Reviewerly/exHarmony huggingface dataset:
Genetic programming with separability detection for symbolic regression

Abstract Genetic Programming (GP) is a popular and powerful evolutionary optimization algorithm that has a wide range of applications such as symbolic regression, classification and program synthesis. However, existing GPs often ignore the intrinsic structure of the ground truth equation of the symbolic regression problem. To improve the search efficacy of GP on symbolic regression problems by fully exploiting the intrinsic structure information, this paper proposes a genetic programming with separability detection technique (SD-GP). In the proposed SD-GP, a separability detection method is proposed to detect additive separable characteristics of input features from the observed data. Then based on the separability detection results, a chromosome representation is proposed, which utilizes multiple sub chromosomes to represent the final solution. Some sub chromosomes are used to construct separable sub functions by using separate input features, while the other sub chromosomes are used to construct sub functions by using all input features. The final solution is the weighted sum of all sub functions, and the optimal weights of sub functions are obtained by using the least squares method. In this way, the structure information can be learnt and the global search ability of GP can be maintained. Experimental results on synthetic problems with differing characteristics have demonstrated that the proposed SD-GP can perform better than several state-of-the-art GPs in terms of the success rate of finding the optimal solution and the convergence speed.
Efficient 3D Video Engine Using Frame Redundancy

Traditional 3d video understanding methods process videos frame by frame. We argue that a lot of computation in this mechanism is redundant based on a key observation - adjacent frames in 3D videos have visually similar geometry structure. To handle the redundancy, we propose the Efficient 3D Video Engine (EVE), aiming to avoid the computation of redundant points. It consists of two modules: 1) redundancy removing module designed to detect redundancy and remove it; 2) residual learning module to extract features on non-redundant points. As a simple plug and play framework, EVE can be easily incorporated in mainstream 3D models. Experiments demonstrate that EVE can significantly reduce computation without performance loss on large scale datasets. On the other hand, with similar computation, EVE outperforms the strong baseline by up to 4.1 mIoU on SemanticKITTI. The code is available on https://github.com/ecr23xx/eve.
Autonomous Tracking For Volumetric Video Sequences

As a rapidly growing medium, volumetric video is gaining attention beyond academia, reaching industry and creative communities alike. This brings new challenges to reduce the barrier to entry from a technical and economical point of view. We present a system for robustly and autonomously performing temporally coherent tracking for volumetric sequences, specifically targeting those from sparse setups or with noisy output. Our system will detect and recover missing pertinent geometry across highly incoherent sequences as well as provide users the option of propagating drastic topology edits. In this way, affordable multi-view setups can leverage temporal consistency to reduce processing and compression overheads while also generating more aesthetically pleasing volumetric sequences.
Effect of augmented reality on school journalism: A tool for developing communication competencies in virtual environments

Abstract Augmented reality combines virtual and real elements within the same visual environment. It is urgently necessary that augmented reality be incorporated into public education in developing countries. This challenge requires governments to be committed to education. Nevertheless, within our nations, there exists an emerging body of expertise concerning the innovation of educational material utilizing Information and Communication Technology (ICT). The objective of this study is to explain how the use of augmented reality in school journalism affects the development of communication skills in virtual environments within the context of public secondary education in Latin America. This quasi‐experimental research study was conducted on two separate groups: an experimental group and a control group. The approach was a mix of qualitative and quantitative methodology. The results differed markedly between the two groups. The students in the experimental group developed the four communication skills in virtual environments at an average level of 95%, and they created an augmented reality magazine. Meanwhile, the control group did not even create a printed publication. It is concluded that the use of augmented reality makes a significant pedagogical contribution to the teaching/learning process of school journalism and that with the help of educational policies, this pedagogical contribution can be replicated in other schools.
Trajectory Tracking Control of a Drone-Guided Hose System for Fluid Delivery

In this paper, we present a nonlinear trajectory-tracking controller for a multicopter to deliver a pressurized fluid through an attached hose system. The three-dimensional (3D) dynamic model of the entire system is derived that incorporates multicopter dynamics, the reaction force from the release of the fluid, and the force applied by the weight of the hose. The magnitude of the fuid-release reaction force is calculated using Newton's second law. The force applied by the hose weight onto the mutlicopter dynamics is determined using a catenary approach for a low- tension cable. The nonlinear model predictive control (MPC) technique was applied to develop the controller that is able to drive the multicopter to track two predefined 3D trajectories while taking the operational constraints of the multicopter into account. The simulation results show the effectiveness of the MPC controller.
Modelling and Simulation of Autonomous Indoor Robotic Wastebin in Webots for Waste Management in Smart Buildings

Abstract Waste bins are being used in an indoor environment by every common man for over many decades. Proper disposal of the waste and the waste collection system in an indoor environment is an important aspect of the cleanliness of the environment, the outlook of the environment, and as well as considering the health aspects of the people. An improper waste collection system in an indoor environment leads to a foul smell and unhygienic condition, thus inherently results in environmental pollution. Another crucial challenge is devising a safe navigation methodology for the smart waste bin in the indoor environment and efficiently model and simulate its performance. Therefore, in this paper, Mobile Smart Waste Bin with a smart waste collection system is proposed using the technical advancements in robotic technology with the navigation techniques. A simple line following navigation technique is employed in such a way that, when the waste bin gets filled with trash, the corresponding bin autonomously moves to the common collection point. This is done to analyse the integration of the physical and intrinsic characteristics of the system through simulation studies and the results are reported. The performance of the proposed indoor robotic dustbin bin is modelled with the underlying physics as well as the environment in webots virtual simulator. The effectiveness of the system is analysed through simulation studies in webots simulator and the results are visualized and reported. Thus, the designed mobile smart bin and proposed waste collection system will be a better autonomous solution compared to existing ones in the indoor environment of Smart Buildings.
Board 60: PeerLogic: Web Services for Peer Assessment

Peer assessment means students giving feedback on each other's work.Dozens of online systems have been developed for peer assessment.All of them face similar issues.The PeerLogic project is an effort to develop specialized features for peer-assessment systems so that they can be used by multiple systems without the need for re-implementation.In addition, the project maintains a "data warehouse," which includes anonymized peer reviews from different peer-assessment systems, which are freely made available to researchers.
Detailed 3D Human Body Reconstruction From a Single Image Based on Mesh Deformation

In the research fields of 3D animation, virtual reality, etc., it is a very important task to reconstruct a 3D human body model with the surface textures.However, due to factors such as diversities of human body poses, uncertainty of depth, and a wide range of viewpoints, the reconstruction of the 3D human body from a single image is an enormous challenge.To solve the aforementioned challenge, this paper proposes a novel architecture to reconstruct the 3D human mesh with great surface details.The key point of the proposed architecture is to combine the topology of the SMPL (Skinned Multi-Person Linear) template mesh and the flexible mesh deformation.Firstly, the typical 2D CNN architecture, ResNet-50, extracts the perceptual features from the input image.The image features are fused into the 3D space via perceptual feature transformation.Our approach does not rely on the parameter space, although the mesh topology of the SMPL is preserved.The approach fully exploits the ability of the SMPL to represent various human body shapes, while avoiding the problem of the spatial loss caused by the parametric method.Secondly, to recover more details of the human body, a graph-based convolutional network is adopted to deform the 3D human body mesh.The network allows starting with fewer vertices and distributing vertex features to the representative positions in a coarse-to-fine manner, which fully utilizes the mesh topology.Experiments demonstrate that our approach can generate 3D human body mesh with great details qualitatively.Compared with the state-of-the-art methods, our approach also achieves better accuracy quantitatively.
A Deep Reinforcement Learning Method for Solving Task Mapping Problems with Dynamic Traffic on Parallel Systems

Efficient mapping of application communication patterns to the network topology is a critical problem for optimizing the performance of communication bound applications on parallel computing systems. The problem has been extensively studied in the past, but they mostly formulate the problem as finding an isomorphic mapping between two static graphs with edges annotated by traffic volume and network bandwidth. But in practice, the network performance is difficult to be accurately estimated, and communication patterns are often changing over time and not easily obtained. Therefore, this work proposes a deep reinforcement learning (DRL) approach to explore better task mappings by utilizing the performance prediction and runtime communication behaviors provided from a simulator to learn an efficient task mapping algorithm. We extensively evaluated our approach using both synthetic and real applications with varied communication patterns on Torus and Dragonfly networks. Compared with several existing approaches from literature and software library, our proposed approach found task mappings that consistently achieved comparable or better application performance. Especially for a real application, the average improvement of our approach on Torus and Dragonfly networks are 11% and 16%, respectively. In comparison, the average improvements of other approaches are all less than 6%.
Multiparty Homomorphic Machine Learning with Data Security and Model Preservation

With the widespread application of machine learning (ML), data security has been a serious issue. To eliminate the conflict between data privacy and computability, homomorphism is extensively researched due to its capacity of performing operations over ciphertexts. Considering that the data provided by a single party are not always adequate to derive a competent model via machine learning, we proposed a privacy-preserving training method for the neural network over multiple data providers. Moreover, taking the trainer’s intellectual property into account, our scheme also achieved the goal of model parameter protection. Thanks to the hardness of the conjugate search problem (CSP) and discrete logarithm problem (DLP), the confidentiality of training data and system model can be reduced to well-studied security assumptions. In terms of efficiency, since all messages are coded as low-dimensional matrices, the expansion rates with regard to storage and computation overheads are linear compared to plaintext implementation without accuracy loss. In reality, our method can be transplanted to any machine learning system involving multiple parties due to its capacity of fully homomorphic computation.
Survey on Copyright Laws about Music Generated by Artificial Intelligence

This paper observes existing legislation in the USA and other countries in the field of copyrights for computergenerated music and fine arts in general. Also, this work provides results of the survey aimed at determining the opinions of various countries citizens about authorship and copyrights to music generated by artificial intelligence. Further examined is the correspondence between the current state of laws and expectations and opinions of people. As a result, this research forms recommendations for computer-generated music copyright laws of countries, as mentioned earlier.
Balancing Data Utility versus Information Loss in Data-Privacy Protection using k-Anonymity

Data privacy has been an important area of research in recent years. Dataset often consists of sensitive data fields, exposure of which may jeopardize interests of individuals associated with the data. In order to resolve this issue, privacy techniques can be used to hinder the identification of a person through anonymization of the sensitive data in the dataset to protect sensitive information, while the anonymized dataset can be used by the third parties for analysis purposes without obstruction. In this research, we investigated a privacy technique, k-anonymity for different values of \pmbk on different number \pmbc of columns of the dataset. Next, the information loss due to k-anonymity is computed. The anonymized files go through the classification process by some machine-learning algorithms i.e., Naive Bayes, J48 and neural network in order to check a balance between data anonymity and data utility. Based on the classification accuracy, the optimal values of \pmbk and \pmbc are obtained, and thus, the optimal \pmbk and \pmbc can be used for k-anonymity algorithm to anonymize optimal number of columns of the dataset.
Gamification in Assembly Training: A Systematic Review

In response to the advancement of digitalization technology, the future of assembly works and associated training methods seem to evolve. Gamification, i.e. the use of game design elements in nongame contexts seem to give a potential application for training procedures in the context of assembly work. However, the study on this field is still very limited. Thus, we investigate the following topics pertaining to assembly works and gamification: (a) how is the future of industrial assembly; (b) what methods are currently used to train the assembly workers; and (c) is gamification prospective for such training. From 20 out of 53 related studies, we eventually found the most relevant literatures. The results indicate that traditional training has not met the requirements of future trends, while augmented reality training methods, may offer extra benefits than the counterparts. The concept of gamification in which operators are directly involved, appropriate with skill-based assembly work requiring hands on experience. Further cognitive considerations and physiological measurements during experiment are required to improved HCI assembly work training systems design, especially within the gamification element context.
Secret sharing schemes for infinite sets of participants: A new design technique

We propose a new design technique for constructing secret sharing schemes over a potentially infinite set of participants. Our findings leverage on a nice property of secret sharing schemes for finite sets of participants based on the Chinese remainder theorem: the possibility of providing shares of different sizes to participants. We successful apply the technique to the (3,∞)-threshold access structure. The scheme we exhibit improves over the best construction currently available. Most importantly, the idea underlying the technique is of independent interest. Hopefully, it could be employed for other access structures, and in other areas of secure computation for potentially infinite sets of players.
An efficient particle swarm optimization with homotopy strategy for global numerical optimization

Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the PSO, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework.
A Short Survey of LSTM Models for De-identification of Medical Free Text

The confidentiality of patient information is legislated by governmental regulations in various countries, such as the Health Insurance Portability and Accountability Act (HIPAA) standards in the USA. Under these laws, adequate protections must be in place to safeguard patients' health records, which are often big data comprised of free text. Machine learning approaches are extensively used for the automated de-identification of medical free text, with outstanding results obtained from several studies that incorporate long short-term memory (LSTM) networks. These networks are a variant of the recurrent neural network (RNN) architecture. Our survey of LSTM models dates back five years, and the contribution of the findings is appreciable. Performance-wise, LSTMs generally surpassed other types of models used in automated de-identification of free text, namely conditional random field (CRF) algorithms and rule-based algorithms. In addition, hybrid or ensemble LSTM models did not outperform LSTM -only models. Finally, we note that the customization of gold-standard, de-identification datasets may result in overfitted models.
Robust Multispectral Pedestrian Detection via Uncertainty-Aware Cross-Modal Learning

With the development of deep neural networks, multispectral pedestrian detection has been received a great attention by exploiting complementary properties of multiple modalities (e.g., color-visible and thermal modalities). Previous works usually rely on network prediction scores in combining complementary modal information. However, it is widely known that deep neural networks often show the overconfident problem which results in limited performance. In this paper, we propose a novel uncertainty-aware cross-modal learning to alleviate the aforementioned problem in multispectral pedestrian detection. First, we extract object region uncertainty which represents the reliability of object region features in multiple modalities. Then, we combine each modal object region feature considering object region uncertainty. Second, we guide the classifier of detection framework with soft target labels to be aware of the level of object region uncertainty in multiple modalities. To verify the effectiveness of the proposed methods, we conduct extensive experiments with various detection frameworks on two public datasets (i.e., KAIST Multispectral Pedestrian Dataset and CVC-14).
Video quality evaluation toward complicated sport activities for clustering analysis

Automatically clustering various sophisticated human activities (e.g., dancing, martial arts, and gymnastics) based on their quality scores is an indispensable technique in physical training, human–computer interaction, etc. Conventionally, many action recognition models are built upon the visual/semantic appearance of human body movements. Recently, due to the introduction of Microsoft Kinect, many skeleton-based human action understanding frameworks have been proposed. In this work, we propose a novel method to cluster the quality of complicated human actions towards contactless operative video reading system (COVRS). More specifically, we first extract the skeleton by leveraging the Kinect, which is subsequently fed into an aggregation deep neural network to extract the deep feature for each human action skeleton. In COVRS, the human hand gesture is an informative clue. Thus, we propose a ranking algorithm to extract the position of human five figures, based on which the deep hand gesture representation is hierarchically learned. Noticeably, it is observable that, the acoustic feature from many human activities also contributes to the quality assessment. We extract multiple acoustic features from the audio associated with each human activity video. Finally, based on the above human skeleton and hand gesture deep features, as well as the shallow acoustic features, we employ a probabilistic model to integrate them for clustering the various human activities using the quality of COVRS. Comprehensive experimental have demonstrated the effectiveness and efficiency of our method. Besides, empirical results have shown that our probabilistic quality model is highly extensible, where additionally visual/acoustic features can be encoded according to different applications.
Digitalization of Education: Conservatism and Innovative Development

The article deals with a problem of digitalization in the sphere of education in terms of the unity of its conservative and innovative principles, as well as identifying the positive and negative consequences of this process. The totally complex digitalization of the educational and professional sphere has led to the development of a new format for human existence. Among the positive aspects of digitalization, the article authors note teaching students to independence and self-organization, taking into account the individual characteristics of the student, developing breakthrough technologies in education, etc. The authors attribute the following features to the negative consequences of this process: a decrease in the possibilities of creative, mental and physical development, a decrease in the quality of socialization, the development of clip thinking, etc. While analyzing the massive invasion of digital technologies in the field of education, special attention is paid to the substantiation of the ways of resistance and minimization of its negative consequences.