Reviewerly/exHarmony huggingface dataset:
Validation of a socio-technical management process for optimising cybersecurity practices

This study developed a socio-technical management process to optimise both technical and non-technical security measures to provide optimal, rather than adequate, enterprise security safeguards. The rationale was that over the last decade, studies have consistently shown that the human being remains the weakest link in the entire enterprise security chain. As a result, the majority of cyberattacks have resulted from human behaviour or error. Despite this, evidence suggests that many enterprises are still taking overly technocentric approaches to cybersecurity risk and this has increased the chances of missing the bigger picture. Thus, a mechanism to optimise both technical and non-technical security measures by identifying and closing socio-technical security gaps in existing enterprise security frameworks was required. The mechanism was derived from the literature and validated by industry practitioners where it was found that practitioners could categorise security controls into social (human included), technical and environmental dimensions. Through this, it was found that there were mainly non-technical (social and environmental dimensions) security gaps at practitioners' organisations. To further demonstrate how this security challenge can be identified and addressed, a desktop application of the management process was carried out on the COBIT 5 for Information Security framework. The results reveal the non-technical security gaps on COBIT 5 and the management process demonstrates how these could be closed and optimised. The importance of this study is to highlight that taking overly technocentric approaches to enterprise security risk does not yield significantly positive results in protecting assets. A new approach is required and the socio-technical management process is this paper's contribution to address that security challenge.
Hybrid meta-heuristic VM load balancing optimization approach

Abstract Cloud computing brings the business computing to a novel pattern from manufacturing to services. Clouds enhance the next generation data centers as a network of virtual computing services. Resource allocation and distribute should be as per Quality of Service (QoS) with Service Level Agreement (SLA) to decrease energy intake and carbon release. Cloud environment resources requests are concurrent and competitive. Efficient resources handling is elementary need in data center and should be performed through the implementation of efficient and dynamic load balancing algorithm. Optimization is a scientific discipline to discover optimal solution from numerous solutions for a problem. In NP-hard problem, ﬁnding the optimal solutions for algorithms is expensive. Hence, many of the proposed algorithms focus on searching approximate solutions for VM load balancing. Heuristic, meta-heuristic and hybrid optimization strategy gaining popularity as find optimized solution in reasonable time for complex problems. A hybrid meta-heuristic is proposed using nature inspired Genetic Algorithm and Particle Swarm Optimization approaches. Optimized results achieved in terms of performance when proposed approach evaluated on Cloud Analyst Simulation tool.
ForeSee: A Cross-Layer Vulnerability Detection Framework for the Internet of Things

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.
Structures and Methods for Fully-Integrated Quantum Random Number Generators

Different electronic devices nowadays exploit random number generators either to create the seed for simulations, for gaming or to encrypt the communications. Particularly for cryptography applications, there is an increasing effort to realize a truly random number generator, based not on mathematical algorithms but exploiting randomness of physical processes. Among them quantum processes, provide intrinsic unpredictability, for example exploiting the arrival time, the number or the properties of photons. In this work we focus on photonic quantum random number generator (QRNG), and we analyze different approaches and entropy extraction methods to realize a fully integrated, CMOS compatible, QRNG with photon-emitter realized on the same chip and very close to the detectors of single photons. Non-idealities of the emitter and the detector have to be studied and depends on the structure. They can be mitigated with robust extraction methods which however should not introduce complex algorithms or secondary entropy sources that can make the device no longer quantum based. The ultimate goal is to realize a CMOS building block that can be implemented as building block, with low power consumption and robust to environmental conditions.
Image Reconstruction Using Pre-Trained Autoencoder on Multimode Fiber Imaging System

Multimode fiber (MMF) based endoscopy could reach high resolution and is fine enough for vivo imaging. However, the received images are speckles due to the mode crosstalk and sensitivity to environment of MMF, which makes image reconstruction the main challenge. We propose to use pre-trained autoencoder for image reconstruction from speckles to original images, which shows high performance and fast convergence speed. The network architecture includes two parts, i.e., encoder and decoder. In the first step, we pre-train the network to initialize the parameters of the decoder. In the second step, the network can learn the mapping relation between speckle patterns and original images. We conduct experiment of transmitting over one-meter MMF with 50-μm-core to verify this method. Structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) are measured to evaluate the performance. Compared with U-net, the SSIM increases by 11% with pre-trained autoencoder. Moreover, the training of pre-trained autoencoder is both fast and steady.
A Performance Analysis of Parallel Differential Dynamic Programming on a GPU

Parallelism can be used to significantly increase the throughput of computationally expensive algorithms. With the widespread adoption of parallel computing platforms such as GPUs, it is natural to consider whether these architectures can benefit robotics researchers interested in solving trajectory optimization problems online. Differential Dynamic Programming (DDP) algorithms have been shown to achieve some of the best timing performance in robotics tasks by making use of optimized dynamics methods and CPU multi-threading. This paper aims to analyze the benefits and tradeoffs of higher degrees of parallelization using a multiple-shooting variant of DDP implemented on a GPU. We describe our implementation strategy and present results demonstrating its performance compared to an equivalent multi-threaded CPU implementation using several benchmark control tasks. Our results suggest that GPU-based solvers can offer increased per-iteration computation time and faster convergence in some cases, but in general tradeoffs exist between convergence behavior and degree of algorithm-level parallelism.
Recommendation System using Lexicon Based Sentimental Analysis with collaborative filtering

Looking at the current scenario it’s easy to come to the conclusion that it’s all about data. Data is being generated at an exponential rate. Not only the amount of data being generated is exponential but also the rate and variety are also accelerating. This also includes a tremendous increase in growth of service, customers and online reviews. More than trillion of reviews of various products are found on the internet. Earlier recommendation systems often put up efficiency problems. In this paper, we present the recommendation system to the customers based on their interest. Sentimental analysis is used to recommend based on the top k rated reviews based on top positive reviews of a geographically wise. Here we use the Hadoop Framework to make our system more efficient and scalable.
Signature Verification Using Convolutional Neural Network

Signatures are widely used to validate the authentication of an individual. A robust method is still awaited that can correctly certify the authenticity of a signature. The proposed solution provided in this paper is going to help individuals to distinguish signatures for determining whether a signature is forged or genuine. In our system, we aimed to automate the process of signature verification using Convolutional Neural Networks. Our model is constructed on top of a pre-trained Convolutional Neural Network called the VGG-19. We evaluated our model on widely accredited signature datasets with a multitude of genuine signature samples sourced from ICDAR[3], CEDAR[1] and Kaggle[2]; achieving accuracies of 100%, 88%, and 94.44% respectively. Our analysis shows that our proposed model can classify the signature if they do not closely resemble the genuine signature.
Improving Style Transfer with Calibrated Metrics

Style transfer produces a transferred image which is a rendering of a content image in the manner of a style image. We seek to understand how to improve style transfer.To do so requires quantitative evaluation procedures, but current evaluation is qualitative, mostly involving user studies. We describe a novel quantitative evaluation procedure. Our procedure relies on two statistics: the Effectiveness (E) statistic measures the extent that a given style has been transferred to the target, and the Coherence (C) statistic measures the extent to which the original image's content is preserved. Our statistics are calibrated to human preference: targets with larger values of E and C will reliably be preferred by human subjects in comparisons of style and content, respectively.We use these statistics to investigate relative performance of a number of Neural Style Transfer (NST) methods, revealing a number of intriguing properties. Admissible methods lie on a Pareto frontier (i.e. improving E reduces C, or vice versa). Three methods are admissible: Universal style transfer produces very good C but weak E; modifying the optimization used for Gatys' loss produces a method with strong E and strong C; and a modified cross-layer method has slightly better E at strong cost in C. While the histogram loss improves the E statistics of Gatys' method, it does not make the method admissible. Surprisingly, style weights have relatively little effect in improving EC scores, and most variability in transfer is explained by the style itself (meaning experimenters can be misguided by selecting styles). Our GitHub Link is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Mitigations on Sybil-Based Double-Spend Attacks in Bitcoin

Double-spending is a potential flaw in cryptocurrencies. In recent years, double-spend attacks have caused severe economic damage to many Bitcoin consumers, thus double-spending problems have attracted wide attention. Studying related countermeasures and mitigations against such attacks is imperative. In this article, we briefly introduce a new form of combined attack: double-spending with a Sybil attack in the Bitcoin network and discuss how to mitigate it. We propose two mitigations such as charging an identity fee and setting a deadline to defend against this attack, and we evaluate the effect of these two mitigations through an economic analysis.
Improved Generalizability of Deep-Fakes Detection using Transfer Learning Based CNN Framework

Deep-Fakes are emerging as a significant threat to society, with potential to become weapons of mass disinformation and chaos. Simple tools provide ways to produce such digital forgeries at a large scale which makes it crucial to develop counter-attacking approaches for detection of these Deep-Learning based manipulations. This work analyzes a Transfer Learning based Convolutional Neural Network framework for the task of Deep-Fake Detection on three of the latest released datasets - DeepFakeDetection (DFD), Celeb-DF, and DeepFakeDetectionChallenge (DFDC) Preview. Additionally, a custom dataset of high-quality Deep-Fakes is compiled and used for evaluation of models. The intuition behind Transfer Learning for Deep-Fakes Detection is explored using the Explainable-AI technique of visualizing intermediate activations to provide interpretability. The critical problem of dataset shift and its effect on domain adaptation is explored by comparing cross-dataset test accuracies, with and without the usage of Transfer Learning. The results of this work indicate that even though Deep-Fake Detection is a highly domain specific task, there is a significant improvement in performance in terms of both single-domain classification accuracy and generalizability by utilizing Transfer Learning.
Blockchain-Based Information Security of Electronic Medical Records (EMR) in a Healthcare Communication System

Protecting the digital medical data from cyber-attacks is essential in a healthcare communication system. Blockchain is a distributed as well as shared ledger-based technology which can more readily encourage information security. Blockchain approach is commonly applied for the digital crypto-currency; however, it is awaited to have future conducts in health care. Many healthcare systems today are still subordinated on the back-dated application for keeping healthcare records. Here, the main challenges faced to date are information security and interoperability. This study introduces a blockchain-based model for securing the Electronic Medical Records (EMR). Here, we have used SHA256 secure hash algorithm for generating a unique and identical 256-bit or 32-byte hash value for a particular medical record. In this manuscript, we have focused on five mechanisms (Digital Access Rules, Data Aggregation, Data Immutability, Data Liquidity, and Patient Identity) of data transition for securing the medical records at the proposed blockchain model.
FX-GAN: Self-Supervised GAN Learning via Feature Exchange

We propose a self-supervised approach to improve the training of Generative Adversarial Networks (GANs) via inducing the discriminator to examine the structural consistency of images. Although natural image samples provide ideal examples of both valid structure and valid texture, learning to reproduce both together remains an open challenge. In our approach, we augment the training set of natural images with modified examples that have degraded structural consistency. These degraded examples are automatically created by randomly exchanging pairs of patches in an image’s convolutional feature map. We call this approach feature exchange. With this setup, we propose a novel GAN formulation, termed Feature eXchange GAN (FX-GAN), in which the discriminator is trained not only to distinguish real versus generated images, but also to perform the auxiliary task of distinguishing between real images and structurally corrupted (feature-exchanged) real images. This auxiliary task causes the discriminator to learn the proper feature structure of natural images, which in turn guides the generator to produce images with more realistic structure. Compared with strong GAN baselines, our proposed self-supervision approach improves generated image quality, diversity, and training stability for both the unconditional and class-conditional settings.
A semi-supervised linear–nonlinear least-square learning network for prediction of carbon efficiency in iron ore sintering process

An iron ore sintering is a large energy-consuming process. The energy mainly comes from the combustion of carbon. Improving the carbon efficiency is beneficial to cost saving and environmental protection. The carbon efficiency has to be predicted before it can be improved. A semi-supervised linear–nonlinear least-square learning network (LLLN) was devised based on the process characteristics for the prediction of the carbon efficiency. First, a new comprehensive carbon ratio (CCR) that takes into account the coke residual was proposed for estimating the carbon efficiency. Then, the process characteristics that are concerned in building the model were presented. They are the existence of linear–nonlinear component and limited labeled samples. After that, a semi-supervised LLLN (SS-LLLN) approach that takes into account the process characteristics was presented for the prediction of the CCR. Last, actual run data was collected to verify the effectiveness of the proposed method. The error distribution, accuracy, and overfitness of an extreme learning machine (ELM), a semi-supervised ELM, an LLLN and an SS-LLLN were compared, which shows the effectiveness of the SS-LLLN.
Personalized query recommendation system : A genetic algorithm approach

Abstract Search engine has become an integral part of our daily life. It helps users to find a specific desirable information from the large amount of data stored in the web. Query recommendation feature of the search engine recommends several alternate queries to the user in response to the user’s original query. These recommendations essentially stay same for different users from a specific geographical region but acceptability of these alternate queries may vary from person to person. A genetic algorithm based personalized recommendation system has been proposed in this work. The proposed method has been evaluated using a commercial search engine’s search logs.
A complex pseudo-spectrum based velocity filtering method for track-before-detect

Velocity filtering based track-before-detect (VF-TBD) has advantages in energy integration and multi-target processing. However, in existing VF-TBD methods, real-valued measurements are used to be accumulated rather than complex-valued measurements. This results in significant waste of information and may degrade algorithm performance, since the phase of measurement is discarded. In this paper, a complex pseudo-spectrum (CPS) method is proposed for VF-TBD to improve the performance of weak target detection, based on the utilization of phase information during energy integration. Firstly, a CPS is constructed around the predicted position of each quantized cell with its complex-valued measurement. Samples of the CPSs from the same frame are added onto corresponding cells to obtain intraframe integrated spectrum, where the target echo values sampled on different cells in a frame have the same phase while noise measurement carries random phase, and the phase diversity is used to improve the performance of energy integration. This procedure can be regarded as coherent integration performed intraframe, contributing to improved integration efficiency. Then, amplitude values of the intraframe integrated results from all frames are added to the integration frame to achieve interframe energy integration. The theoretical detection threshold and receiver operating characteristic curve of the proposed CPS based method in complex Gaussian noise are derived in detail. Theoretical analyses and simulation results are provided to demonstrate the superiority of the proposed method.
Functional Error Correction for Robust Neural Networks

When neural networks (NeuralNets) are implemented in hardware, their weights need to be stored in memory devices. As noise accumulates in the stored weights, the NeuralNet's performance will degrade. This paper studies how to use error correcting codes (ECCs) to protect the weights. Different from classic error correction in data storage, the optimization objective is to optimize the NeuralNet's performance after error correction, instead of minimizing the Uncorrectable Bit Error Rate in the protected bits. That is, by seeing the NeuralNet as a function of its input, the error correction scheme is function-oriented. A main challenge is that a deep NeuralNet often has millions to hundreds of millions of weights, causing a large redundancy overhead for ECCs, and the relationship between the weights and its NeuralNet's performance can be highly complex. To address the challenge, we propose a Selective Protection (SP) scheme, which chooses only a subset of important bits for ECC protection. To find such bits and achieve an optimized tradeoff between ECC's redundancy and NeuralNet's performance, we present an algorithm based on deep reinforcement learning. Experimental results verify that compared to the natural baseline scheme, the proposed algorithm can achieve substantially better performance for the functional error correction task.
A convolutional fuzzy min-max neural network

Data updation is a natural process in real life machine leaning problems. Online machine learning is a possible solution to tackle such new data and facilitate continuous updation of knowledge without hampering the already acquired one. This work proposes a convolutional fuzzy min-max neural network (CFMNN) for image classification with online learning ability. Image classification using convolutional neural network (CNN) is a well-known and established technique. The CNN based systems are trained in offline mode. To recognize new classes in addition to the learned ones, CNN requires a complete re-training based on earlier and new data samples. This training process of CNN is time consuming. The proposed CFMNN is designed to address this problem without the need of re-training. The CFMNN can learn new data classes in an online mode. In this work, the concept of hyperbox fuzzy set is introduced for CNN to add the online learning proficiency. Four different CNN architectures are considered to verify the robustness of CFMNN performance. The proposed method is evaluated on standard datasets MNIST, Caltech-101, Plant seedlings and CIFAR-100. Accuracy and training time are used as performance metrics. The experimental results show that CFMNN with online learning capability has tremendously reduced training time and it gives compatible or better accuracy than the existing methods.