Reviewerly/exHarmony huggingface dataset:
Behaviour-Based Biometrics for Continuous User Authentication to Industrial Collaborative Robots

Collaborative robots (cobots) work in close proximity with human co-workers to accomplish tasks. The proximity of working arrangements and the power required of some cobots for particular tasks means that there is significant potential for cobots to cause damage to their surroundings and people nearby. Working with cobots requires appropriate training and skill. We must ensure that co-workers access appropriate levels of service and functionality from a cobot. We would wish to stop intruders engaging with cobots but also to protect against inappropriate informal working arrangements by colleagues. In this paper, we consider the potential for users’ behaviours to be used as a biometric approach to continuous user authentication. More specifically, we consider how data from a cobot’s internal sensors can be used to characterise a user’s physical interaction with it and serve as a reference template for authentication of that user. We seek to continuously authenticate current user behaviours against these stored characteristic templates while the cobot is being manipulated (as part of a collaborative task). Our approach, based on machine learning and a recognised trust model, can provide a sensible, practical solution to authenticate users continuously as they physically interact with a cobot. Furthermore, it makes use of data that are already maintained by the cobot as part of its general operation. Our work is the first to exploit such data.
Student Prediction of Drop Out Using Extreme Learning Machine (ELM) Algorithm

The purpose of this study was to predict students dropping out of the Education Management Doctoral Program of FKIP Mulawarman University and to evaluate the Extreme Learning Machine in predicting student dropouts. This research uses the Extreme Learning Machine algorithm, the feedforward neural network learning method and the Support Vector Machine algorithm for comparison of the level of accuracy using the same data. The data used is as much as 110 data according to the number of students from the class of 2012 to 2018, the data is taken from the SIA Education Management Study Program of the Mulawarman University Doctoral Program and then processed. In this case, how to predict student dropouts using the variable Gender, Semester 3 IP Value, Working Status, Family Status, Age, and using two DO and NON DO Classes? And calculating the accuracy value using a confusion matrix ?. From the results of this study, it can be concluded that students drop out in the Educational Management of the FKIP Mulawarman University Doctoral Program can be predicted by Extreme Learning Machine using the training value obtained from semester 3 of the 2012-2018 class. From the results of testing the predictive accuracy of the Extreme Learning Machine is 72 %.
Group Instance: Flexible Co-Location Resistant Virtual Machine Placement in IaaS Clouds

This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared to more complicated, state-of-the-art algorithms in terms of resource utilization and co-location security, and 3) it does not require any modifications to the underlying infrastructures of existing public cloud services.
Quantifying the hybrid entanglement of the Stern-Gerlach experiment using discrete reductions

Hybrid entanglement occurs between discrete and continuous variables, resulting in a considerable difficulty for its quantification. However, some advancements have been made, one of them is the discretization of the continuous variable as a tool to quantify it. Here, in this paper, the applicability of discrete entanglement measures to the final quantum state of the Stern-Gerlach Experiment is discussed in order to quantify its hybrid entanglement. Additionally, the possibility of using an equivalent discrete state is compared to the employment of the partial trace in order to consider the discrete component only, we show that the entanglement entropy distinctively represents the discrete part. Hence, the known entanglement capabilities of the SGE allow us to support the use of both techniques. Therefore, these measures give a detailed description of the entanglement creation in this famous experiment.
Measurement-driven sequential random sample consensus GM-PHD filter for ballistic target tracking

In this paper, a new filter named measurement-driven sequential random sample consensus Gaussian mixture probability hypothesis density (MD-S-RANSAC-GM-PHD) filter is proposed for estimating the trajectory of a ballistic target during its coast phase. Unlike the traditional multiple-target tracking (MTT) algorithms that require data association, the proposed method involves modelling the respective collections of targets and measurements as random finite sets (RFS) and applying the PHD recursion to propagate the posterior intensity in time. To generate the new birth target intensity adaptively, a measurement-driven birth intensity estimation algorithm is developed. Since the measurement set used for birth intensity estimation may contain a large amount of clutter, a measurement set pre-processing method based on density-based spatial clustering and sequential random sample consensus (S-RANSAC) algorithm is proposed to eliminate the interference of clutter on generating new target birth intensity. Specifically, the proposed filter extends the standard GM-PHD filter by distinguishing between the persistent and the newborn target, and the extended Kalman filter (EKF) implementation of our proposed filter for ballistic target tracking is also derived. Simulation results illustrate the advantages of our proposed filter in tracking ballistic missile.
SRGAN with Total Variation Loss in Face Super-Resolution

Facial image super-resolution is a crucial preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Convolutional neural networks were earlier used to produce high-resolution images that train quicker and shown excellent performance by learning mapping relation using pairs of low-resolution and high-resolution images. However, in some cases, they are incapable of recovering finer details and often generate blurry images. In this paper, we evaluate a method of applying Generative adversarial networks in generating realistic super-resolution images from low-resolution ones by using three typical losses for super-resolution: Content Loss, Adversarial Loss, Perceptual Loss, and proposed to use Total Variation Loss. We try different pre-trained famous Convolutional neural networks models (VGG19, FaceNet, and EfficientNet) in Perceptual Loss to have a general view with different backbones. Our network gains 32.67 of Peak signal-to-noise ratio (PSNR) and 0.89 of Structural similarity index (SSIM) in 100 random samples from the Flickr-Faces-HQ dataset.
Integrated Dual Analysis of Quantitative and Qualitative High-Dimensional Data

The Dual Analysis framework is a powerful enabling technology for the exploration of high dimensional quantitative data by treating data dimensions as first-class objects that can be explored in tandem with data values. In this article, we extend the Dual Analysis framework through the joint treatment of quantitative (numerical) and qualitative (categorical) dimensions. Computing common measures for all dimensions allows us to visualize both quantitative and qualitative dimensions in the same view. This enables a natural joint treatment of mixed data during interactive visual exploration and analysis. Several measures of variation for nominal qualitative data can also be applied to ordinal qualitative and quantitative data. For example, instead of measuring variability from a mean or median, other measures assess inter-data variation or average variation from a mode. In this work, we demonstrate how these measures can be integrated into the Dual Analysis framework to explore and generate hypotheses about high-dimensional mixed data. A medical case study using clinical routine data of patients suffering from Cerebral Small Vessel Disease (CSVD), conducted with a senior neurologist and a medical student, shows that a joint Dual Analysis approach for quantitative and qualitative data can rapidly lead to new insights based on which new hypotheses may be generated.
Hardware-Based FLUSH+RELOAD Attack on Armv8 System via ACP

Cache side-channel attacks have been getting remarked as it threatens the information security of the multitenant systems. Among them, the FLUSH+RELOAD attack is one of the high-accuracy and high-resolution cache side-channel attacks. However, there have been few works targeting Arm architecture due to its architectural differences from x86 processors such as accessibility of cache flush instruction. In this paper, we design hardware that allows unprivileged cache invalidation on Arm processor through ACP. By utilizing the developed IP, we demonstrate a FLUSH+RELOAD attack on T table-based AES in unprivileged user mode. Our suggestion shows better performance than previous studies using cache flush instruction.
Shop-Floor Service Connector - a message-oriented Middleware Focused on the Usability and Infrastructure Requirements of SMEs Developing Smart Services

Digitalization causes an intensive change in the way software components are developed within the shop floor. The blending of technology domains with current automation technologies leads to high integration efforts with a low degree of reusability. This problem is further reinforced by a diverging system understanding within individual domains and the use of heterogeneous development platforms. Existing solutions from the field of cloud computing are applied in the context of the Industrial Internet of Things (IIoT). However, these systems do not fulfill the requirements of user-friendliness, resource demands, performance, and infrastructure, which is why they are only partially suitable for the current needs of small and medium-sized enterprises (SMEs). For this purpose, the communication middleware Shop-Floor-Service-Connector (SFSC) is developed. Based on the broker-less messaging system ZeroMQ, an overlaid SFSC protocol is implemented. Subsequently, an example scenario is used to illustrate the usage of the system. Finally, the next steps for further developing the middleware are presented.
Ontology-Based Modelling of IoT Design Patterns

Due to the large and complex nature of the Internet of Things (IoT), various analysis and design methodologies are proposed. Design pattern is one of them to provide the solution for a design problem, which is recurring in nature. In this approach, we have presented design patterns for the IoT use cases such as service integration and IoT security. But the semi-formal nature of software patterns may lead to bugs in the proposed pattern. Hence, an ontology-based approach is considered for modelling of the IoT design patterns. Further, we have presented an ontology-based framework for the specification and refinement of IoT design patterns. In this approach, an analysis of meta-models and ontologies has also been performed for reducing the gap between high-level design abstraction of Unified Modeling Language (UML)-based IoT patterns and formal ontology.
Sociotechnical Design Opportunities for Pervasive Family Sleep Technologies

Getting the right amount of high quality sleep is crucial for overall health and wellbeing, and pervasive and ubiquitous computing technologies have shown promise for allowing individuals to track and manage their sleep quality. However, sleep technology research has traditionally focused on individual-level solutions. In this paper, we elucidate social requirements for family sleep technologies. We take a family informatics approach to sleep, through an in-home interview study with 10 families with young children. We describe families' current practices, values, and perceived role for technology, showing that sleep technology has many opportunities beyond individual-level tracking. We also provide design dimensions and implications for family-based sleep technologies, especially the potential for technologies that support family activities and rituals, encourage children's independence, and provide comfort.
Self-Supervised Monocular Depth Estimation Scale Recovery using RANSAC Outlier Removal

Recently, self-supervised method has become an increasingly significant branch of depth estimation task, especially in the realm of autonomous driving applications. However, current per-pixel depth maps yielded from RGB images still suffer from uncertain scale factor generated by the nature of monocular image sequences, which further leads to the insufficiency in practical use. In this work, we first analyze such scale uncertainty both theoretically and practically. Then we perform scale recovery utilizing geometric constraint to estimate accurate scale factor, RANSAC(Random sample consensus) outlier removal is introduced into pipeline to obtain accurate ground point extraction. Adequate experiments on KITTI dataset(dataset generated by an autonomous driving platform built up by KIT and TRINA comprising stereo and optical flow image pairs as well as laser data, distributed to train set and test set on account of deep learning), show that, using only camera height prior, our proposed method, though not relying on additional sensors, is able to achieve accurate scale recovery and outperform existing scale recovery methods.
Neural Architecture Search for Convolutional Neural Networks with Attention

The recent development of neural architecture search (NAS) has enabled us to automatically discover architectures of neural networks with high performance within a few days. Convolutional neural networks extract fruitful features by repeatedly applying standard operations (convolutions and poolings). However, these operations also extract useless or even disturbing features. Attention mechanisms enable neural networks to discard information of no interest, having achieved the state-of-the-art performance. While a variety of attentions for CNNs have been proposed, current NAS methods have paid a little attention to them. In this study, we propose a novel NAS method that searches attentions as well as operations. We examined several patterns to arrange attentions and operations, and found that attentions work better when they have their own search space and follow operations. We demonstrate the superior performance of our method in experiments on CIFAR-10, CIFAR-100, and ImageNet datasets. The found architecture achieved lower classification error rates and required fewer parameters compared to those found by current NAS methods.
Stochastic Gradient Descent with Step Cosine Warm Restarts for Pathological Lymph Node Image classification via PET/CT images

Warm restart strategies are widely used in gradient-free optimization to deal with multi-model functions. In this paper, we present a novel warm restart technique by step cosine function in stochastic gradient descent method that used to train a deep convolution neural network. Three variants of step cosine function with MobileNetv2 and ResNet50 network structure are tested in our pathological lymph node PET/CT dataset. Comparing to the step function as the warm restart schedule, the proposed step cosine warm restart strategy could improve the performance of pathological lymph node image classification in terms of accuracy, sensitivity and specificity, which increased at 2.1%, 0.7% and 2.9% with MobileNetv2, and at 1.3%, 1.4% and 1.3% with ResNet50, respectively.
Nonfrontal and Asymmetrical Facial Expression Recognition Through Half-Face Frontalization and Pyramid Fourier Frequency Conversion

Expression recognition in the wild is easily distorted by nonfrontal and asymmetry faces. In nonfrontal faces, some areas are compressed and distorted. Even after frontalization, these compressed areas may still be blurred and distort expression recognition. Additionally, asymmetrical expressions are common on half or local face areas and produce incorrect expression features. Therefore, this paper presents a half-face frontalization and pyramid Fourier frequency conversion method. Despite the location, range and intensity of incorrect expressions in nonfrontal faces being unknown, according to discrete Fourier transform, it can be proven that the frequency band of the correct expression is much larger than that of incorrect expression on the same face. This can be taken advantage of by pyramid frequency conversion, which is designed based on Fourier frequency conversion. It can adjust incorrect expression frequency in multiscales to take them out off the band-pass of the convolution operations of deep learning and be eliminated completely, whereas correct expression information is reserved. Thus, expressions can be recognized effectively.
Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction-Multilayer Attention Mechanism Convolutional Neural Network

Pedestrian reidentification is a key technology in large-scale distributed camera systems. It can quickly and efficiently detect and track target people in large-scale distributed surveillance networks. The existing traditional pedestrian reidentification methods have problems such as low recognition accuracy, low calculation efficiency, and weak adaptive ability. Pedestrian reidentification algorithms based on deep learning have been widely used in the field of pedestrian reidentification due to their strong adaptive ability and high recognition accuracy. However, the pedestrian recognition method based on deep learning has the following problems: first, during the learning process of the deep learning model, the initial value of the convolution kernel is usually randomly assigned, which makes the model learning process easily fall into a local optimum. The second is that the model parameter learning method based on the gradient descent method exhibits gradient dispersion. The third is that the information transfer of pedestrian reidentification sequence images is not considered. In view of these issues, this paper first examines the feature map matrix from the original image through a deconvolution neural network, uses it as a convolution kernel, and then performs layer-by-layer convolution and pooling operations. Then, the second derivative information of the error function is directly obtained without calculating the Hessian matrix, and the momentum coefficient is used to improve the convergence of the backpropagation, thereby suppressing the gradient dispersion phenomenon. At the same time, to solve the problem of information transfer of pedestrian reidentification sequence images, this paper proposes a memory network model based on a multilayer attention mechanism, which uses the network to effectively store image visual information and pedestrian behavior information, respectively. It can solve the problem of information transmission. Based on the above ideas, this paper proposes a pedestrian reidentification algorithm based on deconvolution network feature extraction-multilayer attention mechanism convolutional neural network. Experiments are performed on the related data sets using this algorithm and other major popular human reidentification algorithms. The results show that the pedestrian reidentification method proposed in this paper not only has strong adaptive ability but also has significantly improved average recognition accuracy and rank-1 matching rate compared with other mainstream methods.
Two-point calibration method for a zoom camera with an approximate focal-invariant radial distortion model

A zoom camera can change its focal length and track moving objects with an adjustable resolution. To extract precise geometric information for the tracked objects, a zoom camera requires an accurate calibration method. High-precision camera calibration methods, however, usually require a number of control points that are not guaranteed in some practical situations. Most zoom cameras suffer radial distortion. Athough a traditional method can recover an undistorted image with known intrinsic parameters, it fails to work for a zoom camera with an unknown focal length. Motivated by these problems, we propose a two-point calibration method (TPCM). In this scheme, we first propose an approximate focal-invariant radial distortion (AFRD) model. With the AFRD model, an RGB image can be undistorted with an unknown focal length. After that, the TPCM method is presented to estimate the focal length and rotation matrix with only two control points for one image. Synthetic experiments demonstrate that the AFRD model is efficient. In the real data experiment, the mean reprojection error of the TPCM method is less than one pixel, which is smaller than current state-of-the-art methods, and we believe meets the demand for high-precision calibration.
Parallel Design of Apriori Algorithm Based on the Method of “Determine Infrequent Items &amp; Remove Infrequent Itemsets”

Abstract In the method of fault association rule diagnosis, Apriori algorithm has low efficiency for big data processing. In this paper, aiming at the defects of Apriori algorithm, MapReduce computing framework is used to optimize the Apriori association rule algorithm. This method improves the accuracy of association mining in fault diagnosis. In the process of optimization, this paper proposes the method of “Determine Infrequent Items &amp; Remove Infrequent Itemsets”. Through experiments, this method effectively reduces the computational space needed by Apriori algorithm in association rule mining, and improves the computing speed.
Forward stepwise random forest analysis for experimental designs

In experimental designs, it is usually assumed that the data follow normal distributions and the models have linear structures. In practice, experimenters may encounter different types of responses and be uncertain about model structures. If this is the case, traditional methods, such as the ANOVA and regression, are not suitable for data analysis and model selection. We introduce the random forest analysis, which is a powerful machine learning method capable of analyzing numerical and categorical data with complicated model structures. To perform model selection and factor identification with the random forest method, we propose a forward stepwise algorithm and develop Python and R codes based on minimizing the OOB error. Six examples including simulation and case studies are provided. We compare the performance of the proposed method and some frequently used analysis methods. Results show that the forward stepwise random forest analysis, in general, has a high power for identifying active factors and selects models that have high prediction accuracy.
Detection of Complex Abnormal Ship Behavior Based on Event Stream

In recent years, maritime traffic has increased dramatically. Some ships exhibit abnormal behavior, which may indicate different dangers or threats. The speed and position parameters are often used to detect abnormal ship behavior. However, such methods only detect part of the ship's abnormal behavior, and ignore the events before or after the abnormal behavior. This paper uses event stream processing to detect the complex abnormal behavior of ships. Event stream processing treats one abnormal behavior of a ship as an event, and then determines the complex abnormal behavior through the correlation of multiple events. This method can detect a series of abnormal ship behaviors, which helps to identify illegal behaviors of ships at sea and improve the safety of maritime navigation.