Reviewerly/exHarmony huggingface dataset:
DEN: Disentangling and Exchanging Network for Depth Completion

In this paper, we tackle the depth completion problem. Conventional depth sensors usually produce incomplete depth maps due to the property of surface reflection, especially for the window areas, metal surfaces, and object boundaries. However, we observe that the corresponding RGB images are still dense and preserve all of the useful structural information. The observation brings us to the question of whether we can borrow this structural information from RGB images to inpaint the corresponding incomplete depth maps. In this paper, we answer that question by proposing a Disentangling and Exchanging Network (DEN) for depth completion. The network is designed based on the assumption that after suitable feature disentanglement, RGB images and depth maps share a common domain for representing structural information. So we firstly disentangle both RGB and depth images into domain-invariant content parts, which contain structural information, and domain-specific style parts. Then, by exchanging the complete structural information extracted from the RGB image with incomplete information extracted from the depth map, we can generate the complete version of the depth map. Furthermore, to address the mixed-depth problem, a newly proposed depth representation is applied. By modeling depth estimation as a classification problem coupled with coefficient estimation, blurry edges are enhanced in the depth map. At last, we have implemented ablation experiments to verify the effectiveness of the proposed DEN model. The results also demonstrate the superiority of DEN over some state-of-the-art approaches.
Low Cost IoT-Based Smart Wheelchair for Type-2 Diabetes and Spine-Disorder Patients

A smart wheelchair is a powered chair which eliminates the need for user control thereby restoring autonomy to patients with various disabilities like Type-2 diabetes (T2D), spine related disorder, skeletal deformities and other maladies thereby enabling them to navigate smoothly. The main objective of the smart wheelchair is to reduce user effort during control and to ensure safety during movement. In this paper, the focus is to design and develop an user-friendly smart wheelchair using inexpensive hardware and open-source software so that the larger section of the target population, particularly developing countries like India can afford it. The user can control the wheelchair by giving discrete voice commands with the help of the voice application in the smartphone instead of hands or mechanical means and navigate accordingly. The wheelchair can detect obstacles and avoid the same to prevent accidents. The position of the wheelchair is tracked by global positioning system (GPS) and sent to the cloud platform which can be further processed. The architecture of the system is described in detail and experiments are performed to demonstrate the utility of the same.
Automated Assessment of Lymphocytes Using Machine Learning Techniques

This paper investigates the performance of machine learning techniques for automated assessment of lymphocytes. A total of four algorithms, i.e., support vector machine (SVM), deep learning for java (DL4J), multi-layer perceptron (MLP) and K* are applied to the lymphocytes dataset. To ensure the robustness, all the four algorithms are evaluated with both preprocessed data and data without prior pre-processing. With the pre-processed data, MLP outperforms all the other techniques and achieve an accuracy of 98.64%. SVM, DL4J and K* achieve accuracies of 97.97%, 96.62% and 97.29%, respectively.
Not all domains are equally complex: Adaptive Multi-Domain Learning

Deep learning approaches are highly specialized and require training separate models for different tasks. Multidomain learning looks at ways to learn a multitude of different tasks, each coming from a different domain, at once. The most common approach in multi-domain learning is to form a domain agnostic model, the parameters of which are shared among all domains, and learn a small number of extra domain-specific parameters for each individual new domain. However, different domains come with different levels of difficulty; parameterizing the models of all domains using an augmented version of the domain agnostic model leads to unnecessarily inefficient solutions, especially for easy to solve tasks. We propose an adaptive parameterization approach to deep neural networks for multidomain learning. The proposed approach performs on par with the original approach while reducing by far the number of parameters, leading to efficient multi-domain learning solutions.
Fusing Multitask Models by Recursive Least Squares

It is easy to obtain multi-tasking models from open source platforms or various organizations. However, using these models at the same time will bring a great burden on storage and reduce computing efficiency. In this paper, we propose a transformation-based multi-task fusion method, called transformation fusion(TF), which is implemented by recursive least squares. The recursive transformation fusion not only reduces the storage burden brought by models fusion but also avoids computing the inverse matrix of high-dimensional matrix. Our multi-model fusion method can also be applied to many mainstream tasks, such as multi-task learning and offline distributed learning. Our fusion method can be applied to data-based fusion tasks as well as data-free fusion tasks. Extensive experiments demonstrate the effectiveness of our fusion method.
The influence of state policy on the development of Soviet out-of-school chemistry education in the 1920

The article presents an analysis of the actual Soviet practice of out-of-school chemistry education in the early years of the Soviet power and the changes that took place in out-of-school education in the new educational environment under the influence of socio-political factors. A description of the main modernisation processes in the education system as a whole, which influenced the development and improvement of Soviet out-of-school chemistry education in the 1920s, is presented. The main legislative acts and decrees of the Soviet government in the field of out-of-school education in the 1920s, which led to the renewal of the goals and content of additional chemistry education in accordance with the tasks of global polytechnic national education of the people, are considered. At the legislative level, the Soviet power had returned the management of out-of-school education to the People's Commissariat for Education, which contributed to its development and the variety of its directions and forms. The article focuses on the need to carefully study the positive historical experience of organising out-of-school chemistry education in the 1920s in order to identify the directions of its modernisation at the present stage of development of the system of additional education for schoolchildren.
Multikernel Passive Stochastic Gradient Algorithms and Transfer Learning

This article develops a novel passive stochastic gradient algorithm. In passive stochastic approximation, the stochastic gradient algorithm does not have control over the location where noisy gradients of the cost function are evaluated. Classical passive stochastic gradient algorithms use a kernel that approximates a Dirac delta to weigh the gradients based on how far they are evaluated from the desired point. In this article, we construct a multikernel passive stochastic gradient algorithm. The algorithm performs substantially better in high dimensional problems and incorporates variance reduction. We analyze the weak convergence of the multikernel algorithm and its rate of convergence. In numerical examples, we study the multikernel version of the passive least mean squares algorithm for transfer learning to compare the performance with the classical passive version.
Vibration Measurement of Signal Bonds for Shinkansen

In automatic train control systems, which are indispensable for train services, a signal bond is used for positioning of and communicating with trains, and is welded to a rail. If the signal bond welded to the rail falls off over time and loses its electrical connection, trains cannot be operated. This paper proposes a method for detecting welding detachment of signal bonds by vibration analysis using IoT technology. Specifically, the vibration of signal bonds is analyzed by using the vibration when the train passes through the rails to detect welding detachment. A vibration measurement experiment was conducted at Shin-Tosu station of Kyushu Railway Company, Japan. This paper shows the vibration of a signal bond welded normally obtained from the experiment.
A 32x32 Time-Domain Wavefront Computing Accelerator for Path Planning and Scientific Simulations

Recent hardware accelerators based on FPGA –[2] and ASIC –[4] have demonstrated high throughput and energy-efficient processing of path planning for applications, including the manipulation of arm-robots on a high-resolution map [1] and the autonomous navigation of battery-powered micro-robots [4]. The accelerators have focused on demonstrating the modified shortest path planning algorithms, such as extended-stride A$\ast$ [1] and dual-tree rapid-exploring random tree (RRT) [4], and achieved much higher throughput and energy efficiency (two-to-three orders of magnitude higher) than traditional implementations with CPU and GPU. A two-dimensional processing element (PE) array [5] was proposed as an alternative computing paradigm for searching the shortest path based on the expansion of wavefront in the time-domain. The time-domain delay accumulation was used for finding the shortest paths by accumulating time delays while propagating through the PEs that correspond to the pixels in a two-dimensional searching space. However, it still follows the existing A$\ast$ algorithm using an extra gradient control circuit with resistor ladders to estimate the remaining distances, resulting in inaccurate results (i.e. sub-optimal paths) with extra hardware and energy overhead.
Investigating motor skill training and user arousal levels in VR : Pilot Study and Observations

Virtual Reality (VR) for skill training is seeing increasing interest from academia and industry thanks to the highly immersive and realistic training opportunities they offer. Of the many factors affecting the effectiveness of training in VR, the arousal levels of the users merit a closer examination. Though subjective methods of measuring arousal exist in VR literature, there is potential in using cost-effective sensors to directly measure these from bio-signals generated by the nervous system. We introduce the design of preliminary observations from a pilot study exploring user's arousal levels and performance while executing a series of fine motor skill tasks (buzzwire tracing). Future directions of the work are also discussed.
AirFlute

The AirFlute is an interactive and less-expensive alternative to traditional music lessons. AirFlute uses the Leap Motion Controller within a web browser to track how a student moves their fingers in 3-dimensional physical space. Each movement of the student (e.g., moving or bending a finger) is visualized on the virtual flute display within a web browser and the corresponding note is played through the computer's speaker. By expressing a correct fingering, a student can simulate playing a note on the flute without a physical instrument. AirFlute has three main features: 1) a place for users to free play music, 2) a tutorial for users to learn note fingerings, and 3) a capability for users to practice exercises and receive feedback. This brief abstract summarizes the motivation and implementation of AirFlute as a research application in human-computer interaction. AirFlute has the potential to broaden participation in music performance for students who may not be able to afford a physical instrument.
Distance Estimation with Mobile Augmented Reality in Action Space: Effects of Animated Cues

Augmented reality is standard on many modern smartphone platforms. The distance of virtual objects seen through the smartphone display should be perceived accurately for easy interaction with virtual objects with high fidelity. We investigate whether distance perception through mobile augmented reality devices is affected by an animated avatar. The avatar walks and is positioned from near to far action space. We conduct a distributed experiment "in the wild" to investigate these effects.
Resource aware scheduler for distributed stream processing in cloud native environments

Abstract Recently distributed stream processors are increasingly being deployed in cloud computing infrastructures. In this article, we study performance characteristics of distributed stream processing applications in Google Compute Engine which is based on Kubernetes. We identify performance gaps in terms of throughput which appear in such environments when using a round robin (RR) scheduling algorithm. As a solution, we propose resource aware stream processing scheduler called resource aware scheduler for stream processing applications in cloud native environments (RaspaCN). We implement RaspaCN's job scheduler using two‐step process. First, we use machine learning to identify the optimal number of worker nodes. Second, we use RR and multiple Knapsack algorithms to produce performance optimal stream processing job schedules. With three application benchmarks called HTTP Log Processor, Nexmark, and Email Processor representing real world stream processing scenarios we evaluate the performance benefits obtained via RaspaCN's scheduling algorithm. RaspaCN could produce percentage increase of average throughput values by at least 37%, 38%, and 10%, respectively, for HTTP Log Processor, Nexmark, and Email Processor benchmarks for fixed input data rates. Furthermore, we conduct experiments with varying input data rates as well and show 7% improved average throughput for HTTP Log Processor. These experiments show the effectiveness of our proposed stream processor job scheduler for producing improved performance.
Learning Discriminative Features for Semi-Supervised Anomaly Detection

Anomaly detection is the task of identifying unusual samples in data. Typically anomaly detection is defined on an unlabeled dataset that is assumed most of the samples are normal and others are anomalies. However, in industrial practice, one may have access to a part of annotated data. This gives us the potential for semi-supervised learning. In addition, existing methods assume all training data is normal and neglect the impact of a small number of anomalous samples. In this paper, we consolidate the model’s discriminative power by introducing a transfer learning scheme to anomaly detection, thereby the model suffers less perturbation caused by pollution. We also propose a novel loss function to further adapt to semi-supervised data scenario. We ensure that the contribution of pollution can be well suppressed and reach a harmonious balance in magnitude of loss/gradient between unlabeled and labeled samples. Experiments on three publicly available datasets show that our method achieves state-of-the-art results.
WITHDRAWN: Stochastic energy saving strategies using machine learning for badminton robots

In Sports, specifically, badminton is a dynamic and skill motions for participants. Previous robots had difficulties behaving as human beings because of their high limitations of low operational speed, heavy bodies, and basic mechanisms. This paper introduces two control approaches to improve a robot's energy effectiveness which needs to make point-to-point motions over a set period. The first method is based on an Adaptive Proximate Energy-Optimized Servo Algorithm (APEOSA) which has optimized parameters for energy efficiency. The second strategy is a Model Predictive Control Strategy (MPCS) to energy management. The technique has been created for a Badminton robot. The robot may still intercept several competitor transportation units on time and in both cases, there is a significant decrease in energy consumption which has been minimized during experimental analysis. An enormous energy-saving, about 40%, is accomplished using EOMPCS and APEOSA compared to APTOS, with the same positioning error as faced by EOMPCS and APTOS.
HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Many biomedical applications require accurate non-rigid image registration that can cope with complex deformations. However, popular diffeomorphic Demons registration algorithms suffer from difficulties for complex and serious distortions since they only use image greyscale and gradient information. To address these difficulties, a new diffeomorphic Demons registration algorithm is proposed using hierarchical neighbourhood spectral features namely HNSF Log-Demons in this paper. In view of three important properties of hierarchical neighbourhood spectral features based on line graph such as rotation invariance, invariance of linear changes of brightness, and robustness to noise, the hierarchical neighbourhood spectral features of a reference image and a moving image is first extracted and these novel spectral features are incorporated into the energy function of the diffeomorphic registration framework to improve the capability of capturing complex distortions. Secondly, the Nystr o ̈ m approximation based on random singular value decomposition is employed to effectively enhance the computational efficiency of HNSF Log-Demons. Finally, the hybrid multi-resolution strategy based on wavelet decomposition in the registration process is utilised to further improve the registration accuracy and efficiency. Experimental results show that the proposed HNSF Log-Demons not only effectively ensures the generation of smooth and reversible deformation field, but also achieves better performance than state-of-the-art algorithms.
Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos

Data storage has been a problem as technology advances, there are more devices capable of capturing images, sounds, videos, etc. On the security side, many people choose to use security cameras that are available 24 h a day to capture anomalous events and maintain the security of the area, however, storing all captured videos generates high costs, as well as the prolonged analysis that this type of videos implies. For this reason, we propose a method that allows selecting only the important events captured by a video surveillance camera and then classifying them among the types of most constant criminal acts in Peru.
Towards closing the gap in weakly supervised semantic segmentation with DCNNs: Combining local and global models

Generating training sets for Deep Convolutional Neural Networks (DCNNs) is a bottleneck for modern real-world applications. This is a demanding task for applications where annotating training data is costly, such as in semantic segmentation. In the literature, there is still a gap between the performance achieved by a network trained on full and on weak annotations. In this paper, we establish a simple and natural strategy to measure this gap and to identify the components necessary to reduce it. On scribbles, we establish new state-of-the-art results: we obtain a mIoU of 75.6% without, and 75.7% with CRF post-processing. We reduce the gap by 64.2% whereas the current state-of-the-art reduces it only by 57.5%. Thanks to a formal reformulation of the weak supervision problem, a systematic study of the different components involved, and an original experimental strategy, we unravel a counter-intuitive mechanism analog to the philosophy of ensemble learning. This strategy is simple and amenable to generalizations to other weakly-supervised scenarios: averaging poor local predicted annotations with a generic naive baseline and reusing them for training a DCNN yields new state-of-the-art results. We show that our strategy accommodates effortlessly other pixel-level weak annotations such as bounding boxes and remains competitive.
Exploring 4D Image Sets of Early Heart Development Using Gesture and an Immersive, Spatial Operating Environment

Abstract Scientists today can collect more data than they can perceive using traditional visualization methods. New technologies and sensors allow researchers to gather dynamic, complex multidimensional datasets—all of which must be carefully studied to reveal hidden patterns and narratives. The authors used an immersive platform to design a new visualization for real-time, intuitive spatial manipulations of time-based volumetric datasets via a wand-based gestural interface. The resulting work resolves microscopic tissue structures at a human scale in a room-based pixel space, facilitating research, discovery and in-person teaching and collaboration.
Attention-guided Multi-view Stereo Network For Depth Estimation

The purpose of the Multi-View Stereo is to restore the target 3D geometric model from multi-perspective images. There are several problems with the existing approaches based on deep learning, such as missing the detailed information in the predicted depth map, the low surface accuracy, and the incomplete reconstructed 3D point cloud model. In order to overcome these problems, we propose the Attention-guided Multiview Stereo Network For 3D Depth Estimation(AG-MVSNet). We combine the camera geometry with the deep neural network. And we adopt the coarse-to-fine deep learning framework to restore the target 3D geometry model. High-quality detailed feature information has an important influence on multi-view 3D reconstruction, and reference images in the natural environment contain detailed feature information which is needed in the reconstruction process. Therefore, we use the detailed feature information from different scales of reference images to restore the lost details of the high-level features. The quantitative and qualitative experimental results show that the proposed algorithm is more complete than the common multi-view 3D reconstruction algorithms.
Study of DRMF and ASM facial landmark point for micro expression recognition using KLT tracking point feature

Abstract Micro-expression recognition is one of the popular researches in analysing expressions on the face. Micro-expression is a facial movement that occurs in a short time and is difficult to identify manually by human eyes. In general research, facial landmarks are used to form a large size ROI for each facial feature for the feature extraction process. In this study, we track the subtle motions of micro expressions by using point features. This approach aims to get feature extraction from tracking results and then analyse micro-expression. We compared the Active Shape Model and Response Map Fitting methods to produce accurate points and fast time on facial features. To measure the subtle motion tracking of facial features in each frame tracking is done using the Kanade-Lucas-Tomasi method. To estimate the rationality of our method, we conducted an experiment on CASME II and SAMM dataset for micro-expressions. The results show that the points on DRMF are more accurate with point-to-point error is 7.9 and the time taken is faster which requires time is 0.02 second. We evaluated the method proposed for evaluation showed that using CASME II - Naive Bayes (79.3%) and SAMM - Naive Bayes (74.6%).