Reviewerly/exHarmony huggingface dataset:
Assessing the Severity of Smart Attacks in Industrial Cyber-Physical Systems

Industrial cyber-physical systems (&lt;monospace&gt;ICPS&lt;/monospace&gt;) are heterogeneous inter-operating parts that can be physical, technical, networking, and even social like agent operators. Incrementally, they perform a central role in critical and industrial infrastructures, governmental, and personal daily life. Especially with the Industry 4.0 revolution, they became more dependent on the connectivity by supporting novel communication and distance control functionalities, which expand their attack surfaces that result in a high risk for cyber-attacks. Furthermore, regarding physical and social constraints, they may push up new classes of security breaches that might result in serious economic damages. Thus, designing a secure &lt;monospace&gt;ICPS&lt;/monospace&gt; is a complex task, since this needs to guarantee security and harmonize the functionalities between the various parts that interact with different technologies. This article highlights the significance of cyber-security infrastructure and shows how to evaluate, prevent, and mitigate &lt;monospace&gt;ICPS&lt;/monospace&gt;-based cyber-attacks. We carried out this objective by establishing an adequate semantics for &lt;monospace&gt;ICPS&lt;/monospace&gt;’s entities and their composition, which includes social actors that act differently than mobile robots and automated processes. This article also provides the feasible attacks generated by a reinforcement learning mechanism based on multiple criteria that selects both appropriate actions for each &lt;monospace&gt;ICPS&lt;/monospace&gt; component and the possible countermeasures for mitigation. To efficiently analyze &lt;monospace&gt;ICPS&lt;/monospace&gt;’s security, we proposed a model-checking-based framework that relies on a set of predefined attacks from where the security requirements are used to assess how well the model is secure. Finally, to show the effectiveness of the proposed solution, we model, analyze, and evaluate the &lt;monospace&gt;ICPS&lt;/monospace&gt; security on two real use cases.
Image-Based Face Detection and Recognition

Face recognition from image or video is a popular topic in biometrics research. Many public places usually have surveillance cameras for video capture and these cameras have their significant value for security purpose. It is widely acknowledgedthatthefacerecognitionhaveplayedanimportant role in surveillance system as it doesn’t need the object’s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a mainissue. The goal of this paper is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy, better response rate as an initial step for video surveillance. Solution is proposed based on performed tests on various face rich databases in terms of subjects, pose, emotions, race andlight.
Fraud Detection in Credit Cards using Logistic Regression

Due to the increasing number of customers as well as the increasing number of companies that use credit cards for ending financial transactions, the number of fraud cases has increased dramatically.Dealing with noisy and imbalanced data, as well as with outliers, has accentuated this problem.In this work, fraud detection using artificial intelligence is proposed.The proposed system uses logistic regression to build the classifier to prevent frauds in credit card transactions.To handle dirty data and to ensure a high degree of detection accuracy, a pre-processing step is used.The pre-processing step uses two novel main methods to clean the data: the mean-based method and the clustering-based method.Compared to two well-known classifiers, the support vector machine classifier and voting classifier, the proposed classifier shows better results in terms of accuracy, sensitivity, and error rate.
Comparative Evaluation of CNN Architectures for Image Caption Generation

Aided by recent advances in Deep Learning, Image Caption Generation has seen tremendous progress over the last few years.Most methods use transfer learning to extract visual information, in the form of image features, with the help of pre-trained Convolutional Neural Network models followed by transformation of the visual information using a Caption Generator module to generate the output sentences.Different methods have used different Convolutional Neural Network Architectures and, to the best of our knowledge, there is no systematic study which compares the relative efficacy of different Convolutional Neural Network architectures for extracting the visual information.In this work, we have evaluated 17 different Convolutional Neural Networks on two popular Image Caption Generation frameworks: the first based on Neural Image Caption (NIC) generation model and the second based on Soft-Attention framework.We observe that model complexity of Convolutional Neural Network, as measured by number of parameters, and the accuracy of the model on Object Recognition task does not necessarily co-relate with its efficacy on feature extraction for Image Caption Generation task.We release the code at https://github.com/iamsulabh/cnnvariants.
Networked VR: State of the Art, Solutions, and Challenges

The networking of virtual reality applications will play an important role in the emerging global Internet of Things (IoT) framework and it is expected to provide the foundation of the expected 5G tactile Internet ecosystem. However, considerable challenges are ahead in terms of technological constraints and infrastructure costs. The raw data rate (5 Gbps–60 Gbps) required achieving an online immersive experience that is indistinguishable from real life vastly exceeds the capabilities of future broadband networks. Therefore, simply providing high bandwidth is insufficient in compensating for this difference, because the demands for scale and supply vary widely. This requires exploring holistic solutions that exceed the traditional network domain, and integrating virtual reality (VR) data capture, encoding, network, and user navigation. Emerging services are extremely inefficient in terms of mass use and data management, which significantly reduces the user experience, due to their heuristic design choices. Other key aspects must be considered, such as wireless operation, ultra-low latency, client/network access, system deployment, edge computing/cache, and end-to-end reliability. A vast number of high-quality works have been published in this area and they will be highlighted in this survey. In addition to a thorough summary of recent progress, we also present an outlook of future developments in the quality of immersive experience networks and unified data set measurement in VR video transmission, focusing on the expansion of VR applications, security issues, and business issues, which have not yet been addressed, and the technical challenges that have not yet been completely solved. We hope that this paper will help researchers and developers to gain a better understanding of the state of research and development in VR.
Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears

Effective determination of malaria parasitemia is paramount in aiding clinicians to accurately estimate the severity of malaria and guide the response for quality treatment. Microscopy by thick smear blood films is the conventional method for malaria parasitemia determination. Despite its edge over other existing methods of malaria parasitemia determination, it has been critiqued for being laborious, time consuming and equally requires expert knowledge for an efficient manual quantification of the parasitemia. This pauses a big challenge to most low developing countries as they are not only highly endemic but equally low resourced in terms of technical personnel in medical laboratories This study presents an end-to-end deep learning approach to automate the localization and count of P.falciparum parasites and White Blood Cells (WBCs) for effective parasitemia determination. The method involved building computer vision models on a dataset of annotated thick blood smear images. These computer vision models were built based on pre-trained deep learning models including Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) models that help process the obtained digital images. To improve model performance due to a limited dataset, data augmentation was applied. Results from the evaluation of our approach showed that it reliably detected and returned a count of parasites and WBCs with good precision and recall. A strong correlation was observed between our model-generated counts and the manual counts done by microscopy experts (posting a spear man correlation of ρ = 0.998 for parasites and ρ = 0.987 for WBCs). Additionally, our proposed SSD model was quantized and deployed on a mobile smartphone-based inference app to detect malaria parasites and WBCs in situ. Our proposed method can be applied to support malaria diagnostics in settings with few trained Microscopy Experts yet constrained with large volume of patients to diagnose.
A fractal resistance-capacitance circuit model for the current flowing in porous media

The current flows in a porous media is a very complex nature phenomenon, and it is very difficult to establish its mathematical model with the traditional definition of derivative. In this paper, the fractal resistance-capacitance circuit of porous media is successfully established based on He?s fractal derivative, and the two-scale transform is adopted to solve the fractal circuit. In this fractal resistance-capacitance circuit circuit, the fractal dimension represents the effective porosity of the two plates of the capacitor, and the influence of its value on the fractal resistance-capacitance circuit circuit is also elaborated.
Photon-assisted Landau–Zener transitions in a periodically driven Rabi dimer coupled to a dissipative mode

We investigate multiple photon-assisted Landau–Zener (LZ) transitions in a hybrid circuit quantum electrodynamics device in which each of two interacting transmission-line resonators is coupled to a qubit, and the qubits are driven by periodic driving fields and also coupled to a common phonon mode. The quantum state of the entire composite system is modeled using the multi-D2 Ansatz in combination with the time-dependent Dirac–Frenkel variational principle. Applying a sinusoidal driving field to one of the qubits, this device is an ideal platform to study the photon-assisted LZ transitions by comparing the dynamics of the two qubits. A series of interfering photon-assisted LZ transitions takes place if the photon frequency is much smaller than the driving amplitude. Once the two energy scales are comparable, independent LZ transitions arise and a transition pathway is revealed using an energy diagram. It is found that both adiabatic and nonadiabatic transitions are involved in the dynamics. Used to model environmental effects on the LZ transitions, the common phonon mode coupled to the qubits allows for more available states to facilitate the LZ transitions. An analytical formula is obtained to estimate the short time phonon population and produces results in reasonable agreement with numerical calculations. Equipped with the knowledge of the photon-assisted LZ transitions in the system, we can precisely manipulate the qubit state and successfully generate the qubit dynamics with a square-wave pattern by applying driving fields to both qubits, opening up new venues to manipulate the states of qubits and photons in quantum information devices and quantum computers.
Maximum entropy models from phase harmonic covariances

The covariance of a stationary process $X$ is diagonalized by a Fourier transform. It does not take into account the complex Fourier phase and defines Gaussian maximum entropy models. We introduce a general family of phase harmonic covariance moments, which rely on complex phases to capture non-Gaussian properties. They are defined as the covariance of $\hat{H} (L X)$, where $L$ is a complex linear operator and $\hat{H} $ is a non-linear phase harmonic operator which multiplies the phase of each complex coefficient by integers. The operator $\hat{H} (L X)$ can also be calculated from rectifiers, which relates $\hat{H} (L X)$ to neural network coefficients. If $L$ is a Fourier transform then the covariance is a sparse matrix whose non-zero off-diagonal coefficients capture dependencies between frequencies. These coefficients have similarities with high order moment, but smaller statistical variabilities because $\hat{H} (L X)$ is Lipschitz. If $L$ is a complex wavelet transform then off-diagonal coefficients reveal dependencies across scales, which specify the geometry of local coherent structures. We introduce maximum entropy models conditioned by these wavelet phase harmonic covariances. The precision of these models is numerically evaluated to synthesize images of turbulent flows and other stationary processes.
An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing

The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dynamically choose k for each test instance to be classified. However, given a large dataset, it becomes very tasking to maximize the k-Nearest Neighbor performance by tuning k. This work proposes the use of Simulated Annealing, a metaheuristic search algorithm, to select optimal k, thus eliminating the prospect of an exhaustive search for optimal k. The results obtained in four different classification tasks demonstrate a significant improvement in the computational efficiency against the k-Nearest Neighbor methods that perform exhaustive search for k, as accurate nearest neighbors are returned faster for k-Nearest Neighbor classification, thus reducing the computation time.
Cost-Aware Feature Selection for IoT Device Classification

Classification of IoT devices into different types is of paramount importance, from multiple perspectives, including security and privacy aspects. Recent works have explored machine learning techniques for fingerprinting (or classifying) IoT devices, with promising results. However, existing works have assumed that the features used for building the machine learning models are readily available or can be easily extracted from the network traffic; in other words, they do not consider the costs associated with feature extraction. In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features. We also take a step forward from the current practice of considering the misclassification loss as a binary value, and make a case for different losses based on the misclassification performance. Thereby, and more importantly, we introduce the notion of risk for IoT device classification. We define and formulate the problem of cost-aware IoT device classification. This being a combinatorial optimization problem, we develop a novel algorithm to solve it in a fast and effective way using the Cross-Entropy (CE) based stochastic optimization technique. Using traffic of real devices, we demonstrate the capability of the CE based algorithm in selecting features with minimal risk of misclassification while keeping the cost for feature extraction within a specified limit.
Skeleton-Based Square Grid for Human Action Recognition With 3D Convolutional Neural Network

Convolutional neural networks (CNNs) can effectively handle grid-structured data but not dynamic skeletons, which are usually expressed as graph structures. In this study, we first propose a skeleton-based square grid (SSG) for transforming dynamic skeletons into three-dimensional (3D) grid-structured data so that CNNs can be applied to such data. Each SSG contains a joint-based square grid (JSG) and a rigid-based square grid (RSG) based on intrinsic and extrinsic dependencies of various body parts, respectively. Next, to enhance the ability of deep features to capture the correlations among 3D grid-structured data, a two-stream 3D CNN is constructed to learn spatiotemporal features using the JSG and RSG sequences. Finally, we introduce a soft attention model that selectively focuses on the informative body parts in the skeleton sequences. We validate our model in terms of action recognition using three datasets: NTU RGB+D, Kinetics Motion, and SBU Kinect Interaction datasets. Our experimental results demonstrate the effectiveness of the proposed approach as well as its superior performance when compared with those of state-of-the-art methods.
Detection of Phishing in Internet of Things Using Machine Learning Approach

Phishing attacks are growing in the similar manner as e-commerce industries are growing. Prediction and prevention of phishing attacks is a very critical step towards safeguarding online transactions. Data mining tools can be applied in this regard as the technique is very easy and can mine millions of information within seconds and deliver accurate results. With the help of machine learning algorithms like random forest, decision tree, neural network, and linear model, we can classify data into phishing, suspicious, and legitimate. The devices that are connected over the internet, known as internet of things (IoT), are also at very high risk of phishing attack. In this work, machine learning algorithms random forest classifier, support vector machine, and logistic regression have been applied on IoT dataset for detection of phishing attacks, and then the results have been compared with previous work carried out on the same dataset as well as on a different dataset. The results of these algorithms have then been compared in terms of accuracy, error rate, precision, and recall.
A Study on Semi-directed Graphs for Social Media Networks

In the literature of graph theory, networks are represented as directed graphs or undirected graphs and a mixed of both combinations.In today's era of computing, networks like brain and facebook that do not belong to any of the mentioned networks category and in fact, it belongs to the combination of both networks which have connections as directed as well as undirected.To represent such networks, semi-directed graphs have been studied in this paper that provides the detailed mathematical fundamentals related to better understand the conceptualization for social media networks.This paper also discusses the suitable matrices analyze for the representation of the graphs.Few new terminologies like incidence number, complete-incidence related to semi-directed graphs and counter isomorphism of semi-directed graphs have been inculcated.A centrality measure, namely incidence centrality, has also been proposed based on incidence number on neighbors in social media networks.
Automatic phenotyping of electronical health record: PheVis algorithm

Electronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Phenorm, an automated unsupervised algorithm to identify patient medical conditions from EHR data, has been developed. PheVis extends PheNorm at the visit resolution. PheVis combines diagnosis codes together with medical concepts extracted from medical notes, incorporating past history in a machine learning approach to provide an interpretable parametric predictor of the occurrence probability for a given medical condition at each visit. PheVis is applied to two real-world use-cases using the datawarehouse of the University Hospital of Bordeaux: i) rheumatoid arthritis, a chronic condition; ii) tuberculosis, an acute condition. Cross-validated AUROC were respectively 0.943 [0.940; 0.945] and 0.987 [0.983; 0.990]. Cross-validated AUPRC were respectively 0.754 [0.744; 0.763] and 0.299 [0.198; 0.403]. PheVis performs well for chronic conditions, though absence of exclusion of past medical history by natural language processing tools limits its performance in French for acute conditions. It achieves significantly better performance than state-of-the-art unsupervised methods especially for chronic diseases.
Continual Learning for Named Entity Recognition

Named Entity Recognition (NER) is a vital task in various NLP applications. However, in many real-world scenarios (e.g., voice-enabled assistants) new named entities are frequently introduced, entailing re-training NER models to support these new entities. Re-annotating the original training data for the new entities could be costly or even impossible when storage limitations or security concerns restrict access to that data, and annotating a new dataset for all of the entities becomes impractical and error-prone as the number of entities increases. To tackle this problem, we introduce a novel Continual Learning approach for NER, which requires new training material to be annotated only for the new entities. To preserve the existing knowledge previously learned by the model, we exploit the Knowledge Distillation (KD) framework, where the existing NER model acts as the teacher for a new NER model (i.e., the student), which learns the new entity by using the new training material and retains knowledge of old entities by imitating the teacher's outputs on this new training set. Our experiments show that this approach allows the student model to ``progressively'' learn to identify new entities without forgetting the previously learned ones. We also present a comparison with multiple strong baselines to demonstrate that our approach is superior for continually updating an NER model.
Virtual Reality Customized 360-Degree Experiences for Stress Relief

The latest studies in virtual reality (VR) have evidenced the potential of this technology to reproduce environments from multiple domains in an immersive way. For instance, in stress relief research, VR has been presented as a portable and inexpensive alternative to chromotherapy rooms, which require an adapted space and are expensive. In this work, we propose a portable and versatile alternative to the traditional chromotherapy color-loop treatment through four different 360-degree virtual experiences. A group of 23 healthy participants (mean age 22.65 ± 5.48) were conducted through a single-session experience divided into four phases while their electroencephalography (EEG) was recorded. First, they were stressed via the Montreal imaging stress task (MIST), and then relaxed using our VR proposal. We applied the Wilcoxon test to evaluate the relaxation effect in terms of the EEG relative gamma and self-perceived stress surveys. The results that we obtained validate the effectiveness of our 360-degree proposal to significantly reduce stress (p-value = 0.0001). Furthermore, the participants deemed our proposal comfortable and immersive (score above 3.5 out of 5). These results suggest that 360-degree VR experiences can mitigate stress, reduce costs, and bring stress relief assistance closer to the general public, like in workplaces or homes.