Reviewerly/exHarmony huggingface dataset:
A Role-Based Access Control Model in Modbus SCADA Systems. A Centralized Model Approach

Industrial Control Systems (ICS) and Supervisory Control systems and Data Acquisition (SCADA) networks implement industrial communication protocols to enable their operations. Modbus is an application protocol that allows communication between millions of automation devices. Unfortunately, Modbus lacks basic security mechanisms, and this leads to multiple vulnerabilities, due to both design and implementation. This issue enables certain types of attacks, for example, man in the middle attacks, eavesdropping attacks, and replay attack. The exploitation of such flaws may greatly influence companies and the general population, especially for attacks targeting critical infrastructural assets, such as power plants, water distribution and railway transportation systems. In order to provide security mechanisms to the protocol, the Modbus organization released security specifications, which provide robust protection through the blending of Transport Layer Security (TLS) with the traditional Modbus protocol. TLS will encapsulate Modbus packets to provide both authentication and message-integrity protection. The security features leverage X.509v3 digital certificates for authentication of the server and client. From the security specifications, this study addresses the security problems of the Modbus protocol, proposing a new secure version of a role-based access control model (RBAC), in order to authorize both the client on the server, as well as the Modbus frame. This model is divided into an authorization process via roles, which is inserted as an arbitrary extension in the certificate X.509v3 and the message authorization via unit id, a unique identifier used to authorize the Modbus frame. Our proposal is evaluated through two approaches: A security analysis and a performance analysis. The security analysis involves verifying the protocol's resistance to different types of attacks, as well as that certain pillars of cybersecurity, such as integrity and confidentiality, are not compromised. Finally, our performance analysis involves deploying our design over a testnet built on GNS3. This testnet has been designed based on an industrial security standard, such as IEC-62443, which divides the industrial network into levels. Then both the client and the server are deployed over this network in order to verify the feasibility of the proposal. For this purpose, different latencies measurements in industrial environments are used as a benchmark, which are matched against the latencies in our proposal for different cipher suites.
Mapping Driver Mutations to Histopathological Subtypes in Papillary Thyroid Carcinoma: Applying a Deep Convolutional Neural Network

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancers and informative biomarkers are critical for risk stratification and treatment guidance. About half of PTCs harbor BRAFV600E and 10%-15% have RAS mutations. In the current study, we trained a deep learning convolutional neural network (CNN) model (Google Inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to classify PTCs into BRAFV600E or RAS mutations. We aimed to answer whether CNNs can predict driver gene mutations using images as the only input. The performance of our method is comparable to that of recent publications of other cancer types using TCGA tumor slides with area under the curve (AUC) of 0.878-0.951. Our model was tested on separate tissue samples from the same cohort. On the independent testing subset, the accuracy rate using the cutoff of truth rate 0.8 was 95.2% for BRAF and RAS mutation class prediction. Moreover, we showed that the image-based classification correlates well with mRNA-derived expression pattern (Spearman correlation, rho = 0.63, p = 0.002 on validation data and rho = 0.79, p = 2 × 10-5 on final testing data). The current study demonstrates the potential of deep learning approaches for histopathologically classifying cancer based on driver mutations. This information could be of value assisting clinical decisions involving PTCs.
Quantum two‐mode squeezing radar and noise radar: covariance matrices for signal processing

Recently, the authors have built and evaluated a prototype quantum radar in the laboratory which operates at microwave frequencies. This radar, which they call a quantum two-mode squeezing radar (QTMS radar), generates a pair of entangled microwave signals and transmits one of them through free space, using the other signal as a reference to perform matched filtering. The specific type of entanglement is called a two-mode squeezed vacuum, a type of continuous-variable entanglement between two frequencies. Motivated by the success of these experiments, they try to better understand the entangled QTMS radar signals in this study. They do so by comparing it to a simpler, more conventional radar system, which they call a two-mode noise radar (TMN radar). They also show how both types of radars are related to standard noise radars as described in the literature. They find that the signals for QTMS radar signals and TMN radar signals have the same mathematical form and that they are related to noise radar by a simple mathematical transformation. This shows that QTMS radar signals can be emulated by a fictional, idealised TMN radar and that it is possible to apply results from the noise radar literature to QTMS radar.
Robustness Verification of Classification Deep Neural Networks via Linear Programming

There is a pressing need to verify robustness of classification deep neural networks (CDNNs) as they are embedded in many safety-critical applications. Existing robustness verification approaches rely on computing the over-approximation of the output set, and can hardly scale up to practical CDNNs, as the result of error accumulation accompanied with approximation. In this paper, we develop a novel method for robustness verification of CDNNs with sigmoid activation functions. It converts the robustness verification problem into an equivalent problem of inspecting the most suspected point in the input region which constitutes a nonlinear optimization problem. To make it amenable, by relaxing the nonlinear constraints into the linear inclusions, it is further refined as a linear programming problem. We conduct comparison experiments on a few CDNNs trained for classifying images in some state-of-the-art benchmarks, showing our advantages of precision and scalability that enable effective verification of practical CDNNs.
LS-CNN: Characterizing Local Patches at Multiple Scales for Face Recognition

Faces in the wild may contain pose variations, age changes, and with different qualities which significantly enlarge the intra-class variations. Although great progresses have been made in face recognition, few existing works could learn local and multi-scale representations together. In this work, we propose a new model, called Local and multi-Scale Convolutional Neural Networks (LS-CNN). First, since similar discriminative face regions may occur at different scales, it is necessary to learn multi-scale features. To this aim, we introduce a new backbone network, namely Harmonious multi-Scale Network (HSNet), which extracts rich multi-scale features from two harmonious perspectives: utilization of different kernel sizes in a single layer, and concatenation of multi-scale feature maps from different layers. Second, identifying similar local patches is important when global face appearances have dramatic changes. Meanwhile, different face regions have different discriminative abilities. To capture critical local similarities and weigh adaptively on different local patches, a spatial attention is proposed. Third, channels have different convolutional kernels which can detect different features with various importance. Besides, hierarchical channels concatenated from different layers contain diverse information: channels from low layers describe local details or small-scale parts, and channels in high layers represent high-level abstraction or large-scale parts. To emphasize important channels and suppress less informative ones automatically, channel attention is used. Due to the complementary characteristics of channel attention and spatial attention, they are fused to form the Dual Face Attentions (DFA). To the best of our knowledge, this is the first effort to employ attentions for the general face recognition task. The LS-CNN is developed by incorporating DFA into HSNet model. Experimental results on various face matching tasks show its capability of learning complex data distributions.
Multi-Touch Kit

Mutual capacitance-based multi-touch sensing is now a ubiquitous and high-fidelity input technology. However, due to the complexity of electrical and signal processing requirements, it remains very challenging to create interface prototypes with custom-designed multi-touch input surfaces. In this paper, we introduce Multi-Touch Kit, a technique enabling electronics novices to rapidly prototype customized capacitive multi-touch sensors. In contrast to existing techniques, it works with a commodity microcontroller and open-source software and does not require any specialized hardware. Evaluation results show that our approach enables multi-touch sensors with a high spatial and temporal resolution and can accurately detect multiple simultaneous touches. A set of application examples demonstrates the versatile uses of our approach for sensors of different scales, curvature, and materials.
Mise-Unseen

Creating or arranging objects at runtime is needed in many virtual reality applications, but such changes are noticed when they occur inside the user's field of view. We present Mise-Unseen, a software system that applies such scene changes covertly inside the user's field of view. Mise-Unseen leverages gaze tracking to create models of user attention, intention, and spatial memory to determine if and when to inject a change. We present seven applications of Mise-Unseen to unnoticeably modify the scene within view (i) to hide that task difficulty is adapted to the user, (ii) to adapt the experience to the user's preferences, (iii) to time the use of low fidelity effects, (iv) to detect user choice for passive haptics even when lacking physical props, (v) to sustain physical locomotion despite a lack of physical space, (vi) to reduce motion sickness during virtual locomotion, and (vii) to verify user understanding during story progression. We evaluated Mise-Unseen and our applications in a user study with 15 participants and find that while gaze data indeed supports obfuscating changes inside the field of view, a change is rendered unnoticeably by using gaze in combination with common masking techniques.
A novel hybrid BPSO–SCA approach for feature selection

Abstract Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features not only increase computational complexity of the underlying algorithm but also deteriorate its performance. Hence, there an urgent need of feature selection method that select an informative subset of features from high dimensional without compromising the performance of the underlying algorithm. In this paper, we select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization (BPSO) and sine cosine algorithm (SCA) named as hybrid binary particle swarm optimization and sine cosine algorithm (HBPSOSCA). Here, we employ a V-shaped transfer function to compute the likelihood of changing position for all particles. First, the effectiveness of the proposed method is tested on ten benchmark test functions. Second, the HBPSOSCA is used for data clustering problem on seven real-life datasets taken from the UCI machine learning store and gene expression model selector. The performance of proposed method is tested in comparison to original BPSO, modified BPSO with chaotic inertia weight (C-BPSO), binary moth flame optimization algorithm, binary dragonfly algorithm, binary whale optimization algorithm, SCA, and binary artificial bee colony algorithm. The conducted analysis demonstrates that the proposed method HBPSOSCA attain better performance in comparison to the competitive methods in most of the cases.
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i.e. deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes the quality of a naïve extension of unsupervised image denoisers to 3D point clouds unfortunately only little better than mean filtering. To overcome this, and to enable effective and unsupervised 3D point cloud denoising, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on the manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.
FaDe: A Blockchain-Based Fair Data Exchange Scheme for Big Data Sharing

In the big data era, data are envisioned as critical resources with various values, e.g., business intelligence, management efficiency, and financial evaluations. Data sharing is always mandatory for value exchanges and profit promotion. Currently, certain big data markets have been created for facilitating data dissemination and coordinating data transaction, but we have to assume that such centralized management of data sharing must be trustworthy for data privacy and sharing fairness, which very likely imposes limitations such as joining admission, sharing efficiency, and extra costly commissions. To avoid these weaknesses, in this paper, we propose a blockchain-based fair data exchange scheme, called FaDe. FaDe can enable de-centralized data sharing in an autonomous manner, especially guaranteeing trade fairness, sharing efficiency, data privacy, and exchanging automation. A fairness protocol based on bit commitment is proposed. An algorithm based on blockchain script architecture for a smart contract, e.g., by a bitcoin virtual machine, is also proposed and implemented. Extensive analysis justifies that the proposed scheme can guarantee data exchanging without a trusted third party fairly, efficiently, and automatically.
A novel deep neural network based on randomly occurring distributed delayed PSO algorithm for monitoring the energy produced by four dual-axis solar trackers

An accurate predictive model is essential for monitoring the energy produced by a solar system based on different meteorological parameters. In the present paper, a novel machine-learning model named DNN-RODDPSO is proposed to improve the real-time prediction accuracy of the hourly energy produced by four dual-axis solar trackers. This model integrates a new deep neural network (DNN) model with a recent variant of PSO algorithm referred to as a randomly occurring distributed delayed particle swarm optimization (RODDPSO) algorithm. This algorithm is adopted to enhance the training process of the DNN model by reducing the risk of being trapped into local optima and for the search space diversification. Furthermore, to develop the DNN-RODDPSO model, the hourly observations of seven meteorological parameters including time variable measured during 2014–2015 in Alice Springs city, Australia, are used. This model integrates two novel hidden layers, the first one is a selective layer based on daytime/nighttime data selection. The second one named automatic inputs relevance determination to point out the most relevant inputs for an accurate prediction. The obtained results demonstrate the high performance of the two novel hidden layers and the RODDPSO algorithm to improve significantly the prediction accuracy compared to the actual literature standards.
Boundless: Generative Adversarial Networks for Image Extension

Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repetitive pixels with inconsistent semantics. We introduce semantic conditioning to the discriminator of a generative adversarial network (GAN), and achieve strong results on image extension with coherent semantics and visually pleasing colors and textures. We also show promising results in extreme extensions, such as panorama generation.
Embodied Amodal Recognition: Learning to Move to Perceive Objects

Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded. In contrast, humans and other embodied agents have the ability to move in the environment and actively control the viewing angle to better understand object shapes and semantics. In this work, we introduce the task of Embodied Amodel Recognition (EAR): an agent is instantiated in a 3D environment close to an occluded target object, and is free to move in the environment to perform object classification, amodal object localization, and amodal object segmentation. To address this problem, we develop a new model called Embodied Mask R-CNN for agents to learn to move strategically to improve their visual recognition abilities. We conduct experiments using a simulator for indoor environments. Experimental results show that: 1) agents with embodiment (movement) achieve better visual recognition performance than passive ones and 2) in order to improve visual recognition abilities, agents can learn strategic paths that are different from shortest paths.
Improving the accuracy using pre-trained word embeddings on deep neural networks for Turkish text classification

Today, extreme amounts of data are produced, and this is commonly referred to as Big Data. A significant amount of big data is composed of textual data, and as such, text processing has correspondingly increased in its importance. This is especially valid to the development of word embedding and other groundbreaking advancements in this field. However, When studies on text processing and word embedding are examined, it can be seen that while there have been many world language-oriented studies, especially for the English language, there has been an insufficient level of study undertaken specific to the Turkish language. As a result, Turkish was chosen as the target language for the current study. Two Turkish datasets were created for this study. Word vectors were trained using the Word2Vec method on an unlabeled large corpus of approximately 11 billion words. Using these word vectors, text classification was applied with deep neural networks on a second dataset of 1.5 million examples and 10 classes. The current study employed the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods – other types of this architecture – and their variations as deep neural network architectures. The performances of the embedding methods for the words used in this study, their effects on the rate of accuracy, and the success of the deep neural network architectures were then analyzed in detail. When studying the experimental results, it was determined that the GRU and LSTM methods were more successful compared to the other deep neural network models used in this study. The results showed that the pre-trained word vectors’ (PWVs) accuracy on deep neural networks improved at rates of approximately 5% and 7%. The datasets and word vectors of the current study will be shared in order to contribute to the Turkish language literature in this field.
Adversarial Colorization of Icons Based on Contour and Color Conditions

We present a system to help designers create icons that are widely used in banners, signboards, billboards, homepages, and mobile apps. Designers are tasked with drawing contours, whereas our system colorizes contours in different styles. This goal is achieved by training a dual conditional generative adversarial network (GAN) on our collected icon dataset. One condition requires the generated image and the drawn contour to possess a similar contour, while the other anticipates the image and the referenced icon to be similar in color style. Accordingly, the generator takes a contour image and a man-made icon image to colorize the contour, and then the discriminators determine whether the result fulfills the two conditions. The trained network is able to colorize icons demanded by designers and greatly reduces their workload. For the evaluation, we compared our dual conditional GAN to several state-of-the-art techniques. Experiment results demonstrate that our network is over the previous networks. Finally, we will provide the source code, icon dataset, and trained network for public use.
Neuroscience-Inspired Online Unsupervised Learning Algorithms: Artificial Neural Networks

Inventors of the original artificial neural networks (ANNs) derived their inspiration from biology [1]. However, today, most ANNs, such as backpropagation-based convolutional deeplearning networks, resemble natural NNs only superficially. Given that, on some tasks, such ANNs achieve human or even superhuman performance, why should one care about such dissimilarity with natural NNs? The algorithms of natural NNs are relevant if one's goal is not just to outperform humans on certain tasks but to develop general-purpose artificial intelligence rivaling that of a human. As contemporary ANNs are far from achieving this goal and natural NNs, by definition, achieve it, natural NNs must contain some "secret sauce" that ANNs lack. This is why we need to understand the algorithms implemented by natural NNs.
An Empirical Study on Culture, Automation, Measurement, and Sharing of DevSecOps

Programmers are central agents in creating secure applications. However, software engineering and software security have historically worked in separate silos. DevSecOps is the practice of breaking down silos between development, operations, and quality assurance of security. To understand the state of art and challenges of DevSecOps, we interviewed six developers about their DevSecOps practices. We asked interview subjects about their security practices rooted in the four pillars of DevOps, namely culture, automation, sharing, and measurement. Results were analyzed by using qualitative methods. The results of the study show that it is necessary first to create a security culture. Several interviewees identified the importance of caring about security and issues in existing culture, such as how developers feel attacked by security engineers if they create vulnerable code. After establishing a security culture in the organization, development and operations need the necessary training and knowledge so that security automation tools can be utilized effectively. Measurements need to be applied continuously to keep track of identified vulnerabilities, the amount of training staff has received, and staffs general opinions regarding security.