Reviewerly/exHarmony huggingface dataset:
Machine Learning With the Sugeno Integral: The Case of Binary Classification

In this article, we elaborate on the use of the Sugeno integral in the context of machine learning. More specifically, we propose a method for binary classification, in which the Sugeno integral is used as an aggregation function that combines several local evaluations of an instance, pertaining to different features, or measurements, into a single global evaluation. Due to the specific nature of the Sugeno integral, this approach is especially suitable for learning from ordinal data, i.e., when measurements are taken from ordinal scales. This is a topic that has not received much attention in machine learning so far. The core of the learning problem itself consists of identifying the capacity underlying the Sugeno integral. To tackle this problem, we develop an algorithm based on linear programming. The algorithm also includes a suitable technique for transforming the original feature values into local evaluations (local utility scores), as well as a method for tuning a threshold on the global evaluation. To control the flexibility of the classifier and mitigate the problem of overfitting the training data, we generalize our approach toward <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -maxitive capacities, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> plays the role of a hyperparameter of the learner. We present experimental studies, in which we compare our method with competing approaches on several benchmark datasets.
A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada’s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.
Geostatistical simulation of rock physical and geochemical properties with spatial filtering and its application to predictive geological mapping

Constructing an interpretive geological model of the subsurface based on site-specific geological, geophysical and geochemical sampling information is a primary step in the prospection, exploration and modeling of ore deposits, in order to understand geological heterogeneity and to predict the rock properties in the subsurface, which is critical for all the following stages of the mining process. To improve the accuracy of such geological models, a geostatistical approach based on the conditional simulation of quantitative variables followed with their classification into geological categories is proposed. The novelty is the use of coregionalization analysis that allows decomposing the quantitative information into uncorrelated components associated with different spatial scales and filtering the undesired components from the simulation outcomes, in particular, the nugget effect that represents small-scale variability and measurement errors. The methodology is illustrated with the predictive geological mapping in an iron ore deposit hosted by banded iron formations, based on a set of physical and geochemical variables such as metal grades available from geochemical assays and rock types available from geological core logging. First, the quantitative variables are simulated conditionally to exploration drill hole data, then classified into rock types. The simulation in which the nugget effect has been removed provides more regular boundaries between rock type domains and improves the predictability of the rock type, with an 90.1% cross-validation accuracy score against a 79.9% for the simulation that reproduces the spatial variability without any filtering. Despite its small amplitude, the small-scale variability in the quantitative variables considerably deteriorates the rock type classification.
A new parameter setting-based modified differential evolution for function optimization

The population-based efficient iterative evolutionary algorithm (EA) is differential evolution (DE). It has fewer control parameters but is useful when dealing with complex problems of optimization in the real world. A great deal of progress has already been made and implemented in various fields of engineering and science. Nevertheless, DE is prone to the setting of control parameters in its performance evaluation. Therefore, the appropriate adjustment of the time-consuming control parameters is necessary to achieve optimal DE efficiency. This research proposes a new version of the DE algorithm control parameters and mutation operator. For the justifiability of the suggested method, several benchmark functions are taken from the literature. The test results are contrasted with other literary algorithms.
Low Rank Communication for Federated Learning

Federated learning (FL) aims to learn a model with privacy protection through a distributed scheme over many clients. In FL, an important problem is to reduce the transmission quantity between clients and parameter server during gradient uploading. Because FL environment is not stable and requires enough client responses to be collected within a certain period of time, traditional model compression practices are not entirely suitable for FL setting. For instance, both design of the low-rank filter and the algorithm used to pursue sparse neural network generally need to perform more training rounds locally to ensure that the accuracy of model is not excessively lost. To breakthrough transmission bottleneck, we propose low rank communication Fedlr to compress whole neural network in clients reporting phase. Our innovation is to propose the concept of optimal compression rate. In addition, two measures are introduced to make up accuracy loss caused by truncation: training low rank parameter matrix and using iterative averaging. The algorithm is verified by experimental evaluation on public datasets. In particular, CNN model parameters training on the MNIST dataset can be compressed 32 times and lose only 2% of accuracy.
GoSafe: On the practical characterization of the overall security posture of an organization information system using smart auditing and ranking

The lack of national security standardization bodies can have adverse impact on the adoption of international security standards and best practices. To assure security confidence among various organizations and to promote systematic adoption of standards and best standards, a practical framework that can support comparative measures is needed. . This paper presents GoSafe, a novel practical cybersecurity assessment framework that is tailored to the ISO 2700x standard requirements for the development of Information Security Management System (ISMS). GoSafe can be used for both self-assessment and auditing/scoring tool by national cybersecurity authorities. Using GoSafe, organizations can evaluate their existing information security management systems against local and international standards by utilizing built-in pre-audit tools. As such, GoSafe will help organizations evaluate and enhance their readiness for evolving risks and threats. In GoSafe framework, a novel mathematical model was also designed and implemented for the scoring/rating tool, namely, the national cyber security index (aeNCI). The aeNCI employs multiple parameters to determine the maturity of existing cybersecurity programs at national organizations and generate a classification and comparison reports. The efficacy of GoSafe proposed framework is demonstrated using a practical case study. The results enabled the stakeholder to verify the security configuration of their systems and identify potential attack/risk vectors.
Huber’s M-Estimation-Based Cubature Kalman Filter for an INS/DVL Integrated System

To deal with the problems of outliers and nonlinearity in the complex underwater environment, a Huber’s M-estimation-based cubature Kalman filter (CKF) is proposed for an inertial navigation system (INS)/Doppler velocity log (DVL) integrated system. First, a loosely coupled INS/DVL integrated system is designed, and the nonlinear system model is established in the case of big misalignment angle. Then, Huber’s M-estimation is introduced for robust estimation to resist outliers. Meanwhile, the CKF is focused to handle the nonlinearity of the state equation. Finally, simulation and the vehicle test are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the conventional Kalman filter (KF) and outlier detection-based robust cubature Kalman filter (RCKF) in terms of navigation accuracy in the complex underwater environment.
Optical Character Recognition for Autonomous Stores

Autonomous stores are a new trend in the retail world. Vision-based product identification is one of the frequently used sensing modalities. There are different approaches and design configurations for the vision sensing but they all are based on extracting features for the product appearance, such as color, shape, dimension. None of the proposed solutions rely on reading the package label of the product being identified. In this paper, we present an Optical Character Recognition (OCR) framework, inspired by natural language processing, for package label detection and transcription from product images. To the best of the authors knowledge, this is the first autonomous store system that considers an OCR sensing modality. This is a step forward to a closer approximation to the human cognitive way of purchasing products.
The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search

Training classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious behavior. We report on the results of a recently held robust malware detection challenge. There were two tracks in which teams could participate: the attack track asked for adversarially modified malware samples and the defend track asked for trained neural network classifiers that are robust to such modifications. The teams were unaware of the attacks/defenses they had to detect/evade. Although only 9 teams participated, this unique setting allowed us to make several interesting observations.
Simulating Illness: Experiencing Visual Migraine Impairments in Virtual Reality

Combining virtual reality (VR) and serious games has shown high potential to raise understanding for patients and how symptoms might impact their well-being. To investigate migraine symptoms, we developed a visual migraine simulation in a virtual environment (VE) using a VR head-mounted display (HMD). For the migraine visualization we developed a migraine aura combined with an overexposure effect. We conducted a user study (N = 32) in a within-subject design with two conditions: (1) taking the perspective of a migraine patient and only imagining to suffer from migraine, (2) experiencing migraine by additionally being exposed to a migraine visualization. Participants answered questionnaires measuring their empathic reaction towards migraine patients and their experienced presence. Results yield that participants showed a significantly and notably higher empathic reaction and a slightly stronger sense of presence when visually experiencing migraine symptoms, compared to only imagining to suffer from migraine.
RVAE-ABFA : Robust Anomaly Detection for HighDimensional Data Using Variational Autoencoder

The curse of dimensionality is a fundamental difficulty in anomaly detection for high dimensional data. To deal with this problem, the autoencoder based approach is an elegant solution. However, existing works require a clean training dataset that is not always guaranteed in real scenarios. In this paper, we propose a novel anomaly detection method named RVAE-ABFA (robust variational autoencoder with attention based feature adaptation for high dimensional data anomaly detection), which significantly improves the anomaly detection performance when training data is contaminated. Rather than only utilize reconstruction error, we take the learned low dimensional embeddings generated by variational autoencoder into consideration. In RVAE-ABFA, the learned low dimensional embeddings are helpful to detect anomalies in contaminated data because of the ability of variational inference. We also propose an ABFA (attention based feature adaptation) mechanism to adjust the weights of low dimensional embeddings and reconstruction error. Furthermore, we adopt the adversarial training criterion to perform variational inference by the adversarial network named RAAE-ABFA (robust adversarial autoencoder with attention based feature adaptation for high dimensional data anomaly detection) in which we can generate extra samples when training data is not enough. Experimental results on several benchmark datasets show that the proposed method significantly outperforms state-of-the-art unsupervised anomaly detection methods and is more robust when training data is contaminated.
Generalized Net Model of the First Phase of the General Claim Process

In the present paper an application to the apparatus of generalized nets is proposed for modeling the first phase of the general claim process. The advantages of using such model are very different, some of them are expressed in helping to detect and eliminate unnecessary complications in the process itself, leading to its simplification and optimization, presenting a complex process simpler and more understandable, for scientific and teaching needs etc.
Security of Satellite-Based CV-QKD under Realistic Assumptions

With the vastly growing need for secure communication, quantum key distribution (QKD) has been developed to provide high security for communications against potential attacks from the fast-developing quantum computers. Among different QKD protocols, continuous variable (CV-) QKD employing Gaussian modulated coherent states has been promising for its complete security proof and its compatibility with current communication systems in implementation with homodyne or heterodyne detection. Since satellite communication has been more and more important in developing global communication networks, there have been concerns about the security in satellite communication and how we should evaluate the security of CV-QKD in such scenarios. To better analyse the secure key rate (SKR) in this case, in this invited paper we investigate the CV-QKD SKR lower bounds under realistic assumptions over a satellite-to-satellite channel. We also investigate the eavesdropper's best strategy to apply in these scenarios. We demonstrate that for these channel conditions with well-chosen carrier centre frequency and receiver aperture size, based on channel parameters, we can optimize SKR correspondingly. The proposed satellite-based QKD system provides high security level for the coming 5G and beyond networks, the Internet of things, self-driving cars, and other fast-developing applications.
Density peaks clustering based on k‐nearest neighbors sharing

Summary The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of samples are neglected. This leads to the result that DPC algorithm performs poorly on complex data sets with large difference in density, flow pattern or cross‐winding. In the meantime, the fault tolerance of allocation strategy for samples is relatively poor. Based on the findings, this article proposes a density peaks clustering based on k‐nearest neighbors sharing (DPC‐KNNS) algorithm, which uses the similarity between shared neighbors and natural neighbors to define the local density of samples and the allocation. Comparison between theoretical analysis and experiments on various synthetic and real data reveal that the algorithm proposed in this article can discover the cluster center of complex data sets with large difference in density, flow pattern or cross‐winding. It can also provide effective clustering.
Error-Tolerant Computation for Voting Classifiers With Multiple Classes

In supervised learning, labeled data are provided as inputs and then learning is used to classify new observations. Error tolerance should be guaranteed for classifiers when they are employed in critical applications. A widely used type of classifiers is based on voting among instances (referred to as single voter classifiers) or multiple voters (referred to as ensemble classifiers). When the classifiers are implemented on a processor, Time-Based Modular Redundancy (TBMR) techniques are often used for protection due to the inflexibility of the hardware. In TBMR, any single error can be handled at the cost of additional computing either once for detection or twice for correction after detection; however, this technique increases the computation overhead by at least 100%. The Voting Margin (VM) scheme has recently been proposed to reduce the computation overhead of TBMR, but this scheme has only been utilized for k Nearest Neighbors (kNNs) classifiers with two classes. In this paper, the VM scheme is extended to multiple classes, as well as other voting classifiers by exploiting the intrinsic robustness of the algorithms. kNNs (that is a single voter classifier) and Random Forest (RF) (that is an ensemble classifier) are considered to evaluate the proposed scheme. Using multiple datasets, the results show that the proposed scheme significantly reduces the computation overhead by more than 70% for kNNs with good classification accuracy and by more than 90% for RF in all cases. However, when extended to multiple classes, the VM scheme for kNNs is not efficient for some datasets. In this paper, a new protection scheme referred to as k + 1 NNs is presented as an alternative option to provide efficient protection in those scenarios. In the new scheme, the computation overhead can be further reduced at the cost of allowing a very low percentage of errors that can modify the classification outcome.
“So if Mr Blue Head here clicks the link...” Risk Thinking in Cyber Security Decision Making

Cyber security decision making is inherently complicated, with nearly every decision having knock-on consequences for an organisation’s vulnerability and exposure. This is further compounded by the fact that decision-making actors are rarely security experts and may have an incomplete understanding of the security that the organisation currently has in place. They must contend with a multitude of possible security options that they may only partially understand. This challenge is met by decision makers’ risk thinking —their strategies for identifying risks, assessing their severity, and prioritising responses. We study the risk thinking strategies employed by teams of participants in an existing dataset derived from a tabletop cyber-physical systems security game. Our analysis identifies four structural patterns of risk thinking and two reasoning strategies: risk-first and opportunity-first . Our work highlights that risk-first approaches (as prescribed by the likes of NIST-800-53 and ISO 27001) are followed neither substantially nor exclusively when it comes to decision making. Instead, our analysis finds that decision making is affected by the plasticity of teams—that is, the ability to readily switch between ideas and practising both risk-first and opportunity-first reasoning.
Face recognition system based on a single-board computer

With state-of-the-art computing systems along with Graphical Processing Units a Deep Neural Network can be realized by training on any publicly available dataset in order to detect faces. In a real-time application, the inference of such a neural network may not require high computational power as while in the training procedure. In this paper, the authors proposed face recognition and face detection system based on a single-board computer. Several different single-board computers like Raspberry Pi, Banana Pi and Nvidia Jetson Nano were evaluated. The authors compared two different face detection algorithms. These two algorithms are Haar feature-based cascade classifier and the second one is a multitask cascaded convolutional neural network (MTCNN). As a face recognition algorithm, the authors used FaceNet, which directly learns a mapping from face images to a compact Euclidean space where distances correspond to a measure of face similarity. FaceNet outputs embeddings as feature vectors which are feed to face classification algorithm. The system is trained and tested on a proprietary database and is installed to monitor who enters the room. The system has over 97% accuracy. The goal of this paper is to convey the possibility of successfully incorporating face recognition and face detection systems in small, low-power devices.
Learning run-time compositions of interacting adaptations

Self-adaptive systems continuously adapt to internal and external changes in their execution environment. In context-based self-adaptation, adaptations take place in response to the characteristics of the execution environment, captured as a context. However, in large-scale adaptive systems operating in dynamic environments, multiple contexts are often active at the same time, requiring simultaneous execution of multiple adaptations. Complex interactions between such adaptations might not have been foreseen or accounted for at design time. For example, adaptations can partially overlap, requiring only partial execution of each, or they can be conflicting, requiring some of the adaptations not to be executed at all, in order to preserve system execution. To ensure a correct composition of adaptations, we propose ComInA, a novel reinforcement learning based approach, which autonomously learns interactions between adaptations as well as the most appropriate adaptation composition for each combination of active contexts, as they arise. We present an initial evaluation of ComInA in an urban public transport network simulation, where multiple adaptations to buses, routes, and stations are required. Early results show that ComInA correctly identifies whether adaptations are compatible or conflicting and learns to execute adaptations which maximize system performance. However, further investigation is needed into how best to utilize such identified relationships to optimize a wider range of metrics and utilize more complex composition strategies.