Reviewerly/exHarmony huggingface dataset:
Rendering for 3D Light Field composed of 360° Images

Light Field (LF) is very simple in that it creates views by combining pixels from images. Especially, 3D LF considers light rays passing through a line, not a plane, so it is easy to captures light rays. This paper proposes a LF rendering method in a 3D LF composed of 360° images, which supports maximum field of view. Existing 3D LF acquires light rays with a general camera such as an action camera or a DSLR camera, and the conventional 3D LF rendering takes this into consideration. If the 3D LF rendering method is applied to the 3D LF composed of 360° images, unnatural views are rendered. To cope with this problem, this paper proposes two rendering schemes. First is warping, which is used to create views from equirectangular 360° images. It serves to straighten curved objects in a equirectangular image. Second is light ray selection considering vertical incident angle (LRS-VA). The relationship between light rays and viewpoint motion depends on vertical incident angle, but the conventional 3D LF rendering does not take this into account. By considering this relationship, LRS-VA renders the top and bottom pole views more natural. In addition, LF rendering requires very fast operation speed, and this paper verifies the real-time operation of the proposed method through GPU implementation.
Distributed Target Tracking Method Based on Variational Bayesian with Systematic Biases

The unknown and time-varying systematic biases and measurement noise and abnormal non-Gaussian process noise will result in low target tracking accuracy. Additionally, traditional Kalman filtering based on Gaussian noise is difficult to meet the requirements of the estimation accuracy. In this paper, a variational Bayesian-based adaptive Kalman filter algorithm under distributed fusion framework (DF-VBAKF) is proposed. It first a priori model of probability density is constructed based on the properties of multiple unknown parameters. And then, through variational Bayesian and adaptive Kalman filter, the posterior distribution probabilities of target state, system biases, and noise covariance are jointly updated by using standard variational method. Finally, collecting local state estimation and its covariance from different platform, the control center fuses them according to the covariance intersection fusion strategy, and feeds results back to platform. The simulation results show that the proposed algorithm has higher estimation accuracy for systematic biases, noise and target state, leading to improvement of target tracking accuracy, too.
Deep learning-based anomaly detection for imaging in autonomous vehicles

Autonomous driving and self-driven vehicles have become among the most pursued research areas in recent years. Nowadays, various driving tasks can be solved by applying the newest machine learning techniques such as line tracking, traffic sign recognition, automated speed adjustment, and parking. However, difficult visual conditions and anomalies can cause problems in selected algorithms, which may occur unexcepted and failure operations in these cases. It is also expected not just very expensive to do such kinds of experiments, but these problematic conditions are also lead to dangerous traffic situations at the same time. We made an effort to put these kinds of studies into a cost-effective and safe model-scale environment. This paper introduces an anomaly detection method capable of recognizing abnormal and burnt-out objects in image scenes. Our proposed method is based on a fast neural network architecture using YOLO layers to detect regions. Our experiments demonstrate the capabilities and detection accuracy of the designed neural network, called anomalyNet, with the complete training and evaluation process. In the study, we work with publicly available datasets, but our model-sized track and DAVE (University of Debrecen Autonomous VehiclE) play an important role also.
Cooperation Pattern Exploration for Multi-Agent Reinforcement Learning

Effective exploration is an important problem in reinforcement learning. It has developed well in the singleagent domain, but not so well in the multi-agent domain with more complex interactions. In this article, we provide a new approach to address the problem of efficient multi-agent exploration, which is called cooperation pattern exploration (CPE). CPE utilizes variational inference to predict cooperation patterns from the agent's observation-action trajectories, and trains the agent to distinguish and explore different cooperation patterns by minimizing the error from the true cooperation pattern. In addition, CPE applies the value decomposition framework to fuse the individual Q-values of agents during the centralized training stage. In order to enhance the effective exploration ability of agents, we integrate the novelty weight generated by random network distillation (RND) into the design of the value decomposition network to guide agents to explore more novel cooperation patterns. We test the performance of the designed algorithm on the SMAC platform. The experimental results show that CPE can effectively improve the cooperation ability of agents.
Emotional Dialogue Generation with Emotion Embedding

As an important research content of artificial intelligence, dialogue system has received extensive attention from industry and academia. Existing dialogue systems mainly focus on solving problems such as content richness and semantic consistency. The research on emotion control has not received much attention. It’s still quite challenging to generate emotional responses. In this paper, we propose a dialogue generation model based on Seq2Seq and add emotion embedding to the decoder. Experiments show that the model can generate appropriate responses both in emotion and content. We also train a Bert_BiLSTM emotion classifier to improve the emotion annotation quality of the CDCG Dataset.
Cloning Object Detectors

Object detectors based on neural networks are deployed in various consumer electronics products to predict different types of object and their location in images. This paper presents a cloning attack on object detectors, using problem domain samples and oracle access to a trained object detector. As in known cloning attacks on image classifiers, the presented attack uses the oracle access to label the samples. The resulting set of labeled samples, referred to as the surrogate dataset, is then used to train the clone detector. Compared to image classifiers, the surrogate dataset created by an object detector can contain more types of error. The paper describes a way to assess the quality of the surrogate dataset. The cloning attack was implemented, and experiments were conducted with a CenterNet and a RetinaNet object detector, and the Oxford-IIIT Pet, Tsinghua-Tencent 100K, and WIDER FACE datasets. The results show that object detectors can be cloned successfully, even if the quality of the surrogate dataset is relatively low. However, in case of a low-quality surrogate dataset, the quality of the clone detector was only high if it used the same architecture as the target detector.
On General Factorization Problems of <i>n</i>-D Polynomial Matrices

Different from previous perspective, this brief studies relationships between the modules <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho ({\mathbf {F}}_{1})$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho ({\mathbf {F}})$ </tex-math></inline-formula> generated by the rows of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {F}}_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {F}}$ </tex-math></inline-formula> respectively under the assumption of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {F}}$ </tex-math></inline-formula> admitting a general factorization <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {F}}={\mathbf {G}}_{1}{\mathbf {F}}_{1}$ </tex-math></inline-formula> . Two necessary and sufficient conditions for the equivalence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho ({\mathbf {F}}_{1})$ </tex-math></inline-formula> and quotient modules of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho ({\mathbf {F}})$ </tex-math></inline-formula> with respect to some polynomials are obtained for two different situations. As a by-product, some general factorizations of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf {F}}$ </tex-math></inline-formula> by computing related quotient modules can be got.
Network-accelerated cluster scheduler

Efficient use of computing clusters is crucial in large-scale data centers: even small gains in utilization can save millions of dollars. However, as the number of microsecond-scale tasks increases, using a CPU to schedule tasks becomes inefficient. Cluster scheduling running within the network can solve this problem, and brings additional benefits in scalability, performance and power efficiency. However, the resource constraints of programmable network devices make network-accelerated cluster scheduling hard. In this paper we propose P4-K8s-Scheduler, a network-accelerated cluster scheduler for Kubernetes implemented on a programmable network device. Preliminary results show that by scheduling Pods in the network at line-rate, P4-K8s-Scheduler can reduce the scheduling overheads by an order of magnitude compared to state-of-the-art Kubernetes schedulers.
A Novel Three-layer-architecture based Planning Method and Its Applications for Multi-heterogeneous Autonomous Land Vehicles

This paper presents a novel Three-layer-architecture based planning method for heterogeneous Au-tonomous Land Vehicles (ALVs). To improve the planning problem for multi-robot systems, especially for heterogeneous ALVs, a novel Three-layer-architecture, including a Global-road-network layer, a Path-trajectory layer, and a Planning-executive layer, is introduced. The Global-road-network layer includes real road-network information and algorithms for global planning according to specific applications. The Path-trajectory layer mainly reflects the different motion characteristics of different ALVs generated by actual driving or by simulation. The Planning-executive layer implements the local planning algorithm for avoiding obstacles, which is closely related to the Path-trajectory layer. Several typical tasks by using multiple heterogeneous ALVs are employed to verify the proposed Three-layer-architecture, such as multiple ALVs encircling a single target, multiple ALVs tracking multiple targets, and multiple ALVs to multiple targets patrolling tasks. Some classical algorithms and their improved forms are also employed in these corresponding layers to complete the plan-ning tasks. Experimental results show that the performance of the proposed algorithm is robust and stable.
Computing Non-Contactable Drowsiness Monitoring System with Mobile Machine Vision

This project proposes a human facial features detection based on color segmentation via skin color and Viola-Jones algorithm for real time application. YCbCr color space is used to detect the presence of skin in an image where the image is normalized, and luminance is removed to increase face detection accuracy. The second method, Viola-Jones which use Haar feature to detect facial feature such as face and eye also developed and tested. To perform in real time detection, CamShift algorithm and template matching are used to track face and eyes sequentially in Android platform. Then, the real time detection and tracking are evaluated to assess its performance. Finally, the algorithm is applied to drowsiness detection using PERCLOS.
Initializing FWSA K-Means With Feature Level Constraints

Weighted K-Means (WKM) algorithms are increasingly important with the increase of data dimension. WKM faces an initialization problem that is more complicated than K-Means’ because in addition to picking initial cluster centers, it should also provide feature weights. Moreover, the one-dimensional solution to WKM’s widely used objective function is unacceptable in most cases. Yet, the initialization of WKM, especially the initialization of feature weight, has been largely ignored. This paper studies the problem by analyzing Feature weight self-adjustment K-Means(FWSA K-Means), a popular WKM proposed to avoid the one-dimensional solution. Experimental results suggest that the algorithm is actually easy to cluster mainly based on a single feature information when it is not well initialized. Moreover, the paper argues that initial feature weights and cluster centers are equally important in determining the final partition. Therefore it suggests using feature level constraints to improve the initialization and proposes a semi-supervised algorithm Constrained FWSA K-Means (CFWSA K-Means). The algorithm uses constraints in evaluating feature weights and clusters to guide their evolution at the stage of initialization. Experimental results suggest that it is effective and robust in utilizing constraints. In addition, if its initialization process is started by the cluster centers provided by BRIk, an initialization approach for K-Means, the performance can be further improved.
Innovative Research on Multimedia English Teaching Curriculum Design Under the Background of Computer Big Data

The rapid development of computer information technology and big data has expanded the space for educational reform, and more and more English teachers enjoy the use of multimedia courseware. And how to grasp the essence of education informatization and make English teaching courses better meet the process of education informatization is a problem that teachers must think and explore in depth. This paper studies the design and innovation of multimedia English teaching courses under the background of computers and big data. This study took 80 English teachers and 300 students as the survey subjects, and adopted questionnaire surveys and interviews to investigate the current situation of multimedia English teaching. In view of the existing problems, this article proposes optimization suggestions for the innovative design of multimedia English teaching courses from the levels of schools, teachers and students. The results of the questionnaire “Reasons for Difficulties in Using Multimedia” showed that 10.81% answered “outdated equipment”; 43.24% answered “limited resources”; 32.43% answered “limited personal skills”; answered “no difficulty” accounted for 3.51%. It can be seen that the main reason for teachers’ difficulty in using multimedia is limited resources and personal skills.
Fused Multiscale Cost Volume for Efficient Stereo Matching

Stereo matching is an important part of reconstructing depth information and 3D scenes. However, a important problem is improving accuracy and efficiency at the same time. We propose an end-to-end convolutional neural network model to estimate disparity from stereo images. To improve the accuracy of feature extraction in complex regions, we use atrous spatial pyramid pooling module to extract and fuse features, and atrous convolutions of different rates explicitly adjust the receptive field of the filter. To balance the accuracy of the algorithm and GPU memory consumption, we design a fused multiscale cost volume and the fusion strategy to improve the efficiency in estimating the disparity, which reduces the amount of memory consumption while ensuring the accuracy of the algorithm. Our model is trained and evaluated on SceneFlow and KITTI datasets. Experimental results demonstrate that our method achieve a better accuracy even in weak textures areas, while GPU memory consumption and runtime are reduced.
Things Data Interoperability Through Annotating oneM2M resources for NGSI-LD Entities

In this era of Information and Communication Technology (ICT) and the Industrial Internet of Things (IIoT), semantic interoperability plays an important role in interworking among different standards. One such standard is oneM2M, which supports semantic interoperability between non-semantic oneM2M resource model and semantic data, but it is only limited to Resource Description Framework (RDF) triple data. Where Next Generation Service Interfaces – Linked Data (NGSI-LD) – provides information model and protocol for enhancing the capabilities to represent more complex structures of Linked Data, limited research has been conducted regarding such framework or protocol to support the interpretation and translation among these two different standards. This paper proposes a mapping protocol for interpreting and translating non-semantic oneM2M resource data to NGSI-LD interfaces.
A Novel Hybrid Approach for the Designing and Implementation of Dogri Spell Checker

A spell checker is a software program that highlights words in a text that may be incorrect. A spell checker is an essential feature of every word processor, regardless of the language. The spell checker examines the written text for misspellings and provides the best right suggestions for such misspellings. In this paper, first ever attempt has been made for the designing and implementation of the Dogri spell checker using a hybrid approach. The principal components of this system are error detection, error rectification by producing suggestions, and replacement by manually or automatically using the proposed methodology. The algorithm correctly suggests 74.72% of erroneous words and finds 80.79% of misspelled words.
Face Profile Biometric Enhanced by Eyewitness Testimonies

Continuous development in surveillance systems is increasingly motivating research in biometrics to articulate unconstrained recognition of human faces. Comparative soft biometric has recently been employed to characterize eyewitness testimonies for use in a biometric system to improve the recognition accuracies of the traditional biometric systems. In this paper, we present a face profile recognition system by fusing features extracted in a traditional face recognition system and eyewitness testimonies processed in a soft biometric system to improve recognition accuracies. Here we have also demonstrated an association between our traditional face profile biometric system and the soft biometric system by numerically mapping the features extracted from a face profile to its soft biometric attributes. Our experiments on 230 subjects in XM2VTSDB dataset demonstrate 84% accuracy for traditional biometrics. The recognition rate is further improved to 98% accuracy when soft and traditional biometrics are fused.
Face Recognition: Recent Advancements and Research Challenges

A Review of Face Recognition Technology: In the previous few decades, face recognition has become a popular field in computer-based application development This is due to the fact that it is employed in so many different sectors. Face identification via database photographs, real data, captured images, and sensor images is also a difficult task due to the huge variety of faces. The fields of image processing, pattern recognition, and computer vision are all crucial to face recognition. Almost 20 papers from 2011 to 2021 were analyzed and it was observed that a framework called Deep Learning includes a number of significant algorithms. To get better results, you need to employ distinct network models for different applications (images, speech, and text). A review of major person recognition research is also included which is followed by a detailed description of the most accurate and latest datasets (like CKPlus, DeepFace, ImageNet, etc.) required in face recognition. Further, the properties of appropriate face authentication applications and architectures are discussed. This paper also includes the research challenges that might occur and some potential future works as well. We present a prospective analysis of facial recognition also in this manuscript.
A Correct Face Mask Usage Detection Framework by AIoT

The COVID-19 pandemic, which affected over 400 million people worldwide and caused nearly 6 million deaths, has become a nightmare. Along with vaccination, self-testing, and physical distancing, wearing a well-fitted mask can help protect people by reducing the chance of spreading the virus. Unfortunately, researchers indicate that most people do not wear masks correctly, with their nose, mouth, or chin uncovered. This issue makes masks a useless tool against the virus. Recent studies have attempted to use deep learning technology to recognize wrong mask usage behavior. However, current solutions either tackle the mask/non-mask classification problem or require heavy computational resources that are infeasible for a computational-limited system. We focus on constructing a deep learning model that achieves high-performance results with low processing time to fill the gap in recent research. As a result, we propose a framework to identify mask behaviors in real-time benchmarked on a low-cost, credit-card-sized embedded system, Raspberry Pi 4. By leveraging transfer learning, with only 4–6 h of the training session on approximately 5,000 images, we achieve a model with accuracy ranging from 98 to 99% accuracy with the minimum of 0.1 s needed to process an image frame. Our proposed framework enables organizations and schools to implement cost-effective correct face mask usage detection on constrained devices.
Multi-input transfer function model for Covid-19 incidences in Jakarta

Indonesia is currently facing the Covid-19 pandemic, which all countries in the world have also faced in the past year. The contribution of daily cases from each province is the key to the high and low number of positive cases of Covid- 19 in.Indonesia. Based on data from the Covid-19 handling task force for Indonesia, it was noted that Jakarta is the province with the highest spread of Covid-19 cases in Indonesia. Daily positive cases in Jakarta contributed 25% to national cases. For this reason, this study aims to analyze the incidence of positive cases of Covid-19 in.Jakarta through a prediction model for time series data. The multi-input transfer function model used is one of the multivariate time series models to predict Covid-19 cases in Jakarta based on several external variables. The external variables or input series used are the Covid-19 cases in Bodetabek, close contact data, and death case data. All variables used are time-series data from April 2020 to April 2021. The analysis results show that the three input variables significantly correlate in predicting Covid-19 cases in Jakarta. The multi-input transfer function model that has been formed has a pretty good performance in predicting Covid-19 cases in Jakarta with a MAPE of 10%.
Tuning Functional Link Artificial Neural Network for Software Development Effort Estimation

Software development effort estimation (SDEE) is a critical task in project management for accurate planning, staffing, resource allocation, scheduling, and cost estimation. Detailing nonlinear correlations between cost drivers and project costs using conventional parametric methodologies is difficult. In this context, we evaluated methods based on foundation-centered swarm intelligence and functional link neural networks for SDEE. The primary objective of this study was to investigate the use of functional link artificial neural networks for improving the predictability of SDEE. The findings are presented as computational intelligence methods for SDEE.