Reviewerly/exHarmony huggingface dataset:
Deep Classification-driven Domain Adaptation for Cross-Modal Driver Behavior Recognition

We encounter a wide range of obstacles when integrating computer vision algorithms into applications inside the vehicle cabin, e.g. variations in illumination, sensor-type and -placement. Thus, designing domain-invariant representations is crucial for employing such models in practice. Still, the vast majority of driver activity recognition algorithms are developed under the assumption of a static domain, i.e. an identical distribution of training- and test data. In this work, we aim to bring driver monitoring to a setting, where domain shifts can occur at any time and explore generative models which learn a shared representation space of the source and target domain. First, we formulate the problem of unsupervised domain adaptation for driver activity recognition, where a model trained on labeled examples from the source domain (i.e. color images) is intended to adjust to a different target domain (i.e. infrared images) where only unlabeled data is available during training. To address this problem, we leverage current progress in image-to-image translation and adopt multiple strategies for learning a joint latent space of the source and target distribution and a mapping function to the domain of interest. As our long-term goal is a robust cross-domain classification, we enhance a Variational Auto-Encoder (VAE) for image translation with a classification-driven optimization strategy. Our model for classification-driven domain transfer leads to the best cross-domain recognition results and outperforms a conventional classification approach in color-to-infrared recognition by 13.75%.
A Study on Global Path Planners Algorithms for the Simulated TurtleBot 3 Robot in ROS

This article aims to study global path planners algorithms using a mobile robot in the ROS (Robot Operating System) framework. Experiments were carried out in different environments, aiming to apply the Dijkstra and A * algorithms in simulation. The tests were performed with the robot TurtleBot 3 Burger, which runs using open source software. Location and orientation data from the sensors of the robot were stored. After analyzing the results, there was a slight superiority of Dijkstra algorithm from the ROS libraries, in relation to the A * algorithm in the environments with obstacles.
Valuable Waste Classification Modeling based on SSD-MobileNet

One of the most challenging classifications is the waste classification. If this can be done automatically, it will have a great use in waste management industry in terms of time and cost reduction. Nowadays, 2 kinds of waste classification and separation exist, namely the manual waste classification and the automatic waste classification using various technologies. The first one can be done using human understanding and power, while the latter is in the search of suitable methods for waste classification automatically. This work aimed to optimize the waste classification using SSD-MobileNet, which is a Convolutional Neural Network architecture. The purposed waste are the plastic bottles, the glass bottles, and the metal cans. The number of image dataset used in this work is 952. The model has 24,000 steps of training and 9 hours in total. The loss value of the model is 0.2711. The model is tested on Raspberry Pi 4 with the average classification accuracy of 95% for plastic bottles, 82% for glass bottles, and 86% for metal cans. With these waste classification accuracies, this model can be used in an embedded system for waste classification.
The Road to Ubiquitous Personal Fabrication: Modeling-Free Instead of Increasingly Simple

The tools for personal digital fabrication (DF) are on the verge of reaching mass-adoption beyond technology enthusiasts, empowering consumers to fabricate personalized artifacts.We argue that to achieve similar outreach and impact as personal computing, personal fabrication research may have to venture beyond ever-simpler interfaces for creation, towards lowest-effort workflows for remixing.We surveyed novice-friendly DF workflows from the perspective of HCI.Through this survey, we found two distinct approaches for this challenge: 1) simplifying expert modeling tools (AutoCAD →Tinkercad), 2) enriching tools not involving primitive-based modeling with powerful customization (e.g., Thingiverse).Drawing parallels to content creation domains like photography, we argue that the bulk of content is created via remixing (2).In this work, we argue that to be able to include the majority of the population in DF, research should embrace omission of workflow steps, shifting towards automation, remixing, and templates, instead of modeling from the ground up.
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification

Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and reidentification.The existing approaches for person, vehicle, or animal re-identification tasks suffer from high intra-class variance due to deformable shapes and different camera viewpoints.To overcome this limitation, we propose to align the image embedding with a predefined order of the keypoints.The proposed keypoint aligned embeddings model (KAE-Net) learns part-level features via multi-task learning which is guided by keypoint locations.More specifically, KAE-Net extracts channels from a feature map activated by a specific keypoint through learning the auxiliary task of heatmap reconstruction for this keypoint.The KAE-Net is compact, generic and conceptually simple.It achieves state of the art performance on the benchmark datasets of CUB-200-2011, Cars196 and VeRi-776 for retrieval and reidentification tasks.
Vector Field Guided Path Following Control: Singularity Elimination and Global Convergence

Vector field guided path following (VF-PF) algorithms are fundamental in robot navigation tasks, but may not deliver the desirable performance when robots encounter singular points where the vector field becomes zero. The existence of singular points prevents the global convergence of the vector field’s integral curves to the desired path. Moreover, VF-PF algorithms, as well as most of the existing path following algorithms, fail to enable following a self-intersected desired path. In this paper, we show that such failures are fundamentally related to the mathematical topology of the path, and that by "stretching" the desired path along a virtual dimension, one can remove the topological obstruction. Consequently, this paper proposes a new guiding vector field defined in a higher-dimensional space, in which self-intersected desired paths become free of self-intersections; more importantly, the new guiding vector field does not have any singular points, enabling the integral curves to converge globally to the "stretched" path. We further introduce the extended dynamics to retain this appealing global convergence property for the desired path in the original lower-dimensional space. Both simulations and experiments are conducted to verify the theory.
Free Logics are Cut-Free

Abstract The paper presents a uniform proof-theoretic treatment of several kinds of free logic, including the logics of existence and definedness applied in constructive mathematics and computer science, and called here quasi-free logics. All free and quasi-free logics considered are formalised in the framework of sequent calculus, the latter for the first time. It is shown that in all cases remarkable simplifications of the starting systems are possible due to the special rule dealing with identity and existence predicate. Cut elimination is proved in a constructive way for sequent calculi adequate for all logics under consideration.
Single-photon frequency shifting with a quadrature phase-shift keying modulator

Abstract Deterministic frequency manipulation of single photons is an essential tool for quantum communications and quantum networks. We demonstrate a 15.65 GHz frequency shift for classical and nonclassical light using a commercially available quadrature phase-shift keying modulator. The measured spectrum of frequency-shifted single photons indicates a high carrier-to-sideband ratio of 30 dB. We illustrate our frequency shifter’s utility in quantum photonics by performing Hong–Ou–Mandel quantum interference between two photons whose initial frequency spectra overlap only partially, and showing visibility improvement from 62.7 to 89.1% after one of the photons undergoes a corrective frequency shift.
Will blockchain shift Online Travel Agencies toward growth or to an end?

The hospitality industry is one of the fastest-growing sectors globally. With the emergence of different distribution platforms, customers can obtain real-time price changes, availability, and promotions. Looking to the future, traditional online travel agencies will have to undergo a utility shift in order to adapt to new technology. Therefore, blockchain technology has the potential to revolutionize distribution channels’ operational processes. This article aims to introduce blockchain’s immense potential and initiate two research propositions for further study.
RETRACTED: Image recognition of competitive aerobics movements based on embedded system and digital image processing

Aerobics Exercise in the form of exercise that combines rhythmic aerobic exercise with stretching and strength training practices improves all the elements. The main purpose of designing the system for receiving the aerobics image is to train based on motion recognition technology. To establish a link between aerobics social adaptation and students’ cultivation at different levels through the impact of experimental data related to aviation training, empirical inquiry, and analysis. The most popular aerobic exercise machine on the treadmill. There are handrails on the treadmill, which is perfect for recovering from an injury. Just like any cardio exercise for a heart-pumping, it can help reduce the risk of heart disease and other chronic diseases, improve sleep, improve your mood, and increase brain function. It is very important to implement training programs that are the best medicine for the heartbeat's precise control (manpower) during exercise on the automatic and treadmill. Treadmill embedding technology can enhance disposal intelligence and add additional features. Treadmill system structure and work patterns are analyzed by scientifically embedded technology and control system design. The systems’ hardware layout is explored and designed, effectively combining embedded technology and serial communication technology to achieve optimal optical treadmill science display layout and game parameters. Therefore, the treadmill control system based on embedded technology has high-cost performance, low cost and relatively complete fashion characteristics, and has a good usability opportunity.
Two-stage approach to feature set optimization for unsupervised dataset with heterogeneous attributes

Abstract Unsupervised feature selection (UFS) is utilized in various application domains, such as data mining, pattern recognition, machine learning, etc. UFS follows three basic approaches, namely filter, wrapper, and hybrid (that is, a combination of both filter and wrapper) to select the relevant and non-redundant features. It has been observed that a filter method does not guarantee an optimal solution. However, a wrapper approach is computationally expensive. The hybrid method are known to give a better trade-off between filter and wrapper strategies. But, the practical applicability of schemes mentioned above are preferably restricted only to a numerical dataset and are not so suitable for a mixed dataset. Therefore, there is a need for a UFS scheme which can handle both the numerical and non-numerical features directly. In this paper, a robust and efficient two-phase (i.e., feature ranking (FR) and feature selection (FS)) UFS method is proposed. The proposed FR utilizes entropy and mutual information to produce maximum informative and non-redundant ranked features from a high-dimensional mixed dataset. Further, the proposed FS follows k-prototype clustering algorithm with improved Callinski-Harasbaz criteria-based selection methodology to choose optimal features. Experiments on real-life dataset substantiate that the proposed approach provides a better subset of features compared to the existing state of the art approaches.
A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization

This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed algorithm consists of three mechanisms. First, we set up a modular neural network (MNN) and train it with historical population information. Some of the initial solutions are generated by the MNN when an environmental change is detected. Second, some solutions are predicted based on forward-looking center points. Finally, some solutions are generated randomly to maintain the diversity. With these mechanisms, when the new environment has been encountered before, initial solutions generated by MNN will have the same distribution characteristics as the final solutions that were obtained in the same environment last time. Because the initialization mechanism based on the MNN does not need the solutions in recent time, the proposed algorithm can also solve dynamic multi-objective optimization problems with a dramatically and irregularly changing Pareto set. The proposed algorithm is tested on a variety of test instances with different dynamic characteristics and difficulties. The comparisons of experimental results with other state-of-the-art algorithms demonstrate that the proposed algorithm is promising for dealing with dynamic multi-objective optimization.
An efficient host overload detection algorithm for cloud data center based on exponential weighted moving average

Summary The major aim of data center (DC) is to optimize the three parameters, viz., energy consumption, resource utilization, and quality of service (QoS). Our objective is to balance these three parameters by managing the virtual machine (VM) efficiently. Therefore, this paper presents a holistic view of VM consolidation and explains its constituent subprocesses: (i) host overload detection, (ii) VM selection for migration from the overloaded host, and (iii) VM placement for provisioning selected VMs on a new set of hosts. Later, this paper attempts to optimize the parameters mentioned above by proposing a host overload detection algorithm based on exponential weighted moving average (EWMA) and its variants. The performance of the proposed EWMA is compared with the state‐of‐the‐art algorithms based on (i) local regression, (ii) median absolute deviation, and (iii) interquartile range. The efficacy of the proposed EWMA is evaluated in combination with four different existing VM selection policies: (i) minimum migration time, (ii) minimum utilization, (iii) maximum correlation, and (iv) random selection. The evaluation metrics comprise energy consumption, VM migration count from the overloaded hosts representing the resource utilization, service level agreement (SLA) violations describing the QoS, and the average execution time. Simulation experiments are carried out on the CloudSim simulator using PlanetLab real cloud trace as workload. The proposed EWMA consumes 18.33% less energy, causes 47.81% less VM migration, makes 9.91% less SLA violation, and takes 43.44% less average‐time for the execution of the entire workload as compared to state‐of‐the‐art algorithms.
Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namely translating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods. The code is available at https://github.com/cchao0116/S2PNM-TKDE2021.
Smart gym trainer using Human pose estimation

Human pose estimation becomes a popular project today in the field of computer vision. The human pose estimation can be developed using Artificial Intelligence or Machine learning in which the system is fed with sample data or trained models and hence can localize joints in the human body over a video or an image. Now once the joints of the human body are localized we can use it for wide applications such as getting the gait cycle of a person walking or tracking down the movements of a professional athlete in order to understand the physical techniques and strategies involved to achieve his/her success. Thus one of the applications of Human pose estimation could be developing a smart gym trainer software, that could help struggling bodybuilders to achieve their goals.
Learning by sampling: learning behavioral family models from software product lines

Family-based behavioral analysis operates on a single specification artifact, referred to as family model, annotated with feature constraints to express behavioral variability in terms of conditional states and transitions. Family-based behavioral modeling paves the way for efficient model-based analysis of software product lines. Family-based behavioral model learning incorporates feature model analysis and model learning principles to efficiently unify product models into a family model and integrate the behavior of various products into a behavioral family model. Albeit reasonably effective, the exhaustive analysis of product lines is often infeasible due to the potentially exponential number of valid configurations. In this paper, we first present a family-based behavioral model learning techniques, called FFSMDiff. Subsequently, we report on our experience on learning family models by employing product sampling. Using 105 products of six product lines expressed in terms of Mealy machines, we evaluate the precision of family models learned from products selected from different settings of the T-wise product sampling criterion. We show that product sampling can lead to models as precise as those learned by exhaustive analysis and hence, reduce the costs for family model learning.
Nonhuman primate model in mammary gland biology and neoplasia research

Research on breast cancer pathogenesis, prevention and drug development remains an important field as this disease is still one of the leading causes of cancer death worldwide. Nonhuman primates, particularly macaque species, may serve as a highly translational animal model in breast cancer studies due to their similarity with humans in genetics, anatomy, reproductive and endocrine physiology including mammary gland development profile. The use of nonhuman primates in biomedical research, however, requires high ethical standards and an increasing expectation to improve strategies to replace, reduce and refine their use. Here, we discuss some key features of nonhuman primate mammary gland biology relevant to their strengths and limitations as models in studies of breast development and cancer risk.
Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time

In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images. Our approach lifts 2D detections to 3D space by predicting additional regression and classification parameters and hence keeping the runtime close to pure 2D object detection. The additional parameters are transformed to 3D bounding box keypoints within the network under geometric constraints. Our proposed method features a full 3D description including all three angles of rotation without supervision by any labeled ground truth data for the object's orientation, as it focuses on certain keypoints within the image plane. While our approach can be combined with any modern object detection framework with only little computational overhead, we exemplify the extension of SSD for the prediction of 3D bounding boxes. We test our approach on different datasets for autonomous driving and evaluate it using the challenging KITTI 3D Object Detection as well as the novel nuScenes Object Detection benchmarks. While we achieve competitive results on both benchmarks we outperform current state-of-the-art methods in terms of speed with more than 20 FPS for all tested datasets and image resolutions.
Automatic segmentation of three clinical target volumes in radiotherapy using lifelong learning

<h2>Abstract</h2><h3>Background and purpose</h3> Convolutional neural networks (CNNs) have comparable human level performance in automatic segmentation. An important challenge that CNNs face in segmentation is catastrophic forgetting. They lose performance on tasks that were previously learned when trained on task. In this study, we propose a lifelong learning method to learn multiple segmentation tasks continuously without forgetting previous tasks. <h3>Materials and methods</h3> The cohort included three tumors, 800 patients of which had nasopharyngeal cancer (NPC), 800 patients had breast cancer, and 800 patients had rectal cancer. The tasks included segmentation of the clinical target volume (CTV) of these three cancers. The proposed lifelong learning network adopted dilation adapter to learn three segmentation tasks one by one. Only the newly added dilation adapter (seven layers) was fine tuning for incoming new task, whereas all the other learned layers were frozen. <h3>Results</h3> Compared with single-task, multi-task or transfer learning, the proposed lifelong learning can achieve better or comparable segmentation accuracy with a DSC of 0.86 for NPC, 0.89 for breast cancer, and 0.87 for rectal cancer. Lifelong learning can avoid forgetting in sequential learning and yield good performance with less training data. Furthermore, it is more efficient than single-task or transfer learning, which reduced the number of parameters, size of model, and training time by ~58.8%, ~55.6%, and ~25.0%, respectively. <h3>Conclusion</h3> The proposed method preserved the knowledge of previous tasks while learning a new one using a dilation adapter. It could yield comparable performance with much less training data, model parameters, and training time.