Reviewerly/exHarmony huggingface dataset:
SwiVR-Car-Seat

Autonomous vehicles provide new input modalities to improve interaction with in-vehicle information systems. However, due to the road and driving conditions, the user input can be perturbed, resulting in reduced interaction quality. One challenge is assessing the vehicle motion effects on the interaction without an expensive high-fidelity simulator or a real vehicle. This work presents SwiVR-Car-Seat, a low-cost swivel seat to simulate vehicle motion using rotation. In an exploratory user study (N=18), participants sat in a virtual autonomous vehicle and performed interaction tasks using the input modalities touch, gesture, gaze, or speech. Results show that the simulation increased the perceived realism of vehicle motion in virtual reality and the feeling of presence. Task performance was not influenced uniformly across modalities; gesture and gaze were negatively affected while there was little impact on touch and speech. The findings can advise automotive user interface design to mitigate the adverse effects of vehicle motion on the interaction.
HPGCN: Hierarchical poselet-guided graph convolutional network for 3D pose estimation

3D pose estimation remains a challenging task since human poses exhibit high ambiguity and multi-granularity. Traditional graph convolution networks (GCNs) accomplish the task by modeling all skeletons as an entire graph, and are unable to fuse combinable part-based features. By observing that human movements occur due to part of human body (i.e. related skeletons and body components, known as the poselet) and those poselets contribute to each movement in a hierarchical fashion, we propose a hierarchical poselet-guided graph convolutional network (HPGCN) for 3D pose estimation from 2D poses. HPGCN sets five primitives of human body as basic poselets, and constitutes high-level poselets according to the kinematic configuration of human body. Moreover, HPGCN forms a fundamental unit by using a diagonally dominant graph convolution layer and a non-local layer, which corporately capture the multi-granular feature of human poses from local to global perspective. Finally HPGCN designs a geometric constraint loss function with constraints on lengths and directions of bone vectors, which help produce reasonable pose regression. We verify the effectiveness of HPGCN on three public 3D human pose benchmarks. Experimental results show that HPGCN outperforms several state-of-the-art methods.
A flexible approach for virtual machine selection in cloud data centers with AHP

Abstract Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic. Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi‐criteria decision‐making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers.
Overcoming challenges when teaching hands-on courses about Virtual Reality and Augmented Reality: Methods, techniques and best practice

This paper presents methods and techniques for teaching Virtual Reality (VR) and Augmented Reality (AR) that were conceived and refined during more than 20 years of our teaching experience on these subjects in higher education. We cover a broad spectrum from acquainting learners with VR and AR as only one aspect of a more general course to an in-depth course on VR and AR during a whole semester. The focus of the paper is methods and techniques that allow learners to not only learn about VR and AR on a theoretical level but that facilitate their own VR and AR experiences with all senses and foster hands-on learning. We show why this is challenging (e.g., the high workload involved with the preparation of hands-on experiences, the large amount of course time that needs to be devoted), and how these challenges can be met (e.g., using our Circuit Parcours Technique). Moreover, we discuss learning goals that can be addressed in VR and AR courses besides hands-on experiences when using our methods and techniques. Finally, we provide best practice examples that can be used as blueprints for parts of a VR and AR course.
Design of a class of generalized parallel mechanisms for adaptive landing and aerial manipulation

In this paper, the concept of adaptive landing and aerial manipulation mechanism (ALAM) is proposed. The mechanism can be installed on a rotary-wing unmanned aerial vehicle (UAV), allowing the UAV to land on uneven terrain and perform aerial manipulation. Based on the Lie group, the design principles of the ALAM are proposed by combining the advantages of the generalized parallel mechanism. The design procedure of the mechanism is introduced. Then, the structure synthesis is performed for the ALAMs with 1 to 3 degrees of freedom (DOF) generalized moving platform. In order to evaluate the landing performance of the mechanism, two general indexes for evaluating landing adaptability and stability are established based on three typical structured terrains. Multi-objective parameter optimization of a 3T1R ALAM is carried out. Finally, a prototype is developed and validated for its workspace range and landing performance.
Fast and large-converge-radius inverse compositional Levenberg–Marquardt algorithm for digital image correlation: principle, validation, and open-source toolbox

This paper presents an inverse compositional Levenberg-Marquardt (IC-LM) algorithm for robust, efficient, and accurate image registration in digital image correlation (DIC). In essence, the IC-LM algorithm is a mixture of the classical inverse compositional Gaussian-Newton (IC-GN) and gradient descent algorithms. Further normalization of the local coordinate and image intensity is also introduced to adaptively initialize the damping parameter in the IC-LM algorithm. The proposed IC-LM algorithm is proven to hold a larger converge radius while having comparable accuracy, precision, and efficiency compared with the classical IC-GN algorithm. The efficient reliability-guided displacement tracking strategy is also merged into the IC-LM algorithm to provide an accurate initial guess for all calculation points. For the sake of reproducibility of this algorithm, the open-source MATLAB toolbox featuring the IC-LM algorithm is available on GitHub (https://github.com/cbbuaa/DIC_ICLM_MATLAB).
Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm

One way to increase immunity and maintain immunity can be done by consuming herbal plants. This herbal medicine is empirically believed to be useful as a cultural treasure from generation to generation. All parts of the plant can be used as medicine, one of which is the leaves. However, most people do not know the herbal leaves. This herbal leaf can actually be recognized from the characteristics of its shape. This study aims to identify types of herbal leaves using first-order feature extraction and the Multiclass Support Vector Machine (Multiclass SVM) algorithm. First-order feature extraction is able to extract features using the parameters of mean, skewness, variance, kurtosis, and entropy. Meanwhile, Multiclass SVM identifies by obtaining the optimal line in separating the data set of two classes of two-dimensional space points in order to find the maximum hyperplane in separating the data points into classes so that they can be grouped. From the test results, the identification accuracy is an average of 76&#x0025;. This shows that the algorithm has been able to identify, but needs improvement.
Intelligent Yoga Coaching System Based on Posture Recognition

Nowadays, yoga as a popular sport has received more and more people’s attention. However, many people do not have enough time and suitable places to practice yoga. In recent years, the development of posture estimation methods has provided convenience for intelligent yoga coaches. This research aims to develop a yoga training system based on real-time pose estimation. The system is composed of a posture recognition network, a yoga standard movement posture library, a yoga movement correction algorithm and a system UI interface. Taking the real-time human motion video stream collected by the camera as input, the gesture recognition network based on OpenPose is used to extract the joint points of human gestures. According to our proposed correction algorithm based on entropy weight, real-time feedback and correction suggestions can be obtained. Experimental results show that this method can show high real-time performance on home computers and high accuracy on real data sets.
PRESTO: Predictive REcommendation of Surrogate models To approximate and Optimize

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate. Many techniques exist for surrogate modeling; however, selecting suitable techniques for a given application remains an open challenge. This work describes PRESTO, a Random Forest classifier-based tool, to recommend appropriate surrogate modeling techniques for a given dataset for surface approximation and surrogate-based optimization, using attributes calculated only using the input and output data. The tool identifies the techniques for surface approximation with an accuracy of 91% and a precision of 90% and for surrogate-based optimization with an accuracy of 98% and a precision of 99%. PRESTO was tested on data generated from a high fidelity process model of the cumene production process. Its performance on this case study was comparable to the training data. PRESTO enables computational time savings for selecting surrogate model forms by avoiding expensive trial-and-error methods.
Robust speech watermarking by a jointly trained embedder and detector using a DNN

This paper utilizes deep neural networks for robust digital watermarking and authentication of speech signals. We present two adversarial neural networks, called embedder and detector. The embedder network tends to achieve imperceptible watermark embedding by minimizing the differences between the original and watermarked signals. The detector strives towards errorless watermark detection. We have proposed joint optimization of these two networks to achieve a trade-off between these requirements. Two models are trained, one with raw speech signals and another with STFT signal representations. Proposed techniques are tested on attacked audio recordings with measures such as SNR, PESQ, BER, and bps. The proposed system achieves high robustness to common watermarking attacks with BERs well below 1%. SNR values greater than 38 dB and PESQ values of 4.33 demonstrate that the difference in original and watermarked signals is negligible. In comparison with other approaches, our scheme shows good overall performance in terms of imperceptibility, robustness, and capacity.
DNN compression by ADMM-based joint pruning

The success of deep neural networks (DNNs) has motivated pursuit of both computationally and memory efficient models for applications in resource-constrained systems such as embedded devices. In line with this trend, network pruning methods reducing redundancy in over-parameterized models are being studied actively. Previous works on this research have demonstrated the ability to learn a compact network by imposing sparsity constraints on the parameters, but most of them have difficulty not only in identifying both connections and neurons to be pruned, but also in converging to optimal solutions. We propose a systematic DNN compression method where weights and network architectures are jointly optimized. We solve the joint problem using alternating direction method of multipliers (ADMM), a powerful technique capable of handling non-convex separable programming. Additionally, we provide a holistic pruning approach, an integrated form of our method, for automatically pruning networks without specific layer-wise hyper-parameters. To verify our work, we deployed the proposed method to a variety of state-of-the-art convolutional neural networks (CNNs) on three image classification benchmark datasets: MNIST, CIFAR-10, and ImageNet. Results show that the proposed pruning method effectively compresses the network parameters and reduces the computation cost while preserving prediction accuracy.
Protecting the University as a Physical Place in the Age of Postdigitization

Covid-19 forced higher education sectors across the world to digitize the entire university experience online. There are now calls for universities to continue chasing continued and further digitization, often from for-profit businesses and those in Silicon Valley who have been promising to disrupt the sector for decades. We argue that the pandemic has illustrated how crucial universities are to their local communities, and efforts should be made to emphasize their physical place and space. The destruction of American cities in favor of auto-centric suburbs provides a parallel for the possible future of higher education. The Cult of Efficiency mindset and accountability models that dominated neoliberal discourse offered the impetus for highway construction through city centers, often razing Black neighborhoods and ruining communities and culture along the way. The calls for the full digitization of universities echo this same possible destruction for the sector. This is not a Luddite warning to reject all digitization, instead, it is a rejection of the hyper-capitalization of higher education and the disruption promised by for-profit businesses, along with a reminder that the sector should be a local public good.
Software product line scoping: A systematic literature review

Software product line (SPL) scoping aids companies to define the boundaries of their resources such as products, domains, and assets, the target of reuse tasks scoping technical and organizational aspects. As scoping guides the management of the resources in SPL development, it becomes one of the core activities in this process. We can find in the literature several approaches on this topic, proposing techniques and methodologies to be applicable in different organizational scenarios. However, no work comprehensively reviews such approaches and describes the advances in state of the art in the last years. In this context, we look into identifying, analyzing, and extracting detailed characteristics from SPL scoping proposals found in the literature. These characteristics allowed us to compare these approaches, reason about their applicability, and identify existing limitations and research opportunities. Thus, we conducted a systematic literature review alongside snowballing, following a well-defined protocol to retrieve, classify and extract information from the literature. We analyzed a total of 58 studies, identifying 41 different approaches in the field, highlighting their similarities and differences, and establishing a generic scoping process. Furthermore, we discuss research opportunities in the SPL scoping field.
Vyayam: Artificial Intelligence based Bicep Curl Workout Tacking System

As a famous saying goes “Exercise not only changes our body it changes our mind, attitude, and mood”. Fitness is being a trend today. Everyone wants to be fit, beautiful, and healthy. But during this pandemic, everyone can't hire a trainer or go to a gym. Another option is wearable devices in which everyone can't afford it. This paper proposed an AI Trainer model. The proposed model used by anyone irrespective of their age and health condition. The AI Model uses Human Pose Estimation. It is a popular approach and it determines the position and orientation of the human body. This approach generates key points on the human body and based on that it creates a virtual skeleton in 2D dimension. The input is the live video which is taken from a person's webcam and the output is capturing landmarks or key points on the human body. The AI Trainer specifies the count and time of the settings the person needs to perform. It also specifies mistakes and feedback if any. This paper provides a methodology to use the pose estimation running on the CPU to find the correct points. Based on the points the gestures and other curls (biceps) are calculated. This paper proposes an approach using OpenCV to implement human pose estimation.
Application of the Deep Learning Algorithm to Identify the Spatial Distribution of Heavy Metals at Contaminated Sites

Accurate estimation of the spatial distribution of pollutants, such as heavy metals, is indispensable in investigation of contaminated sites. In this paper, we develop a novel deep learning (DL) algorithm, in which the data value of the target point is predicted by a nearest-neighbor neural network. Using 20 hypothetical sites and a real-world one, the developed DL is compared with several commonly used kriging algorithms (e.g., ordinary kriging). By taking the results of ordinary kriging as the benchmark, the interpolation accuracy can be improved by an average of 38.2% and 11.2–36.6%, respectively. The comparisons also indicate that the new method performs much better in cases with significant spatial variability by alleviating the smoothing effect and the edge effect in the kriging. Upon further examination of the developed method, the prediction accuracy is found to first increase and then decrease with the number of neighbor points. Moreover, the influence of the sampling density is limited if the number exceeds a certain threshold (e.g., n = 64 in our case). As a preliminary attempt of applying the DL algorithm at individual contaminated sites, this work provides a general alternative method to identify the spatial distribution of heavy metals.
Sentiment Analysis for COVID-19 Tweets Using Recurrent Neural Network (RNN) and Bidirectional Encoder Representations (BERT) Models

In the last few decades, social media usage has exponentially increased, and people often share information covering various topics of interest. The social media platforms such as Twitter allow users to share images, audio, videos, and text. The textual content can be used as a powerful tool for sentiment analysis. The main goal of this work is to investigate the deep learning models for sentiment analysis of tweets related to COVID-19. The dataset was obtained using tweeter web API between December 20, 2019, to December 15, 2020, and labels were assigned manually as positive, negative, or neutral. Two deep learning models were selected for sentiment analysis: Recurrent Neural Networks (RNN) and the Bidirectional Encoder Representations (BERT) model. The experimental results showed that both RNN and BERT models were effective for sentiment analysis, resulting in 86.4% and 83.14% accuracy, respectively.
‘Address and command’: Two-handed mid-air interactions with multiple home devices

• The A&C approach enables two-handed mid-air interactions with multiple devices • An elicitation study identified A&C gestures for 7 home devices and 12 commands • A spatial AR prototype was developed to test the approach • The usability, memorability and UX was empirically evaluated for 36 tasks • A&C interactions are usable, easy to learn and valued in terms of user experience Mid-air gestural interaction with multiple targets (or devices) in the physical space presents several challenges: gesture design gets more complex in the attempt to avoid gesture conflicts and accidental activations; the potential commands are many but the gesture set must be kept small to enable memorability; and, from a design-process perspective, there are not readily available technologies for implementation and prototyping. In this paper, we present the Address and Command (A&C) interaction model that enables two-handed mid-air interactions with multiple remote devices, in a research approach that includes gesture elicitation, prototyping, and evaluation of the user experience. The A&C model requires that users employ the non-dominant hand to address a device (address gestures) and the dominant hand to provide a command to it (command gestures). This approach affords interactions that simultaneously address and command multiple devices in ubiquitous environments that respond to mid-air input. A&C interactions also afford the design of a single gesture for the same command when applicable for multiple devices, thus decreasing the total number of gestures to be memorized and promoting end-user learning of the gesture set. We have (a) conducted an elicitation study ( n = 18) to define A&C gestures for seven smart home devices and twelve commands; (b) developed a spatial augmented reality prototype that responds to mid-air gesture commands exploiting MS Kinect sensor and Visual Gesture Builder, Unity 3D and projection-mapping technologies onto foam mockups; (c) tested the approach and the gesture vocabulary in terms of usability, memorability and user experience at a scenario of 36 tasks among devices ( n = 17). We found that A&C interactions are feasible, fast, error-free, easy to learn and remember, and are highly valued in terms of user experience.
Sentiment Analysis of Review Data Using Blockchain and LSTM to Improve Regulation for a Sustainable Market

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.