Reviewerly/exHarmony huggingface dataset:
Activity Attribute-Based User Behavior Model for Continuous User Authentication

In general, authentication using a username and password has been used to manage access to the system. This way, it can be arranged so that only authorized people can access a system. However, it does not rule out the possibility of several spoofing attacks. User behavior can be used instead of a password to solve this problem. Given that user behavior is unique, the behavior can be used for the authentication process. One of the authentication processes is to continuously use weblogs, as proposed by Leslie et al. However, the method used by Leslie et al. still has a drawback with the Leslie method. Only the user's role is identified. So, the attacker's identity cannot be known if an attack occurs. A continuous authentication method using weblogs and user attributes such as user id, DateTime, event, category, and products is used to overcome this problem. Using this method, the user's identity can be identified immediately after the role is known. In addition, using the proposed method, the accuracy of user identity authentication obtained is, on average, 95.08%, which means it is greater than the accuracy of user role authentication using the Leslie method, which is 46%..
Maximum Correntropy Criterion Kalman Filter for Indoor Quadrotor Navigation under Intermittent Measurements

We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements.
Omnidirectional Depth Estimation With Hierarchical Deep Network for Multi-Fisheye Navigation Systems

Multi-fisheye System has the advantages of sufficient overlap and the ability to capture a complete 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> scene, which is beneficial for the omnidirectional depth estimation task. However, due to the severe distortion of the fisheye images, it is hard for such systems to extract and match features to predict an accurate depth. In this work, on the basis of a multi-fisheye system, we present a novel end-to-end deep learning architecture for omnidirectional depth estimation: 1) to capture the reliable features of the distorted fisheye image, a multi-scale feature extraction and aggregation module is improved, which can adaptively obtain the global context information to represent the features; 2) to leverage more aligned features, especially those in the overlap between multi-fisheye images, we construct a fusion cost volume to combine similarity and semantic information, which can enhance the feature discriminability; and 3) to refine the omnidirectional depth map efficiently, a cascaded cost regularization architecture is proposed. Instead of several costly 3D convolutions, the 3D BSConv based on intra-kernel correlations is introduced to regularize the cost. The proposed method can incrementally predict the depth map from coarse to fine, and reduce the network computational complexity significantly. The experiments in several public indoor and outdoor synthetic datasets demonstrate that the proposed method outperforms some state-of-the-art methods in terms of a synthesis of accuracy and speed, with fewer model parameters.
Cybersecurity Risk in Digitalization of Infrastructure Systems: A Use Case

The increasing number and effectiveness of cyber-attacks and data breaches adversely affect infrastructure systems by exploiting their vulnerabilities. Cyber-attacks on critical infrastructure sometimes undermine safe operations or cause complete shutdowns. Therefore, identifying and evaluating the actual status of infrastructure system processes for critical assets in advance requires analyzing the actual cybersecurity status to protect them from potential cyber-attacks and data breaches. So far, the information deficit about cybersecurity awareness in the infrastructure systems sector exists. International studies are more broadly designed, but not directly focused on gaining detailed knowledge about cybersecurity defense's status quo in the infrastructure system sector. Therefore, strategic steps are required to secure the infrastructure systems against cyber-attacks and vulnerabilities. This includes developing and implementing procedures to improve cyber-attack detection and eliminate system vulnerabilities. In this regard, a maturity level model as a specific analysis method of the actual cybersecurity status of the infrastructure system is introduced to gain knowledge about how to achieve the desired to-be-cybersecurity status against the actual status, as a best practice example is provided for a rail system. This paper describes the cybersecurity risk of digitization in the railway sector as a use case.
Design Gaps in Configurable Systems: Adaptability of DHIS2 to other Domains

Highly specialised domains demand highly granularised and variability of configuration elements to support multiple domain configurations at that level. Configurable systems that have limited configurational elements are bound by their elements to be adaptable to a select few domains and contexts. In this study, we propose design changes and enhancements to the DHIS2 configuration layer and resulting data collection tools to support its adaptability in multiple domains. We suggest that the process of adaptation should not involve transference of initial domain specific UI properties to other domains. Continued evolution of the platform at the feature level is required to make it more adaptable to other domains. Provision of a variety of configuration elements enables implementation level designers to explore, provide and meet a set of usability design configuration needs and goals in respective use cases.
Re-Parameterized Real-Time Stereo Matching Network Based on Mixed Cost Volumes Toward Autonomous Driving

3D perception is an essential capability of autonomous vehicles. Most state-of-the-art stereo matching networks pursue higher prediction accuracy at the cost of the inference speed. However, high demand on computational resource pushes the cost of hardware, hindering practical applications of stereo matching. In this paper, we propose RMCNet, a novel re-parameterized coarse-to-fine network for stereo matching. RMCNet achieves real time on edge devices while outputs accurate disparity maps. To reduce the computing complexity of 3D convolution in cost aggregation, we propose Mixed Cost Volumes that consist of 4D and 3D cost volumes, enabling efficient optimization of the initial disparity map for real-time inference. An Efficient Feature Matching module is proposed to provide absolute disparity candidates for 3D cost volume. To increase the reliability of 4D and 3D cost volumes, we propose a simple but efficient 3D cost aggregation module and a pyramid 2D cost aggregation module for the aggregation of Mixed Cost Volumes. Moreover, we propose a Re-parameterized Channel Sensing module and a Re-parameterized Disparity-Aware module to replace 2D and 3D convolutional blocks with residual connections. Being evaluated on KITTI 2012 and KITTI 2015, the comprehensive performance of the proposed network is improved by the re-parameterization approach. Outstanding trade-off between accuracy and speed on edge devices over existing SOTA works is achieved. With fast average inference speed on NVIDIA Jetson Nano of 33.74 FPS, the accuracy in terms of 3px-all on KITTI 2012 test set and D1-all on KITTI 2015 test set are 3.01% and 3.41%, respectively.
Augmented Reality App for Science Pre-university Students

With the spread of the COVID-19 virus, schools and universities are forced to close their campuses and find an online mode of teaching. A lot of EdTech companies started digitizing the syllabus but this only changes the mode of teaching, not the problems faced by students. Learning should be interactive where students get more hands-on experience with study materials, especially in Science subjects. Student engagement plays an important role in learning as the subject includes hypotheses about how phenomena work and experimentation to test under controlled conditions. Students always complain about the difficulties they face in understanding complex biological diagrams or the molecular structure of organic compounds. Another major problem faced in conducting experiments in the laboratory as some schools do not have the proper infrastructure or equipment required for the experiment, especially with pre-university students (11th and 12th). This paper focuses on the challenges faced by pre-university Science students in both theories and conducting experiments in the laboratory and how to make subject learning more interactive, engaging, and self-learning for the students. The study considers all four elements (pedagogy, technology, governance and process, and feedback) as a base model for the learning life cycle. The research is conducted with pre-university students and focuses on Science subjects including theory and experiment based on CBSE-based curriculum of Biology, Chemistry, and Physics only. The study proposes a solution in the form of an augmented reality app that allows students the freedom to experiment and manipulate complex structures through 3D modeling for better understanding.
Multi-state feature optimization of sign glosses for continuous sign language recognition

Vision-based Continuous Sign Language Recognition (CSLR) is a challenging and weakly supervised task aimed at segmenting sign language from weakly annotated image stream sequences for recognition. Compared with Isolated Sign Language Recognition (ISLR), the biggest challenge of this work is that the image stream sequences have ambiguous time boundaries. Recent CSLR works have shown that the visual-level sign language recognition task focuses on image stream feature extraction and feature alignment, and overfitting is the most critical problem in the CSLR training process. After investigating the advanced CSLR models in recent years, we have identified that the key to this study is the adequate training of the feature extractor. Therefore, this paper proposes a CSLR model with Multi-state Feature Optimization (MFO), which is based on Fully Convolutional Network (FCN) and Connectionist Temporal Classification (CTC). The MFO mechanism supervises the multiple states of each Sign Gloss in the modeling process and provides more refined labels for training the CTC decoder, which can effectively solve the overfitting problem caused by training, while also significantly reducing the training cost in time. We validate the MFO method on the popular CSLR dataset and demonstrate that the model has better performance.
Robust hyperparameter tuned deep Elman neural network for the diagnosis of osteosarcoma on histology images

Osteosarcomas are a type of bone tumour that can develop anywhere in the bone but most typically do so around the metaphyseal growth plates at the ends of long bones. Death rates can be lowered by early detection. Manual osteosarcoma identification can be difficult and requires specialised knowledge. With the aid of contemporary technology, medical photographs may now be automatically analysed and categorised, enabling quicker and more effective data processing. This paper proposes a novel hyperparameter-tuned deep learning (DL) approach for predicting osteosarcoma on histology images with effective feature selection mechanism which aims to improve the prediction accuracy of the classification system for bone tumor detection. The proposed system mainly consists of ‘6’ phases: data collection, preprocessing, segmentation, feature extraction, feature selection, and classification. Firstly, the dataset of histology images is gathered from openly available sources. Then Median Filtering (MEF) is utilized as the preprocessing step that enhances the quality of the input images for accurate prediction by eliminating unwanted information from them. Afterwards, the pre-processed image was segmented using Harmonic Mean-based Otsu Thresholding (HMOTH) approach to obtain the tumor-affected regions from the pre-processed data. Then the features from the segmented tumor portions are extracted using the Self-Attention Mechanism-based MobileNet (SAMMNet) model. A Van der Corput sequence and Adaptive Inertia Weight included Reptile Search Optimization Algorithm (VARSOA) is used to select the more relevant features from the extracted features. Finally, a Hyperparameter-Tuned Deep Elman Neural Network (HTDENN) is utilized to diagnose and classify osteosarcoma, in which the hyperparameters of the neural network are obtained optimally using the VARSOA. The proposed HTDENN attains the higher accuracy of 0.9531 for the maximum of 200 epochs, whereas the existing DENN, MLP, RF, and SVM attains the accuracies of 0.9492, 0.9427, 0.9413, and 0.9387. Likewise, the proposed model attains the better results for precision (0.9511), f-measure (0.9423), sensitivity (0.9345) and specificity (0.9711) than the existing approaches for the maximum of 200 epochs. Simulation outcomes proved that the proposed model outperforms existing research frameworks for osteosarcoma prediction and classification.
On using the Task Models for Validation and Evolution of Usable Security Design Patterns

Security and usability are two interrelated characteristics. Management of the instances where security and usability are in conflict is often a tough task, which is reliant on the skills of the developers and designers. The paper attempts to support the developers and designers in the management of the conflicts by proposing a task model-based approach that enables them to make informed choices concerning the management of the conflicts in the products they develop, thereby enabling them (1) to validate the efficacy of the design patterns before applying them, and (2) to identify limitations in the existing design patterns and propose its evolution. The paper also presents the findings from a case study where an existing usable security pattern was subjected to the proposed task model-based approach.
Cross-Modality Person Re-Identification Algorithm Based on Two-Branch Network

Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible modality is not suitable for low-light environments or at night, when crime is frequent. In contrast, infrared images can reflect the nighttime environment, and most surveillance systems are equipped with dual-mode cameras that can automatically switch between visible and infrared modalities based on light conditions. In contrast to visible-light cameras, infrared (IR) cameras can still capture enough information from the scene in those dark environments. Therefore, the problem of visible-infrared cross-modality person re-identification (VI-ReID) is proposed. To improve the identification rate of cross-modality person re-identification, a cross-modality person re-identification method based on a two-branch network is proposed. Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. The effectiveness of the proposed method is verified by extensive experimental analysis, and the method achieves good recognition accuracy on the visible-infrared image person re-identification standard dataset SYSU-MM01.
On the perception of graph layouts

Abstract In the field of software engineering, graph‐based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.
Multirate Interlaced Kalman Filter

Large systems are typically partitioned into many subsystems to reduce computational load. For this reason, the Interlaced Extended Kalman Filter (IEKF) was created, in which each subsystem estimates only its own state while utilizing information from other subsystems. The information shared is normally the a-priori and a-posteriori state, as well as the a-priori and a-posteriori covariance matrix.Subsystems, however, cannot, for technological reasons, always operate at the same rate. To address this issue, we propose a multirate distributed filter, in which the subsystems operate independently and only share information when a novel measurement activates each subsystem. The only information exchanged is the a-posteriori state and covariance matrix. In the paper, we demonstrate that the proposed filtering technique is accurate and effective by examining the convergence property.A water tank case study is detailed, and two subsystems with different but fixed rates are discussed, illustrating the efficiency of the proposed solution. The same approach can be modified to take into account numerous instances of subsystems as well as missing data due to an unreliable communication route.
Note Taking in VR: The Forearm Keyboard

This work presents and evaluates a forearm keyboard that allows users to enter textual data using a natural full-handed typing mechanism for virtual reality head-mounted display environments. Should the issues noted with the keyboard during the study be solved, the keyboard would compare favourably with others seen in the literature.
Tritention U-Net: A Modified U-Net Architecture for Lung Tumor Segmentation

Lung tumor segmentation in computed tomography (CT) images is a critical task in medical image analysis. It aids in the early detection and diagnosis of lung cancer, which is one of the primary causes of cancer deaths around the world. However, because of the variable sizes, uncertain shapes of lung nodules, and complex internal lung structure, lung tumor segmentation is a difficult problem. In this study, we propose a novel Tritention U-Net as an efficient model for solving that problem. It is integrated with the Tritention Gate on the contracting path between the encoder and decoder to highlight task-relevant salient features. The proposed Tritention U-Net model is trained and evaluated using the Medical Image Decathlon dataset - Task06_Lung, which requires the model to segment a small portion of a lung image. Our model achieved a Dice score of 91.80% and was compared to well-known models to demonstrate the improvement.
SIMULATION TECHNIQUE IN DETERMINING STUDENT ATTENDANCE USING THE MONTE CARLO METHOD

In lectures, attendance is one of the assessment points that plays an important role in determining a student's graduation. The attendance prediction simulation is an estimate of the calculation of student attendance in lectures. This type of research is quantitative research using data collection techniques by means of observation and documentation study. In the process of analysis, the observed data were attendance data of 5th semester computer engineering study program students and a sample of 40 people as research subjects. The stages of the monte carlo simulation are used: Determining variable frequency; Calculating cumulative probabilities; Determine random number intervals; Create a simulation to determine student attendance; Generate random numbers; Make a simulation of the experimental circuit. Based on a series of experimental data that has been The simulation results obtained predicted attendance and absence of computer engineering study program students at the STMIK Agamua Wamena campus from November 7 to December 19, 2022 with an average attendance of above 50%. Keywords— Attendance, Simulation, Monte carlo, Prediction
Algebraic tunings

We propose an approach to tuning systems in which octave doubling ratio is replaced by a suitable algebraic unit τ, and note frequencies are proportional to a subset of the ring Z[τ]. Then it is possible for many difference tones between notes in the tuning to also appear in the tuning. After outlining more general principles, we consider in detail some natural examples based on the golden ratio ϕ=(1+5)/2, limited by norm or by the number of digits in the greedy β-expansion. We discuss additive and multiplicative properties, implementation and composition using these tunings. The Online Supplement contains MIDI and websynths files to implement the tuning Sβ5(ϕ) (based on β-expansions to ϕ−5) on websynths.com and a composition Three Places.
Polish Adaptation of the Cybersickness Susceptibility Questionnaire (CSSQ-PL)

Abstract We present the Polish adaptation of the Cybersickness Susceptibility Questionnaire (CSSQ) by Freiwald et al. [4] and its short version. To validate the scale, we analyzed the results of 85 participants of the VR artistic experience “Nightsss” in versions with 3 and 6 degrees of freedom. Both long and short versions are characterized by high internal consistency. The validity was analyzed by evaluating the difference between the scores obtained in the CSSQ-PL and the level of experienced cybersickness measured with the Simulator Sickness Questionnaire. The scale is suitable for screening participants in research conducted with the use of immersive virtual reality (VR). Further studies are necessary to confirm the predictive power of the scale.