Reviewerly/exHarmony huggingface dataset:
Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.
Prototyping Canvas: Design Tool for Planning Purposeful Prototypes

Abstract While prototypes are critical to the creation of successful products and innovative solutions, building a prototype is characterized by large sunk costs and a plethora of unknowns. The versatility and effectiveness of prototypes paired with the ambiguous nature of developing a prototype can lead to wasted resources. Recent studies support this claim, demonstrating that under certain circumstances, designers often prototype without a clear purpose, building prototypes as a function of the design process rather than as a function of the design. These findings motivated the creation of the Prototyping Canvas, a tool to aid designers in planning for purposeful prototypes by identifying critical assumptions and questions to guide development. Business and engineering design literature influenced the development of the canvas, which was first tested with a client project in the SUTD-MIT International Design Centre (IDC). The feedback and insights from the design team guided revisions to the canvas. The updated canvas was then validated with 55 professionals during a design project sprint. The purpose of this paper is to present the Prototyping Canvas as a valid and effective design tool.
Optimizing honeypot strategies against dynamic lateral movement using partially observable stochastic games

Partially observable stochastic games (POSGs) are a general game-theoretic model for capturing dynamic interactions where players have partial information. The existing algorithms for solving subclasses of POSGs have theoretical guarantees for converging to approximate optimal strategies, however, their scalability is limited and they cannot be directly used to solve games of realistic sizes. In our problem, the attacker uses lateral movement through the network in order to reach a specific host, while the defender wants to discover the attacker by dynamically reallocating honeypots. We demonstrate that restricting to a specific domain allows us to substantially improve existing algorithms: (1) we formulate a compact representation of uncertainty the defender faces, (2) we exploit the incremental strategy-generation method that over iterations expands the possible actions for players. The experimental evaluation shows that our novel algorithms scale several orders of magnitude better compared to the existing state of the art.
Colorizing Near Infrared Images Through a Cyclic Adversarial Approach of Unpaired Samples

This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster, less sensitive to hyper-parameters' selection and generate high quality samples. The obtained results have been quantitatively-using standard evaluation metrics-and qualitatively evaluated showing considerable improvements with respect to the state of the art.
Motion and Depth Augmented Semantic Segmentation for Autonomous Navigation

Motion and depth provide critical information in autonomous driving and they are commonly used for generic object detection. In this paper, we leverage them for improving semantic segmentation. Depth cues can be useful for detecting road as it lies below the horizon line. There is also a strong structural similarity for different instances of different objects including buildings and trees. Motion cues are useful as the scene is highly dynamic with moving objects including vehicles and pedestrians. This work utilizes geometric information modelled by depth maps and motion cues represented by optical flow vectors to improve the pixel-wise segmentation task. A CNN architecture is proposed and the variations regarding the stage at which color, depth, and motion information are fused, e.g. early-fusion or mid-fusion, are systematically investigated. Additionally, we implement a multimodal fusion algorithm to maximize the benefit from all the information. The proposed algorithms are evaluated on Virtual-KITTI and Cityscapes datasets where results demonstrate enhanced performance with depth and flow augmentation.
Cascade Dense-Unet for Prostate Segmentation in MR Images

Automatic prostate segmentation from magnetic resonance images can assist in diagnosis and radiological planning. The extensive clinical application of this task has attracted the attention of researchers. However, due to noise, blurred boundaries and scale variation, it is very challenging to segment prostate from magnetic resonance images. We propose a cascade method for prostate segmentation. The model consists of two stage. In the first stage, a dense-unet model are used to obtain the initial segmentation results. In the second stage, the segmentation result of the first stage is used as prior knowledge, and another dense-unet is used to obtain more accurate segmentation results. The experimental results show that the proposed method can obtain more accurate segmentation results.
Shining a Light on Dark Patterns

Dark patterns are user interfaces whose designers knowingly confuse users, make it difficult for users to express their actual preferences, or manipulate users into taking certain actions. They typically exploit cognitive biases and prompt online consumers to purchase goods and services that they do not want, or to reveal personal information they would prefer not to disclose. Research by computer scientists suggests that dark patterns have proliferated in recent years, but there is no scholarship that examines dark patterns’ effectiveness in bending consumers to their designers’ will. This article provides the first public evidence of the power of dark patterns. It discusses the results of the authors’ large-scale experiment in which a representative sample of American consumers were randomly assigned to a control group, a group that was exposed to mild dark patterns, or a group that was exposed to aggressive dark patterns. All groups were told they had been automatically enrolled in an identity theft protection plan, and the experimental manipulation varied what acts were necessary for consumers to decline the plan. Users in the mild dark pattern condition were more than twice as likely to remain enrolled as those assigned to the control group, and users in the aggressive dark pattern condition were almost four times as likely to remain enrolled in the program. There were two other striking findings. First, whereas aggressive dark patterns generated a powerful backlash among consumers, mild dark patterns did not – suggesting that firms employing them generate substantial profits. Second, less educated subjects were significantly more susceptible to mild dark patterns than their well-educated counterparts. Both findings suggest that there is a particularly powerful case for legal interventions to curtail the use of mild dark patterns.The article concludes by examining legal frameworks for ameliorating the dark patterns problem. Many dark patterns appear to violate federal and state laws restricting the use of unfair and deceptive practices in trade. Moreover, in those instances where consumers enter into contracts after being exposed to dark patterns, their consent could be deemed voidable under contract law principles. The article proposes a quantitative bright-line rule for identifying impermissible dark patterns. Dark patterns are presumably proliferating because firms’ secret and proprietary A-B testing has revealed them to be profit maximizing. We show how similar A-B testing can be used to identify those dark patterns that are so manipulative that they ought to be deemed unlawful.
Analysis of Robust Functions for Registration Algorithms

Registration accuracy is influenced by the presence of outliers and numerous robust solutions have been developed over the years to mitigate their effect. However, without a large scale comparison of solutions to filter outliers, it is becoming tedious to select an appropriate algorithm for a given application. This paper presents a comprehensive analysis of the effects of outlier filters on the Iterative Closest Point (ICP) algorithm aimed at a mobile robotic application. Fourteen of the most common outlier filters (such as M-estimators) have been tested in different types of environments, for a total of more than two million registrations. Furthermore, the influence of tuning parameters has been thoroughly explored. The experimental results show that most outlier filters have a similar performance if they are correctly tuned. Nonetheless, filters such as Var. Trim., Cauchy, and Cauchy MAD are more stable against different environment types. Interestingly, the simple norm L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> produces comparable accuracy, while being parameterless.
Activity recognition in manufacturing: The roles of motion capture and sEMG+inertial wearables in detecting fine vs. gross motion

In safety-critical environments, robots need to reliably recognize human activity to be effective and trust-worthy partners. Since most human activity recognition (HAR) approaches rely on unimodal sensor data (e.g. motion capture or wearable sensors), it is unclear how the relationship between the sensor modality and motion granularity (e.g. gross or fine) of the activities impacts classification accuracy. To our knowledge, we are the first to investigate the efficacy of using motion capture as compared to wearable sensor data for recognizing human motion in manufacturing settings. We introduce the UCSD-MIT Human Motion dataset, composed of two assembly tasks that entail either gross or fine-grained motion. For both tasks, we compared the accuracy of a Vicon motion capture system to a Myo armband using three widely used HAR algorithms. We found that motion capture yielded higher accuracy than the wearable sensor for gross motion recognition (up to 36.95%), while the wearable sensor yielded higher accuracy for fine-grained motion (up to 28.06%). These results suggest that these sensor modalities are complementary, and that robots may benefit from systems that utilize multiple modalities to simultaneously, but independently, detect gross and fine-grained motion. Our findings will help guide researchers in numerous fields of robotics including learning from demonstration and grasping to effectively choose sensor modalities that are most suitable for their applications.
Chaotic Encryption Algorithm With Key Controlled Neural Networks for Intelligent Transportation Systems

The security of sensitive information is vital in many aspects of multimedia applications such as Intelligent Transportation Systems (ITSs), where traffic data collection, analysis and manipulations is essential. In ITS, the images captured by roadside units form the basis of many traffic rerouting and management techniques, and hence, we should take all precautions necessary to deter unwanted traffic actions caused by malicious adversaries. Moreover, the collected traffic images might reveal critical private information. Consequently, this paper presents a new image encryption algorithm, denoted as ChaosNet, using chaotic key controlled neural networks for integration with the roadside units of ITSs. The encryption algorithm is based on the Lorenz chaotic system and the novel key controlled finite field neural network. The obtained cryptanalysis show that the proposed encryption scheme has substantial mixing properties, and thus cryptographic strength with up to 5% increase in information entropy compared to other algorithms. Moreover, it offers consistent resistance to common attacks demonstrated by nearly ideal number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), pixel correlation coefficient values, and robustness to cropped attacks. Furthermore, it has less than 0.002% difference in the NPCR and 0.3% in the UACI metrics for different test images.
Geologist-level wireline log shape identification with recurrent neural networks

The identification of wireline log shapes is of great significance for research in paleogeography and sedimentology, and further for natural resources characterization. Generally, identification of log shapes is manually achieved by geologists through careful observation of the morphological features of log curves. This process is subjective and tedious, and it may introduce artefacts depending on the bias of the interpreters. Therefore, we developed a model which exceeds average performance of experienced geologists in identifying basic log shapes. The excellence of the model mainly results from a large annotated dataset and a specially designed Recurrent Neural Network (RNN) architecture. The dataset contains 2676 Spontaneous Potential (SP) log segments regarding four basic log shapes (bell, funnel, egg and cylinder shapes). There are six layers in the model's architecture, and the core is a layer of Long Short Term Memory (LSTM) units which are designed to extract the morphological features of the log curve. A committee of four experienced geologists annotated a gold standard testing dataset, based on which different approaches can be compared in regard to their accuracies. We also tried standard artificial neural network (ANN), Support Vector Machine (SVM) and an analytical method (Trendline Matching) for this problem. It turns out the proposed RNN model is more accurate than geologists' manual labelling and also the above three methods. The research for this problem is still at the initial stage and expecting more work and ideas to further improve the robustness of the model. Therefore, we also put forward some ideas for improving the model and enlarging the dataset.
Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

Summary An efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.
Influence of the educational environment on students’ managerial competence

This paper considers university educational environment to be an important factor in the development of students’ organizational and managerial competence. The authors discuss the approaches to the educational environment of the university, analyze its parameters and characteristics, and study the connection between the developmental educational environment and a high level of managerial competence attained by technical university graduates. The authors rely on the environmental approach which allows them to assess the impact of the educational environment on students’ organizational and managerial competence taking into account the analysis of empirical data obtained by monitoring methods of sociological research. This method also made it possible to improve self-organization and self-management skills of students, and to achieve a high level of resilience both on the environmental and personal level.
Usable security for QR code

QR codes are widely used in various settings such as consumer advertising, commercial tracking, ticketing and marketing. People tend to scan QR codes and trust their content, but there exists no standard mechanism for providing authenticity and confidentiality of the code content. Attacks such as the redirection to a malicious website or the infection of a smartphone with a malware are realistic and feasible in practice. In this paper, we present the first systematic study of usable state-of-the-art cryptographic primitives inside QR codes. We select standard, popular cryptographic schemes and we compare them based on performance, size and security. We conduct tests that show how different usability factors impact on the QR code scanning performance and we evaluate the usability/security trade-off of the considered cryptographic schemes. Interestingly, we find out that in some cases security breaks usability and we provide recommendations for the choice of secure and usable cryptographic schemes.
A physiological and behavioral feature authentication scheme for medical cloud based on fuzzy-rough core vector machine

The medical cloud brings in connivance to share medical data within the same hospital or between different hospitals. However, sharing medical data on the cloud faces challenges such as (1) the medical cloud platform is vulnerable to cyber attacks, (2) medical data can be illegally accessed by the doctor without patient’s authorization, and (3) unauthorized access from an attacker. Currently, common security authentication schemes generally use fingerprint or face for identification, but these biometric data are easily copied and reused. At present, widely used biometric authentications such as fingerprint and faceID are easy to be copied and reused. The latest GAN algorithm has broken into almost all face recognition systems, the success proportion of the attack reached 95%. Therefore, a secure login authentication scheme is needed. In this paper, we propose a physiological and behavioral feature authentication scheme based on fuzzy-rough theory to limit the access right of medical devices. Such a scheme requires the doctor’s own gesture for the authorization to access the medical device. Fuzzy-rough core vector machine (FRCVM) approach is adopted in our scheme to achieve high classification accuracy and efficiency. The results have shown that our solutions are highly secure and practical. To secure the cloud platform and ensure only the authorized doctors can access the patient data, we have designed an efficient data sharing solution that enables medical data stored in the cloud to be hierarchically authorized for patient access. The solution exploits proxy re-encryption to protect patient-centric medical data sharing.
“Seeing” Data Like an Expert: An Eye-Tracking Study Using Graphical Data Representations

Given the centrality of data visualizations in communicating scientific information, increased emphasis has been placed on the development of students' graph literacy-the ability to generate and interpret data representations-to foster understanding of domain-specific knowledge and the successful navigation of everyday life. Despite prior literature that identifies student difficulties and methods to improve graphing competencies, there is little understanding as to how learners develop these skills. To gain a better resolution of the cognitive basis by which individuals "see" graphs, this study uses eye tracking (ET) to compare the strategies of non-science undergraduates (n = 9), early (n = 7) and advanced (n = 8) biology undergraduates, graduate students (n = 6), and science faculty (n = 6) in making sense of data displays. Results highlight variation in how individuals direct their attention (i.e., fixations and visual search patterns) when completing graph-based tasks as a function of science expertise. As research on the transition from novice to expert is crucially important in understanding how we might design curricula that help novices move toward more expert-like performance, this study has implications for the advancement of new strategies to aid the teaching and learning of data analysis skills.
A Supervised Approach to Predicting Noise in Depth Images

Modern robotic systems are very complex and need to be tested in simulations with detailed sensor noise models to effectively verify robotic behavior. Depth imagery in particular comes with significant noise in the form of scene-dependent pixel-wise dropouts and distortions. Unfortunately, many depth camera simulations contain limited noise models, or can only support generating realistic depth images of simple scenes, which limits their usefulness in effectively testing perception algorithms. We propose a data driven approach to generate more realistic noise for complex simulated environments by using a convolutional neural network (CNN) to predict which pixels of a simulated noise-free depth image will not have returns (no-depth-return pixels, or NDP). We choose to focus on NDP here, as these dropouts are the most common and dramatic form of depth image noise. To train this network, we use reconstructed real-world scenes from the Label Fusion dataset to provide ground truth depth for each noisy depth image used to scan the scene. We use the resulting noise-free and noisy depth image pairs as labeled examples and train the network to predict which pixels of the noise-free image will be NDP. When used to post-process a simulation of a depth sensor, this system produces realistic depth images, even in cluttered scenes. To demonstrate that our approach successfully closes the reality gap for depth imagery, we show that the popular ICP algorithm for object pose estimation fails more realistically on our CNN-corrupted simulated depth images than on uncorrupted depth images and unsupervised domain adaptation baselines.