Reviewerly/exHarmony huggingface dataset:
Designing and Implementing VR<sup>2</sup>E<sup>2</sup>C, a Virtual Reality Remote Education for Experimental Chemistry System

In this work, we introduce the VR2E2C system for Virtual Reality Remote Education for Experimental Chemistry. It aims to increase scientific literacy about remote education of theoretical and experimental chemistry among the public and high school students. It provides a laboratory option for students with disabilities who are keen on chemistry experiments but have limited mobility. VR2E2C provides three modes to accomplish the training: users can choose to control the intelligent robot to finish the experimental operations by instruction or choose to watch the experiments automatically conducted by VR2E2C. The experimental results can be delivered to users in real time or later. This system provides remote education for safe and high fault-tolerant experimental topics, demonstrating how the latest VR and robotics technology could benefit traditional chemical education.
Darknet Traffic Classification using Machine Learning Techniques

A Darknet is an overlay network within the Internet, and packets' traffic originating from it is usually termed as suspicious. In this paper common machine learning classification algorithms are employed to identify Darknet traffic. A ROC analysis along with a feature importance analysis for the best classifier was performed, to provide a better visualisation of the results. The experiments were conducted in the new dataset CIC-Darknet2020 and the classifiers were trained to both binary and multiclass classification. In the first classification task, there were two classes: "Benign" and "Darknet", whereas in the second there were four classes: "Tor", "Non Tor", "VPN" and "Non VPN". An average prediction accuracy of over 98% was achieved with the implementation of Random Forest algorithm for both classification tasks. This is the first work, to the best of our knowledge providing a comprehensive performance evaluation of machine learning classifiers employed for Darknet traffic classification in the new dataset CIC-Darknet2020.
Fighting Class Imbalance with Contrastive Learning

Medical image datasets are hard to collect, expensive to label, and often highly imbalanced. The last issue is underestimated, as typical average metrics hardly reveal that the often very important minority classes have a very low accuracy. In this paper, we address this problem by a feature embedding that balances the classes using contrastive learning as an alternative to the common cross-entropy loss. The approach is largely orthogonal to existing sampling methods and can be easily combined with those. We show on the challenging ISIC2018 and APTOS2019 datasets that the approach improves especially the accuracy of minority classes without negatively affecting the majority ones.
Evaluation Model of College English Multimedia Teaching Effect Based on Deep Convolutional Neural Networks

With the acceleration of global integration, the demand for English instruction is increasingly rising. On the other hand, Chinese English learners struggle to learn spoken English due to the limited English learning environment and teaching conditions in China. The advancement of artificial intelligence technology and the advancement of language teaching and learning techniques have ushered in a new era of language learning and teaching. Deep learning technology makes it possible to solve this problem. Speech recognition and assessment technology are at the heart of language learning, and speech recognition technology is the foundation. Because of the complex changes in speech pronunciation, a large amount of speech signal data, the high dimension of speech characteristic parameters, and a large amount of speech recognition and evaluation computation, the large volume of speech signal processing requires higher requirements of hardware and software resources and algorithms. However, traditional speech recognition algorithms, such as dynamic time-warped algorithms, hidden Markov models, and artificial neural networks, have their advantages and disadvantages. They have encountered unprecedented bottlenecks, so it is difficult to improve their accuracy and speed. To solve these problems, this paper focuses on evaluating the multimedia teaching effect of college English. A multilevel residual convolutional neural network algorithm for oral English pronunciation recognition is proposed based on a deep convolutional neural network. The experiments show that our algorithm can assist learners in identifying inconsistencies between their pronunciation and standard pronunciation and correcting pronunciation errors, resulting in improved oral English learning performance.
Multi-qubit correction for quantum annealers

Abstract We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels—i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value—and successively converges to the ground state. Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as spin-reversal transforms, classical postprocessing techniques, and increased inter-sample delay between successive measurements.
Private Set Intersection With Authorization Over Outsourced Encrypted Datasets

Thanks to its convenience and cost-savings feature, cloud computing ushers a new era. Yet its security and privacy issues must not be neglected. Private set intersection (PSI) is useful and important in many cloud computing applications, such as document similarity, genetic paternity and data mining. The cloud server performs intersection operations on two outsourced encrypted datasets of data owners. In the existing protocols, however, data owners cannot decide whether to use all or part of their encrypted data to compute the intersection, nor can they specify whom to compare with. In this paper, we introduce an enhanced notion of outsourced PSI, called authorized PSI (APSI), which supports flexible authorization and cross-type authorized comparison of datasets. To demonstrate this notion, we propose a concrete APSI protocol, and prove it to be secure in the random oracle model based on simple number-theoretic assumptions. Experimental results show that our APSI protocol has performance comparable with existing related outsourced PSI protocols.
Efficient mediated semi-quantum key distribution

Mediated semi-quantum key distribution (MSQKD) allows two "classical" participants to establish a secret key with the help of an untrusted quantum third party (TP). In all existing MSQKD protocols, the quantum capability required by TP is high and thus it needs much cost in a real implementation. In this paper, we propose an efficient MSQKD protocol, where TP only needs to prepare and measure qubits in the X basis and two "classical" participants just have the ability to prepare and measure qubits in the Z basis. Compared with other similar MSQKD protocols, the proposed protocol reduces the capability requirements of TP without sacrificing the qubit efficiency.
Domain Adaptive Video Segmentation via Temporal Consistency Regularization

Video semantic segmentation is an essential task for the analysis and understanding of videos. Recent efforts largely focus on supervised video segmentation by learning from fully annotated data, but the learnt models often experience clear performance drop while applied to videos of a different domain. This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) for consecutive frames of target-domain videos. DA-VSN consists of two novel and complementary designs. The first is cross-domain TCR that guides the prediction of target frames to have similar temporal consistency as that of source frames (learnt from annotated source data) via adversarial learning. The second is intra-domain TCR that guides unconfident predictions of target frames to have similar temporal consistency as confident predictions of target frames. Extensive experiments demonstrate the superiority of our proposed domain adaptive video segmentation network which outperforms multiple baselines consistently by large margins.
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling

Reconstructing a high-precision and high-fidelity 3D human hand from a color image plays a central role in replicating a realistic virtual hand in human-computer interaction and virtual reality applications. The results of current methods are lacking in accuracy and fidelity due to various hand poses and severe occlusions. In this study, we propose an I2UV-HandNet model for accurate hand pose and shape estimation as well as 3D hand super-resolution reconstruction. Specifically, we present the first UV-based 3D hand shape representation. To recover a 3D hand mesh from an RGB image, we design an AffineNet to predict a UV position map from the input in an image-to-image translation fashion. To obtain a higher fidelity shape, we exploit an additional SRNet to transform the low-resolution UV map outputted by AffineNet into a high-resolution one. For the first time, we demonstrate the characterization capability of the UV-based hand shape representation. Our experi ments show that the proposed method achieves state-of-the-art performance on several challenging benchmarks.
FP-NAS: Fast Probabilistic Neural Architecture Search

Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture. In contrast, Probabilistic NAS, such as PARSEC, learns a distribution over high-performing architectures, and uses only as much memory as needed to train a single model. Nevertheless, it needs to sample many architectures, making it computationally expensive for searching in an extensive space. To solve these problems, we propose a sampling method adaptive to the distribution entropy, drawing more samples to encourage explorations at the beginning, and reducing samples as learning proceeds. Furthermore, to search fast in the multivariate space, we propose a coarse-to-fine strategy by using a factorized distribution at the beginning which can reduce the number of architecture parameters by over an order of magnitude. We call this method Fast Probabilistic NAS (FP-NAS). Compared with PARSEC, it can sample 64% fewer architectures and search 2.1× faster. Compared with FBNetV2, FP-NAS is 1.9× - 3.5 ×faster, and the searched models outperform FBNetV2 models on ImageNet. FP-NAS allows us to expand the giant FBNetV2 space to be wider (i.e. larger channel choices) and deeper (i.e. more blocks), while adding Split-Attention block and enabling the search over the number of splits. When searching a model of size 0.4G FLOPS, FP-NAS is 132× faster than EfficientNet, and the searched FP-NAS-L0 model outperforms EfficientNet-B0 by 0.7% accuracy. Without using any architecture surrogate or scaling tricks, we directly search large models up to 1.0G FLOPS. Our FP-NAS-L2 model with simple distillation out-performs BigNAS-XL with advanced inplace distillation by 0.7% accuracy using similar FLOPS.
Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks

Production plants are being re-designed to implement human-centered solutions. Especially considering high added-value operations, robots are required to optimize their behavior to achieve a task quality at least comparable to the one obtained by the skilled operators. A manual programming and tuning of the manipulator is not an efficient solution, requiring to adopt towards automated strategies. Adding external sensors (e.g., cameras) increases the robotic cell complexity and it doesn't solve the issue since it is usually difficult to build explicit reward functions measuring the robot performance, while it is easier for the user to define a qualitative comparison between two experiments. According to these needs, in this letter, the recently-developed preferences-based optimization approach GLISp is employed and adapted to tune the novel developed path-based velocity planner. The implemented solution defines an intuitive human-centered procedure, capable of transferring (through pairwise preferences between experiments) the task knowledge from the operator to the manipulator. A Franka EMIKA panda robot has been employed as a test platform to perform a robotic sealing task (i.e., material deposition task), validating the proposed methodology. The proposed approach has been compared with a programming by demonstration approach, and with the manual tuning of the path-based velocity planner. Achieved results demonstrate the improved deposition quality obtained with the proposed optimized path-based velocity planner methodology in a limited number of experimental trials (20).
Immersive construction detailing education: building information modeling (BIM)–based virtual reality (VR)

Purpose This research proposes a virtual reality (VR) platform for construction detailing that provides experiential learning in a zero-risk environment. It builds on integrating VR technology as a medium and building information modeling (BIM) as a repository of information and a learning tool. Design/methodology/approach This work discusses the proposed environment curricular unit prototype design, implementation and validation. The validation of the VR environment was conducted in three phases, namely, piloting, testing (system usability and immersion) and learning gain validation, each of which has its aim and outcomes and has been assessed both qualitatively and quantitatively. Findings After considering the feedback, the VR environment prototype is then validated on the level of learning outcomes, providing the evidence that it would enhance students' engagement, motivation and achievement accordingly. The results indicated 30% learning progress after experiencing the VR environment vs. 13.8% for paper-based studying. Originality/value In reference to building construction education, construction site visits provide students with real-life practical experience which are considered an extension for classroom. Nevertheless, it is challenging to integrate construction site visits regularly during the academic semester with respect to the class specific needs. The research at hand adopts integrating VR and BIM in AEC (Architecture, Engineering and Construction) education by proposing a system that can work as a mainstream complementary construction detailing learning method for architecture students. The proposed VR system facilitates a virtual construction site that meets the learning needs where students can explore and build in a real scale environment.
Sending or not? A multimodal framework for Danmaku comment prediction

Danmaku is an emerging comment design for videos that allows real-time, interactive comments from viewers. Danmaku increases viewers’ interaction with other viewers and streamers, thereby raising viewers’ loyalty and sense of belonging. Sending Danmaku comments demonstrates a higher degree of viewer involvement than traditional static comments below the videos. Therefore, it is necessary and meaningful to learn about viewers’ preferences by observing their behavior, as this may benefit the platform as well as the streamers. However, research on how the multimodal environment affects viewers’ behavior in sending Danmaku comments is quite limited. To fill this gap, we propose a new dataset and a deep neural network integrating multimodal information to predict whether viewers will send Danmaku comments (Deep Multimodal network for Danmaku Forecasting, DMDF) in order to evaluate the impact of the interaction of textual features, audio features and visual features on the behavior of viewers sending Danmaku comments. A series of experimental results based on a real dataset of 249657 samples from Bilibili (a leading Chinese video streaming Website) demonstrate the effectiveness of the proposed DMDF and the helpfulness of all modalities, especially visual and acoustic features, in behavior forecasting. DMDF with the multimodal squeeze-and-excitation (MSE) module we proposed achieves 90.14% on accuracy and 83.60% on F1-score, and it reveals the extent to which a user-generated video can influence viewers to send Danmaku comments, which helps predict viewers’ online viewing behavior. Furthermore, our model contributes to the current work on the video understanding task.
A loosely-coupled EMD-denoised stereo VO/INS/GPS integration system in GNSS-denied environments

Although the Inertial Navigation System (INS) and Global Positioning System (GPS) integration framework has many advantages compared to the standalone INS and GPS systems, it suffers from limited access to GPS signals in GPS-denied environments. In this study, we propose a loosely-coupled Empirical Mode Decomposition (EMD)-denoised stereo Visual Odometry (VO)/INS/GPS integration system to address the problem of blocking GPS signals in the INS/GPS navigation system in GPS challenging environments. Furthermore, we provide the new versions of the EMD called Clear Iterative EMD Interval-Thresholding (EMD-CIIT) and EMD Hurst exponents before data fusion to enhance the signal-to-noise ratio of the inertial sensor measurements. To evaluate the effectiveness of the proposed method, we utilize the Kitti dataset. The results represent that the suggested framework has a superior performance in terms of accuracy and stability compared with other traditional methods; so that the achieved experimental accuracies verify that our presented idea outperforms INS/GPS by approximately 58%.
Model-based intelligent user interface adaptation: challenges and future directions

Abstract Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.
Low and non-uniform illumination color image enhancement using weighted guided image filtering

Abstract In the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.
A Circular-Structured Representation for Visual Emotion Distribution Learning

Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks. Since human emotions are ambiguous and subjective, it is more reasonable to address VEA in a label distribution learning (LDL) paradigm rather than a single-label classification task. Different from other LDL tasks, there exist intrinsic relationships between emotions and unique characteristics within them, as demonstrated in psychological theories. Inspired by this, we propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes (i.e., emotion polarity, emotion type, emotion intensity) as well as two properties (i.e., similarity, additivity). Besides, we design a novel Progressive Circular (PC) loss to penalize the dissimilarities between predicted emotion vector and labeled one in a coarse-to-fine manner, which further boosts the learning process in an emotion-specific way. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.
Impedance-based Control for Soft UAV Landing on a Ground Robot in Heterogeneous Robotic System

In this paper, we present a new method for soft landing of an unmanned aerial vehicle (UAV) on a ground robot based on impedance control. It is applied for a heterogeneous robotic system aimed at warehouse stocktaking automation. We describe the operating and mathematical principles of the impedance control for the landing system of the UAV and present the results of the real-world experiments. We evaluate the softness of landing using force sensors attached to each of four legs of the drone by measuring the impact force during the contact. Experimental results revealed that proposed landing impedance control decreased the impact force by 60%. The proposed approach can also be applied for delivery drone landing on the moving truck, cargo ship, or uneven terrain providing low risk of crash accident.