Reviewerly/exHarmony huggingface dataset:
Using a VAE-SOM architecture for anomaly detection of flexible sensors in limb prosthesis

Flexible wearable sensor electronics, combined with advanced software functions, pave the way toward increasingly intelligent healthcare devices. One important application area is limb prosthesis, where printed flexible sensor solutions enable efficient monitoring and assessing of the actual intra-socket dynamic operation conditions in clinical and other more natural environments. However, the data collected by such sensors suffer from variations and errors, leading to difficulty in perceiving the actual operational conditions. This paper proposes a novel method for detecting anomalies in the data that are collected for measuring the intra-socket dynamic operation conditions by printed flexible wearable sensors. A discrete generative model based on Variational AutoEncoder (VAE) is used first to encode the collected multi-variant time-series data in terms of latent states. After that, a clustering method based on the Self-Organizing Map (SOM) is used to acquire discrete and interpretable representations of the VAE encoded latent states. ① An adaptive Markov chain is utilized to detect anomalies by quantifying state transitions and revealing temporal dependencies. The contributions of the proposed architecture conclude as follows: (1) Using the VAE-SOM hybrid model to regularize the continues data as discrete states, supporting interpreting the operational data to analytic models. (2) Employing adaptive Markov chains to generalize the transitions of these states, allowing to model the complex operational conditions. Compared with benchmark methods, our architecture achieves the best F1 score on two public datasets. Moreover, we measure the run-time performance of this lightweight architecture. The results indicate that the proposed method performs low computational complexity, facilitating the applications on the real-life productions.
Comparative Studies on Intelligent Swarming Network (iSWAN) Geno-Generative Algorithm and Top-K Query Processing Algorithm

This paper proposed an enhanced Top-k query processing in a real time distributed database system. The system employs a Particle Swarm Optimizer (PSO) based Geno-Generative iSWAN Model technique that enhances and allows multi-task concurrent query processing in a real time co-simulation data acquisition platform and as part of refinement to an existing Top-k query processing Technique. In this paper, the proposed system is compared for efficiency with the Top-K Query Algorithm, which is emerging as an alternative to more conventional technique for real time query processing in distributed databases. Dynamic simulations were performed with a real time small testbed comprising of physical and non-physical devices to test and evaluate the performance and efficiency of the two systems. Considering the estimated and expected temperatures, the result of simulation study proves that the Intelligent Swarming Network (iSWAN) Geno-Generative Model is more preferred over Top-K Query Algorithm due to its 70% accuracy over the Top-K Model, which reported a lower accuracy level of 40%.
The 3 Cs of Online Learning-Content, Coach and Community

The current era is now not approaching physical classroom learning for younger generations. This flaw leads to the Internet and distance learning called as online education which is one of the important part in education system. But there are some drawbacks in online education. In order to have a better understanding and ideology on online education and its implication among students mostly in a private higher learning institution, survey has been conducted and information is collected with secondary data and analysed using excel. This study mainly focuses on 3C’s-Content, Coach and Community to make it more clear about Online Learning and its role in Education System for improvising in a efficient manner.
Exploring Self-Supervised Pretraining Datasets for Complex Scene Understanding

With the rapid advancements of deep learning research, there have been many milestones achieved in the field of computer vision. However, most of these advances are only applicable in cases where hand-annotated datasets are available. This is considered the current bottleneck of deep learning that self-supervised learning aims to overcome. The self-supervised framework consists of proxy and target tasks. The proxy task is a self-supervised task pretrained on unlabeled data, the weights of which are transferred to the target task. The prevalent paradigm in self-supervised research is to pretrain using ImageNet which is a single-object centric dataset. In this work, we investigate whether this is the best choice when the target task is multi-object centric. We pretrain "SimSiam" which is a non-contrastive self-supervised algorithm using two different pretraining datasets: ImageNet100 (single-object centric) and COCO (multi-object centric). The transfer performance of each pretrained model is evaluated on the target task of multi-label classification using PascalVOC. Furtherly, we evaluate the two pretrained models using CityScapes; an autonomous driving dataset in order to study the implications of the chosen pretraining datasets in different domains. Our results showed that the SimSiam model pretrained using COCO consistently outperformed the ImageNet100 pretrained model by ~+1 percent (57.4 vs 58.3 mAP for CityScapes). This is significant since COCO is smaller in size. We conclude that using multi-object centric datasets for pretraining self-supervised learning algorithms is more efficient in cases where the target task is multi-object centric and in complex scene understanding tasks such as autonomous driving applications.
Implementation of Combinational and Sequential Logic Circuits using Quantum Computing

Quantum computing is an area of computer science that uses the principles of quantum theory. Quantum computing is a booming field and could lead significant advances in various fields, from chemistry to material science to nuclear physics and machine learning. Quantum computing uses quantum bits as a fundamental building block of computations. The power of quantum bits is their ability to scale exponentially, which makes complex computations faster and efficient. The objective of this paper is to design Flip Flops and combinational logic circuits using quantum computing. Flip Flops and combinational circuits assist in complex computations with reduced delay, and high efficiency when designed using quantum computing. The flip flops and adders can henceforth be used in several applications.
Determination of Silt and Clay soil particle distribution using new Silt-Clay Separation Quick method

Abstract Hydrometer method is the standard method that normally used to determine fine particle size distributions for soil classification purposes. This study aims to develop new method that enable quick determination of silt-clay particle distribution compared to the hydrometer method that requires procedure of pretreatment and additional 24 hours required for the complete procedure of the test. The various soil samples were collected from seven different locations including Sunway Carnival (Marine Clay), Suling Hills (Residual Clay), Jawi Quarry (Residual Clay), Kelantan River (River Clay), Batu Kawan (Marine Clay), Kaolin Clay and Sungai Petani (Residual Clay). Three soil samples from each site were prepared for the hydrometer method and additional three samples for the new method silt-clay separation. Sample preparation and testing is repeated for all samples from seven site locations. The results of silt clay particle distribution obtained from both experimental works were compared to analyze the compatibility of data. The data analysis shows that there are no significant differences (P &gt; 0.05) between the two methods in all examined soil samples. This indicates strong evidence to support this research that the silt-clay separation new method is comparable to the standard hydrometer method. Regression analysis between the two methods produced a coefficient of determination (R-square) values of 0.9912 respectively. The straight-line equation generated for all data produced the gradient equal to 0.8465, which is close to 1 indicates stronger evidence to supports the accuracy of the new method. In conclusion, the preliminary results of new method silt-clay separation confirmed its reliability to analyze the particle soil distribution in silt-clay fraction with 99.12% data accuracy compared to the standard hydrometer method.
Private Detective Activity in Slovenia – Organisation Matters

Abstract A private detective in Slovenia may perform detective activities as an independent profession or on the basis of an employment relationship for a natural or legal person with a registered private detective activity (ZDD-1, 2011). Private detectives completed a questionnaire when they attended the annual training in June 2018 – almost half of Slovenian private detectives participated in the survey. The study results show that almost half of self-employed private detectives investigate more drug abuse at work and abuse of competition clauses, while private detective agencies conduct more civil, legal, and administrative proceedings than other organisational forms. Some private detectives do not perform private detective tasks as their main activity, but as a secondary activity, and those individuals for whom private detective work is their primary/main profession mostly deliver packages, write reports and collect debtors’ data on behalf of their clients.
Simulation and Research of Railway Foreign Body Invasion System Based on Single Chip Microcomputer

Abstract With the accelerating growth of railway operating mileage in China, ensuring the safety of railway operations has become a critical problem. According to some previous studies, the railway foreign body invasion limit is a major threat to the safe operation of the railway. As the current stage mainly adopts the combination of manual inspection and video monitoring, it has some disadvantages, such as being time-consuming, laborious, and inefficient. Meanwhile, it cannot timely detect potential danger. Therefore, the research on the simulation of railway foreign body intrusion systems based on single-chip microcomputers is carried out. The program is compiled in C language and uses the microcontroller to achieve real-time foreign object detection and alarm for encroachment. This paper mainly introduces the principle of the system and uses Proteus software to design and simulate the circuit. Last but not least, welding is carried out according to the circuit diagram, and the physical test is carried out to verify the feasibility of the design.
What Are Bayesian Filtering and Smoothing?

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FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments

We introduce the Few-Shot Object Learning (FEWSOL) dataset for object recognition with a few images per object. We captured 336 real-world objects with 9 RGB-D images per object from different views. Fewsol has object segmentation masks, poses, and attributes. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. We investigated (i) few-shot object classification and (ii) joint object segmentation and few-shot classification with state-of-the-art methods for few-shot learning and meta-learning using our dataset. The evaluation results show the presence of a large margin to be improved for few-shot object classification in robotic environments, and our dataset can be used to study and enhance few-shot object recognition for robot perception <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Dataset and code available at https://irvlutd.github.io/FewSOL.
A Digital Twin for Teleoperation of Vehicles in Urban Environments

Teleoperated driving (ToD) is increasingly considered as a fallback solution for autonomous driving. Up to now, ToD requires a highly reliable mobile network capable of transmitting multiple video streams with low latency. Recently, significant advancements have been made in vehicular sensors and perception algorithms, which have huge implications for the challenges faced in ToD. We envisage that a real-time digital twin that tracks the remote vehicle's environment will play a crucial role in reducing the required communication band-width and providing a more convenient teleoperator interface, ultimately enhancing safety of ToD in crowded environments. Furthermore, it would allow various degrees of cooperation between automated driving functionalities and human teleoperators. In this paper, the concept of digital twin for ToD is outlined and a proof of concept is implemented using a real-world vehicle simulator and a teleoperator hardware setup. A significant reduction in required bandwidth is reported by transmitting less video data and reconstructing the scene from the digital twin.
CNN-based Visual Servoing for Simultaneous Positioning and Flattening of Soft Fabric Parts

This paper proposes CNN-based visual servoing for simultaneous positioning and flattening of a soft fabric part placed on a table by a dual manipulator system. We propose a network for multimodal data processing of grayscale images captured by a camera and force/torque applied to force sensors. The training dataset is collected by moving the real manipulators, which enables the network to map the captured images and force/torque to the manipulator's motion in Cartesian space. We apply structured lighting to emphasize the features of the surface of the fabric part since the surface shape of the non-textured fabric part is difficult to recognize by a single grayscale image. Through experiments, we show that the fabric part with unseen wrinkles can be positioned and flattened by the proposed visual servoing scheme.
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions from Single Panoramas

In this work we present FreDSNet, a deep learning solution which obtains semantic 3D understanding of indoor environments from single panoramas. Omnidirectional images reveal task-specific advantages when addressing scene understanding problems due to the 360-degree contextual information about the entire environment they provide. However, the inherent characteristics of the omnidirectional images add additional problems to obtain an accurate detection and segmentation of objects or a good depth estimation. To overcome these problems, we exploit convolutions in the frequential domain obtaining a wider receptive field in each convolutional layer. These convolutions allow to leverage the whole context information from omnidirectional images. FreDSNet is the first network that jointly provides monocular depth estimation and semantic segmentation from a single panoramic image exploiting fast Fourier convolutions. Our experiments show that FreDSNet has slight better performance than the sole state-of-the-art method that obtains both semantic segmentation and depth estimation from panoramas. FreDSNet code is publicly available in https://github.com/Sbrunoberenguel/FreDSNet
GRM: Gradient Rectification Module for Visual Place Retrieval

Visual place retrieval aims to search images in the database that depict similar places as the query image. However, global descriptors encoded by the network usually fall into a low dimensional principal space, which is harmful to the retrieval performance. We first analyze the cause of this phenomenon, pointing out that it is due to degraded distribution of the gradients of descriptors. Then, we propose Gradient Rectification Module (GRM) to alleviate this issue. GRM is appended after the final pooling layer and can rectify gradients to the complementary space of the principal space. With GRM, the network is encouraged to generate descriptors more uniformly in the whole space. At last, we conduct experiments on multiple datasets and generalize our method to classification task under prototype learning framework.
Concentration of Measure Phenomenon and its Implications for Sample-based Planning Algorithms in Very-High Dimensional Configuration Spaces

In very high-dimensional <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\gg 10)$</tex> spaces, a collection of points generated uniformly at random will concentrate very tightly about its expected value - defying intuition developed in low-dimensional spaces. This paper explores the implications of this for two major classes of sample-based robot motion planning algorithms: Rapidly Exploring Random Trees (RRTs) and Probabilistic Road Maps (PRMs). First we show that the graph vertices concentrate in a thin-shelled hyper-sphere, with almost none near the origin nor at the edges of the workspace. Next we examine how varying one of the algorithms' parameters - the maximum edge length- can dramatically alter the algorithms' complexity and the connectivity of the resulting graph. Finally, we explore how the position of the initial node, often placed arbitrarily, can impact the shape of the graph. While the contributions of this paper are largely theoretical, many robotic applications of practical interest have extremely high-dimensional configuration spaces including humanoids, swarms and soft (a.k.a. continuum) robotics.
Bidirectional Generalised Rigid Point Set Registration

In medical robotics and image-guided surgery (IGS), registration is needed in order to align together the coordinate frames of robots, medical imaging modalities, surgical tools, and patients. Existing registration algorithms often assume one point set to be a noise-free model while the other to contain noise and outliers. However, in real scenarios, noise and outliers can exist in both point sets to be registered. To eliminate the above-mentioned challenge, in this paper, we formally formulate the Bi-directional Generalised Rigid Point Set Registration (Bi-GRPSR) problem where normal vectors are adopted, bi-directional probability density function (PDFs) and Hybrid Mixture Models (HMMs) are constructed to derive the objective function. Bi-GRPSR considering anisotropic positional noise is thus cast as a maximum likelihood estimation (MLE) problem, which is solved by the proposed Bi-directional Generalised Anisotropic Coherent Point Drift (Bi-AGCPD) where spatially nearby points are considered to move coherently and iterative expectation maximization (EM) steps are involved. Experimental results on two human bone point sets, under different settings of noise, outliers, and overlapping ratios, validate the effectiveness and improvements of Bi-AGCPD over existing probabilistic and learning-based methods.
Combining Motion and Appearance for Robust Probabilistic Object Segmentation in Real Time

We present a robust method to visually segment scenes into objects based on motion and appearance. Both these cues provide complementary information that we fuse using two interconnected recursive estimators: One estimates object segmentation from motion as a probabilistic clustering of tracked 3D points, and the other estimates object segmentation from appearance as a probabilistic image segmentation. The interconnected estimators provide a probabilistic and consistent object segmentation in real time, which makes them well suited for many downstream robotic tasks. We evaluate our method on one such task, kinematic structure estimation, on a dataset of interactions with articulated objects and show that our fusion improves object segmentation by 70% and in turn estimated kinematic joints by 26% over a purely motion-based approach. Furthermore, we show the necessity of probabilistic modeling for downstream robotic tasks, achieving 339% of the performance of a recent multimodal but deterministic RNN for object segmentation on the estimation of kinematic structure.
Semantic Mapping with Confidence Scores through Metric Embeddings and Gaussian Process Classification

Recent advances in robotic mapping enable robots to use both semantic and geometric understanding of their surroundings to perform complex tasks. Current methods are optimized for reconstruction quality, but they do not provide a measure of how certain they are of their outputs. Therefore, algorithms that use these maps do not have a way of assessing how much they can trust the outputs. We present a mapping approach that unifies semantic information and shape completion inferred from RGBD images and computes confidence scores for its predictions. We use a Gaussian Process (GP) classification model to merge confidence scores (if available) for the given information. A novel aspect of our method is that we lift the measurement to a learned metric space over which the GP parameters are learned. After training, we can evaluate the uncertainty of objects' completed shapes with their semantic information. We show that our approach can achieve more accurate predictions than a classic GP model and provide robots with the flexibility to decide whether they can trust the estimate at a given location using the confidence scores.
Learning Stabilization Control from Observations by Learning Lyapunov-like Proxy Models

The deployment of Reinforcement Learning to robotics applications faces the difficulty of reward engineering. Therefore, approaches have focused on creating reward functions by Learning from Observations (LfO) which is the task of learning policies from expert trajectories that only contain state sequences. We propose new methods for LfO for the important class of continuous control problems of learning to stabilize, by introducing intermediate proxy models acting as reward functions between the expert and the agent policy based on Lyapunov stability theory. Our LfO training process consists of two steps. The first step attempts to learn a Lyapunov-like landscape proxy model from expert state sequences without access to any kinematics model, and the second step uses the learned landscape model to guide in training the learner's policy. We formulate novel learning objectives for the two steps that are important for overall training success. We evaluate our methods in real automobile robot environments and other simulated stabilization control problems in model-free settings, like Quadrotor control and maintaining upright positions of Hopper in MuJoCo. We compare with state-of-the-art approaches and show the proposed methods can learn efficiently with less expert observations.