Miracle-dz/UniFAR-FTUs huggingface dataset:
Cortical Activity at Baseline and During Light Stimulation in Patients With Strabismus and Amblyopia

This research measures and compares the cortical activity at baseline and during light stimulation (LS) in patients with strabismus and amblyopia (SA) and healthy controls (HCs), to understand the differences in its functionality and propose LS as a potential brain stimulator. This observational, longitudinal, prospective study enrolled 17 SA patients and 17 HCs from Querétaro, México. Electroencephalography (EEG) and digital brain mapping (DBM) were used to identify changes in frequency, voltage, and brain coherence. A total of 68 DBM was analyzed for this purpose. Our results indicate that at baseline, patients with strabismus and amblyopia present: i) lower frequency of alpha-wave activity (<inline-formula> <tex-math notation="LaTeX">$p=0.029$ </tex-math></inline-formula>) with an abnormal distribution within hemispheres, ii) theta-wave with a predominance in the frontal lobes, which relates these visual conditions to neurodevelopmental disorders, iii) higher values of low voltage (<inline-formula> <tex-math notation="LaTeX">$p < 0.001$ </tex-math></inline-formula>) and lower values of and high voltage (<inline-formula> <tex-math notation="LaTeX">$p=0.001$ </tex-math></inline-formula>) iv) interhemispheric asynchronicity with a predominance in the left hemisphere. On the other hand, the administration of LS modulates the brain activity of SA patients by i) modifying high and low voltages (<inline-formula> <tex-math notation="LaTeX">$p < 0.001$ </tex-math></inline-formula> and 0.022 respectively), which define the anteroposterior gradient ii) eliminating theta-waves, iii) distributing alpha-wave activity towards the occipital lobes iv) bringing synchronicity between hemispheres. For the HC group, LS alters the distribution of alpha waves within hemispheres, and the state of interhemispheric synchronicity. There were no statistically significant changes in the frequency of the alpha-wave or the anteroposterior gradient. To summarize, LS provokes a state of malleability in the brain of SA patients, by increasing the cortical connectivity, enhancing neural activation and bringing to balance the interhemispheric communication, which converts it into a potential brain stimulator that should be considered as a complementary therapy in the treatment of these patients.
Edge-based Visual Odometry with Stereo Cameras using Multiple Oriented Quadtrees

We propose an efficient edge-based stereo visual odometry (VO) using multiple quadtrees created according to image gradient orientations. To characterize edges, we classify them into eight orientation groups according to their image gradient directions. Using the edge groups, we construct eight quadtrees and set overlapping areas belonging to adjacent quadtrees for robust and efficient matching. For further acceleration, previously visited tree nodes are stored and reused at the next iteration to warm-start. We propose an edge culling method to extract prominent edgelets and prune redundant edges. The camera motion is estimated by minimizing point-to-edge distances within a re-weighted iterative closest points (ICP) framework, and simultaneously, 3-D structures are recovered by static and temporal stereo settings. To analyze the effects of the proposed methods, we conduct extensive simulations with various settings. Quantitative results on public datasets confirm that our approach has competitive performance with state-of-the-art stereo methods. In addition, we demonstrate the practical values of our system in author-collected modern building scenes with curved edges only.
Empirical Study on Multi Convolutional Layer-based Convolutional Neural Network Classifier for Plant Leaf Disease Detection

Recognizing the plant disease automatically in real-time by examining a plant leaf image is highly essential for farmers. This work focuses on an empirical study on Multi Convolutional Layer-based Convolutional Neural Network (MCLCNN) classifier to measure the detection efficacy of MCLCNN on recognizing plant leaf image as being healthy or diseased. To achieve this, a set of experiments were conducted with three distinct plant leaf datasets. Each of the experiments were conducted by setting kernel size of $3\times 3$ and each experiment was conducted independently with different epochs i.e., 50, 75, 100, 125, and 150. The MCLCNN classifier achieved minimum accuracy of 87.47% with 50 epochs and maximum accuracy of 99.25% with 150 epochs for the Peach plant leaves.
An Over-Segmentation-Based Uphill Clustering Method for Individual Trees Extraction in Urban Street Areas From MLS Data

In this article, an over-segmentation-based uphill clustering method for individual extraction of urban street trees from mobile laser scanning data is proposed to solve the problem that the existing methods depend heavily on tree trunks and have poor extraction results in complex environments where the tree trunks are blocked by cars and green belts, and the crown touching or interlocking is large. First, supervoxels are generated by over-segmentation, so that the amount of original data is reduced and the boundaries of different objects are effectively preserved. Then, the potential tree crowns and trunks are obtained by extracting typical object structures. Finally, individual trees extraction is realized by extracting independent crowns from the potential crowns via uphill clustering and searching corresponding trunks from the potential trunks. The main contribution of this article is to propose an individual extraction method for street trees based on uphill clustering that does not rely on the extraction of tree trunks, which improves the completeness of extracted results in complex urban environments. The experimental results demonstrate that the proposed method effectively extracted the street trees individually from the test data, with the completeness of 100%, the correctness of 96.4%, and the F-score of 0.98. Moreover, the proposed method also achieves good result for the extraction of greening trees that are heavily blocked in the green belt areas. And the corresponding completeness, correctness, and the F-score are 94.6%, 83.3%, and 0.89, respectively.
Detection of Hate Tweets using Machine Learning and Deep Learning

Cyberbullying has become a highly problematic occurrence due to its potential of anonymity and its ease for others to join in the harassment of victims. The distancing effect that technological devices have, has led to cyberbullies say and do harsher things compared to what is typical in a traditional face-to-face bullying situation. Given the great importance of the problem, detection is becoming a key area of cyberbullying research. Therefore, it is highly necessary for a framework to accurately detect new cyberbullying instances automatically. To review the machine learning and deep learning approaches, two datasets were used. The first dataset was provided by the University of Maryland consisting of over 30,000 tweets, whereas the second dataset was based on the article ‘Automated Hate Speech Detection and the Problem of Offensive Language’ by Davidson et al., containing roughly 25,000 tweets. The paper explores machine learning approaches using word embeddings such as DBOW (Distributed Bag of Words) and DMM (Distributed Memory Mean) and the performance of Word2vec Convolutional Neural Networks (CNNs) to classify online hate.
Design and Modeling of a Parallel Shifted-Routing Cable-Driven Continuum Manipulator for Endometrial Regeneration Surgery

Endometrial regeneration surgery is a new therapy for intrauterine adhesion (IUA). However, existing instruments lacking dexterity and compliance are with difficulty to successfully perform the tasks of generating transplant wounds and transplanting stem cells during endometrial regeneration surgery. This paper presents a novel shifted-routing continuum manipulator which is driven by only two cables but has high dexterity, simple structure and small size. The design of the continuum manipulator with novel actuation strategy is introduced and the manipulator’s kinematic model is also derived. The analysis and simulation imply that shifted-routing strategy improves the dexterity of manipulators under limited actuation numbers and enhances the ability of reaching targets on fundus and corpus of the uterus. Finally, the shifted-routing continuum manipulator is used to reach targets in a planner endometrium model. The experimental results show that the tip of the manipulator can reach all the area of endometrium from proper directions.
Unmasking Windows Advanced Persistent Threat Execution

The advanced persistent threat (APT) landscape has been studied without quantifiable data, for which indicators of compromise (IoC) may be uniformly analyzed, replicated, or used to support security mechanisms. This work culminates extensive academic and industry APT analysis, not as an incremental step in existing approaches to APT detection, but as a new benchmark of APT related opportunity. We collect 15,259 APT IoC hashes, retrieving subsequent sandbox execution logs across 41 different file types. This work forms an initial focus on Windows-based threat detection. We present a novel Windows APT executable (APT-EXE) dataset, made available to the research community. Manual and statistical analysis of the APT-EXE dataset is conducted, along with supporting feature analysis. We draw upon repeat and common APT paths access, file types, and operations within the APT-EXE dataset to generalize APT execution footprints. A baseline case analysis successfully identifies a majority of 117 of 152 live APT samples from campaigns across 2018 and 2019.
Enhanced tracking wall: A real-time computing method for needle injection on haptic simulators

Haptic simulators can help medical students to train and improve their skills before practicing with a real patient. However, the vast majority of needle insertion haptic simulators are based on sophisticated models that are accurate but highly demanding in computing resources. Most of them do not provide haptic feedback and/or are not suitable for haptic control due to their computing time. In this paper, we presented a new low computing consuming method that aims to provide a realistic needle insertion experience to the student. A description of the proposed solution is provided, and it is illustrated by experimental results to highlight its performance.
Modelling Financially Motivated Cyber Attacks on Electricity Markets Using Mixed Integer Linear Programming

With the vast expansion of grid network and enhancement of Communication system, Cyber security reinforcement is of paramount importance for reliable and secure power system operation. Cyber-attacks on electricity markets acquire financial profits to the adversary. In this paper a modest attempt is made to model the cyber attacker's objective of profit maximization by injecting false data into Day-ahead and Real Time electricity markets. It is considered that attacker runs a bi-level optimization problem which includes attacker's profit maximization objective and market clearing problem for finding out the optimal attack measurements. While manipulating the measuring devices like RTUs, the attacker takes care to avoid being detected by the bad data detection (BDD) procedure run by the ISO. This paper focuses on financially motivated FDI attacks considering attacker as one of the virtual players in electricity market. A novel attacking model is designed using bi-level optimization problem where attacker aims to gain financial benefits by misleading market clearing problem. Potential impact of financially motivated False Data Injection (FDI) attacks on electricity markets is presented by considering PJM 5-bus system. The simulation results show the sharp impact on Locational Marginal Prices (LMPs) in fulfilling the attacker's objective, and the distinct relationship between LMPs and the market clearing prices during the attack.
Path planning for mobile manipulator robots under non-holonomic and task constraints

This paper presents a path planner, which enables a nonholonomic mobile manipulator to move its end-effector on an observed surface with a constrained orientation, given start and destination points. A partial point cloud of the environment is captured using a vision-based sensor, but no prior knowledge of the surface shape is assumed. We consider the multi-objective optimisation problem of finding robot paths which account for the nonholonomic constraints of the base, maximise the robot’s manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g. demolition of legacy nuclear plants, where dismantling does not require a precise path. We show how our approach embeds the nonholonomic constraints of the mobile platform into an extended Jacobian, while additionally encoding the constraint that the end-effector must remain in contact with the cut surface throughout the motion. We use this constrained Jacobian to plan a time-series of robot configurations. Additionally, we show how our novel cost function is suitable for combining with a variety of well-known path planners, such as RRT*. We present several empirical experiments in different scenarios, where a simulated non-holonomic mobile manipulator follows a trajectory, which is generated on noisy point clouds derived from real depth-camera images of real objects. Our planner (RRT*-CRMM) enables successful task completion by optimising the path over the travelled distance, the manipulability of the arm, and the movements of the mobile base.
An Improved Q-Learning for System Power Optimization with Temperature, Performance and Energy Constraint Modeling

Power management of embedded systems based on machine learning have drawn more and more attention. High-level software power management and optimization have gradually become important technologies for controlling the computer system power dissipation. In paper, we have employed an improved power optimization management technique which employ Q-learning algorithm based on temperature, performance and energy. The improved Q-learning has been employed to control the uncertain states of the running system and can effectively make decisions to select a rational policy with multiple parameter constraints. As running hardware and application data can be effectively collected and modeled, the power management framework can easily explore an ideal policy by value function of Q-learning algorithm.
A Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet

Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.
Multiview Ghost-Free Image Enhancement for In-the-Wild Images With Unknown Exposure and Geometry

The multiview low dynamic range images captured with sparse camera arrangement under ill-lighting conditions contain highlighted and shadow regions due to over-exposed and under-exposed regions. The processing of these images produces contrast distortion, and it is challenging to maintain relative brightness with color consistency. Moreover, the disparity map estimation faces the challenges of holes and artifacts due to a wide baseline and poor visibility, with a shared view of vision. In this article, we propose a multiview ghost-free image enhancement strategy for in-the-wild images with unknown exposure and geometry. We address the complex geometric alignment problem for a wide variational baseline among multiple sparsely arranged cameras. The features among multiple viewpoints are detected and matched for the image restoration. The restored image contains highlighted and shadow regions with a color imbalance problem. We synthesize virtual images following the intensity mapping function, which compensates for the relative brightness and color distortions. Finally, we fuse all the images to obtain high-quality images. The proposed method is more frequent and feasible for future multiview systems with varying baselines without relying on disparity maps. The experimental results demonstrate that the proposed method outperformed the state-of-the-art approaches.
Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps

This paper describes a method that corrects errors of a VSLAM-estimated trajectory for cars driving in GPS-denied environments, by applying constraints from public databases of geo-tagged images (Google Street View, Mapillary, etc). The method, dubbed Appearance-based Geo-Alignment for Simultaneous Localisation and Mapping (AGA-SLAM), encodes the available image database as an appearance map, which represents the space with a compact holistic descriptor for each image plus its associated geo-tag. The VSLAM trajectory is corrected on-line by incorporating constraints from the recognized places along the trajectory into a position-based optimization framework. The paper presents a seamless formulation to combine local and absolute metric observations with associations from Visual Place Recognition. The robustness of the holistic image descriptor to changes due to weather or illumination variations ensures a long-term consistent method to improve car localization. The proposed method has been extensively evaluated on more than 70 sequences from 4 different datasets, proving out its effectiveness and endurance to appearance challenges.
Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes

Real-time dense 3D localization and mapping systems are required to enable robotics platforms to interact in and with their environments. Several solutions have used surfel representations to model the world. While they produce impressive results, they require heavy and costly hardware to operate properly. Many of them are also limited to static environments and small inter-frame motions. Whereas most of the state of the art approaches focus on the accuracy of the reconstruction, we assume that many robotics applications do not require a high resolution level in the rebuilt surface and can benefit from a less accurate but less expensive map, so as to gain in run-time and memory efficiency. In this paper we propose a fast RGB-D SLAM articulated around a rough and lightweight 3D representation for dense compact mapping in dynamic indoor environment, targeting mainstream computing platforms. A simple and fast formulation to detect and filter out dynamic elements is also presented. We show the robustness of our system, its low memory requirement and the good performance it enables.
Trust Aware Continuous Authorization for Zero Trust in Consumer Internet of Things

This work describes the architecture and prototype implementation of a novel trust-aware continuous authorization technology that targets consumer Internet of Things (IoT), e.g., Smart Home. Our approach extends previous authorization models in three complementary ways: (1) By incorporating trust-level evaluation formulae as conditions inside authorization rules and policies, while supporting the evaluation of such policies through the fusion of an Attribute-Based Access Control (ABAC) authorization policy engine with a Trust-Level-Evaluation-Engine (TLEE). (2) By introducing contextualized, continuous monitoring and re-evaluation of policies throughout the authorization life-cycle. That is, mutable attributes about subjects, resources and environment as well as trust levels that are continuously monitored while obtaining an authorization, throughout the duration of or after revoking an existing authorization. Whenever change is detected, the corresponding authorization rules, including both access control rules and trust level expressions, are re-evaluated. (3) By minimizing the computational and memory footprint and maximizing concurrency and modular evaluation to improve performance while preserving the continuity of monitoring. Finally we introduce an application of such model in Zero Trust Architecture (ZTA) for consumer IoT.
VFD-NOMA Under Imperfect SIC and Residual Inter-Relay Interference Over Generalized Nakagami-m Fading Channels

Full-duplex (FD) non-orthogonal multiple access (NOMA) communications is a promising technology to enhance spectral efficiency in future wireless access network. However, self-interference cancellation in FD is still a major challenge and thus concept of virtual full-duplex (VFD) relaying has evolved recently. In this letter, we investigate the impact of imperfect successive interference cancellation and residual inter-relay interference on a decode-and-forward relaying based VFD-NOMA. Framework for outage probability (OP), asymptotic OP, and ergodic rate expressions is developed for the considered system model over generalized Nakagami-m fading channels. Accuracy of the derived closed-form expressions is verified through rigorous Monte-Carlo simulations.
Waste Not, Want Not: Lessons in Rapid Quadrupedal Gait Termination from Thousands of Suboptimal Solutions

Elaborate trajectory optimization models with many degrees of freedom can be a useful locomotion-planning tool, as they provide rich solutions that take advantage of the robot’s specific morphology. They are, however, prone to falling into local minima. Depending on the seed that initializes the solver, the trajectories themselves and the extent to which they minimize the cost function can vary widely, making it impossible to judge the quality of any solution without generating many more. In this paper, we argue that this perceived drawback can actually be a powerful advantage in exploratory studies, since the resulting set of diverse motions can reveal which features tend to be associated with good performance, and therefore aid in the formulation of strategies for executing challenging maneuvers. We selected rapid gait termination from a high-speed gallop as our case study - a dangerous and scarcely-researched movement. By analyzing a set of over 3000 monopedal and quadrupedal trajectories, we were able to extract conclusions about how braking and sliding should be performed to reduce the stopping distance, and identify a hindlimb action that creates large braking forces.