Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda

Generative Adversarial Networks (GANs) have demonstrated their ability to learn patterns in data and produce new exemplars similar to, but different from, their training set in several domains, including video games. However, GANs have a fixed output size, so creating levels of arbitrary size for a dungeon crawling game is difficult. GANs also have trouble encoding semantic requirements that make levels interesting and playable. This paper combines a GAN approach to generating individual rooms with a graph grammar approach to combining rooms into a dungeon. The GAN captures design principles of individual rooms, but the graph grammar organizes rooms into a global layout with a sequence of obstacles determined by a designer. Room data from The Legend of Zelda is used to train the GAN. This approach is validated by a user study, showing that GAN dungeons are as enjoyable to play as a level from the original game, and levels generated with a graph grammar alone. However, GAN dungeons have rooms considered more complex, and plain graph grammar's dungeons are considered least complex and challenging. Only the GAN approach creates an extensive supply of both layouts and rooms, where rooms span across the spectrum of those seen in the training set to new creations merging design principles from multiple rooms.
Detection of heterogeneous parallel steganography for low bit-rate VoIP speech streams

This paper considers a new task of detecting heterogeneous parallel steganography (HPS) on streaming media. This task is to detect the existence of the confidential messages hidden in the frames of streaming media with multiple kinds of orthogonal steganographic methods. We target on detecting HPS in this work for low bit-rate Voice over Internet Protocol (VoIP) speech streams, which is a widely-used streaming medium. Specifically, two steganographic methods, i.e., Quantization Index Modulation and Pitch Modulation Steganography, are utilized to form the HPS. Detecting HPS on low bit-rate VoIP speech streams is challenging for existing steganalysis methods. To accomplish the target, we propose a novel deep model named as Steganalysis Feature Fusion Network (SFFN). SFFN consists of three sub-networks, i.e., a feature learning network, a feature fusion network and a classification network. With the three sub-networks, SFFN can effectively extract steganalysis features for the steganographic methods used in HPS and can fuse the features to make credible prediction. The experimental results demonstrate that our method is superior to the state-of-the-art steganalysis methods when detecting HPS. Besides, our method meets the requirement of real-time detection.
Scaling blockchains without giving up decentralization and security

Public blockchains should be able to scale with respect to the number of nodes and to the transactions workload. The blockchain scalability trilemma has been informally conjectured. This is related to scalability, security and decentralization, stating that any improvement in one of these aspects should negatively impact on at least one of the other two. In fact, despite the large research and experimental effort, all known approaches turn out to be tradeoffs.
Realistic Dreams: Cascaded Enhancement of GAN-generated Images with an Example in Face Morphing Attacks

The quality of images produced by generative adversarial networks (GAN) is commonly a trade-off between the model size, its training data needs, and the generation resolution. This trad-off is clear when applying GANs to issues like generating face morphing attacks, where the latent vector used by the generator is manipulated. In this paper, we propose an image enhancement solution designed to increase the quality and resolution of GAN-generated images. The solution is designed to require limited training data and be extendable to higher resolutions. We successfully apply our solution on GAN-based face morphing attacks. Beside the face recognition vulnerability and attack detectability analysis, we prove that the images enhanced by our solution are of higher visual and quantitative quality in comparison to unprocessed attacks and attack images enhanced by state-of-the-art super-resolution approaches.
On the Scalable Multi-Objective Multi-Agent Pathfinding Problem

The Multi-Agent Pathfinding problem (MAPF) has several applications in industry and robotics. The aim of a MAPF-solver is to find a set of optimal and non-overlapping paths for a number of agents in a navigation scenario. Existing approaches are shown to successfully deal with MAPF, where either the makespan or flow-time is used as a single objective. In this article, we treat the MAPF as a multi-objective optimisation problem (MOMAPF). In this paper, we consider three different objective functions, called makespan, flow-time and path-overlaps which are to be optimised at the same time. The MOMAPF problem in this paper is designed to be a scalable test problem for multi-objective optimisation algorithms, where we can scale up the variable space to reflect different real-world scenarios. We propose a new problem formulation for MOMAPF optimisation algorithms and implement it into the NSGA-II and NSGA-III and provide an experimental evaluation of the optimisation results.
DeepFake Face Image Detection based on Improved VGG Convolutional Neural Network

DeepFake can forge high-quality tampered images and videos that are consistent with the distribution of real data. Its rapid development causes people's panic and reflection. In this paper we presents an improved VGG network named NA-VGG to detect DeepFake face image, which was based on image noise and image augmentation. Firstly, In order to learn the tampering artifacts that may not be seen in RGB channels, SRM filter layer is used to highlight the image noise features; Secondly, the image noise map is augmented to weaken the face features. Finally, the augmented noise images are input into the network to train and judge whether the image is forged. The experimental results using the Celeb-DF dataset have shown that NA-VGG made great improvements than other state-of-the-art fake image detectors.
A Fast and efficient stochastic opposition-based learning for differential evolution in numerical optimization

A fast and efficient stochastic opposition-based learning (OBL) variant is proposed in this paper. OBL is a machine learning concept to accelerate the convergence of soft computing algorithms, which consists of simultaneously calculating an original solution and its opposite. Recently, a stochastic OBL variant called BetaCOBL was proposed, which is capable of controlling the degree of opposite solutions, preserving useful information held by original solutions, and preventing the waste of fitness evaluations. While it has shown outstanding performance compared to several state-of-the-art OBL variants, the high computational cost of BetaCOBL may hinder it from cost-sensitive optimization problems. Also, as it assumes that the decision variables of a given problem are independent, BetaCOBL may be ineffective for optimizing inseparable problems. In this paper, we propose an improved BetaCOBL that mitigates all the limitations. The proposed algorithm called iBetaCOBL reduces the computational cost from O(NP2 · D) to O(NP · D) (NP and D stand for population size and a dimension, respectively) using a linear time diversity measure. Also, the proposed algorithm preserves strongly dependent variables that are adjacent to each other using multiple exponential crossover. We used differential evolution (DE) variants to evaluate the performance of the proposed algorithm. The results of the performance evaluations on a set of 58 test functions show the excellent performance of iBetaCOBL compared to ten state-of-the-art OBL variants, including BetaCOBL.
FPGA-Based Hardware Accelerator for Leveled Ring-LWE Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) allows arbitrary computation on encrypted data and has great potential in privacy-preserving cloud computing and securely outsource computational tasks. However, the excessive computation complexity is the key limitation that restricting the practical application of FHE. In this paper we proposed a FPGA-based high parallelism architecture to accelerate the FHE schemes based on the ring learning with errors (RLWE) problem, specifically, we presented a fast implementation of leveled fully homomorphic encryption scheme BGV. In order to reduce the computation latency and improve the performance, we applied both circuit-level and block-level pipeline strategies to improve clock frequency, and as a result, enhance the processing speed of polynomial multipliers and homomorphic evaluation functions. At the same time, multiple polynomial multipliers and modular reduction units were deployed in parallel to further improve the hardware performance. Finally, we implemented and tested our architecture on a Virtex UltraScale FPGA platform. Runing at 150MHz, our implementation achieved $4.60\times \sim 9.49\times $ speedup with respect to the optimized software implementation on Intel i7 processor running at 3.1GHz for homomorphic encryption and decryption, and the throughput was increased by $1.03\times \sim 4.64\times $ compared to the hardware implementation of BGV. While compared to the hardware implementation of FV, the throughput of our accelerator also achieved $5.05\times $ and $167.3\times $ speedup for homomorphic addition and homomorphic multiplication operation respectively.
Smart healthcare solutions using the internet of medical things for hand gesture recognition system

Abstract Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication technologies are assisting for remote monitoring and recommendations in smart healthcare. In this paper, the dependable gesture recognition (DGR) using a series learning method for identifying the action of patient monitoring through remote access is presented. The gesture recognition systems connect to the end-user (remote) and the patient for instantaneous gesture identification. The gesture is recognized by the analysis of the intermediate and structuring features using series learning. The proposed gesture recognition system is capable of monitoring patient activities and differentiating the gestures from the regular actions to improve the convergence. Gesture recognition through remote monitoring is indistinguishable due to the preliminary errors. Further, it is convertible using series learning. Therefore, the misdetections and classifications are promptly identified using the DGR and verified by comparative analysis and experimental study. From the analysis, the proposed DGR approach attains 94.92% high precision for the varying gestures and 89.85% high accuracy for varying mess factor. The proposed DGR reduces recognition time to 4.97 s and 4.93 s for the varying gestures and mess factor, respectively.
Convergence rates of Gaussian ODE filters

A recently introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solution x and its first q derivatives a priori as a Gauss-Markov process X , which is then iteratively conditioned on information about x˙ . This article establishes worst-case local convergence rates of order q+1 for a wide range of versions of this Gaussian ODE filter, as well as global convergence rates of order q in the case of q=1 and an integrated Brownian motion prior, and analyses how inaccurate information on x˙ coming from approximate evaluations of f affects these rates. Moreover, we show that, in the globally convergent case, the posterior credible intervals are well calibrated in the sense that they globally contract at the same rate as the truncation error. We illustrate these theoretical results by numerical experiments which might indicate their generalizability to q∈{2,3,…} .
Cauchy kernel-based maximum correntropy Kalman filter

Non-Gaussian noise processing is a difficult and hot spot in the study of filters. A currently effective method to deal with non-Gaussian noise is replacing the minimum mean square error criterion with the maximum correntropy criterion. Based on the maximum correntropy criterion, maximum correntropy Kalman filter, which usually uses the Gaussian kernel function to define the distance between vectors, is developed. However, when the non-Gaussian noise is multi-dimensional, maximum correntropy Kalman filter tends to break down due to the appearance of singular matrices. To overcome the drawback, a novel filter named Cauchy kernel-based maximum correntropy Kalman filter is proposed, which utilises the Cauchy kernel function to define the distance between vectors. Due to the insensitive feature to the kernel bandwidth and thick-tailed characteristic of the Cauchy kernel function, Cauchy kernel-based maximum correntropy Kalman filter can effectively avoid filter faults and has a better stability. Simulation results demonstrate the excellent performance of the proposed algorithm by comparing it with other conventional methods, such as Kalman filter, ideal Kalman filter, Huber-based filter, Gaussian sum filter and maximum correntropy Kalman filter.