Reviewerly/exHarmony huggingface dataset:
Combining Event-Based Maneuver Selection and MPC Based Trajectory Generation in Autonomous Driving

Maneuver planning, which plays a key role in selecting desired lanes and speeds, is an essential element of autonomous driving. Generally, for a vehicle driving on a multilane road, there are several potential maneuvers in both longitudinal and lateral directions. Selecting the best maneuver from the various options represents a significant challenge. In this paper, we propose a maneuver selection algorithm and combine it with a trajectory generation algorithm, which is based on model predictive control (MPC). The maneuver selection method is a higher-level planner, which selects only one maneuver from all possible maneuvers based on the current situation and delivers it to a lower-level MPC-based trajectory tracking controller. The effectiveness of the proposed algorithm is validated by simulating an overtaking scenario on a multilane highway.
Proposed iPrivacy based Image Encryption in Mobile cloud

Basically, people can be capturing images and outsourced to the mobile cloud. Privacy of image is the major challenge to be facing now-a-days. Encryption is one of the greatest solutions to security. While outsourced the image data to the cloud, Cloud Service Provider (CSP) could be possible to share the image to the unauthorized users for their profit. And the past encryption techniques such as AES, DES, RSA are unsuitable to image than text. Due to the lacking of CSP and past encryption techniques, we propose the encryption technique called iPrivacy-Pseudo Random Permutation (PRP) based Encryption, which is applied to the image to ensure security/privacy. The proposed scheme(iPrivacy-SDS) handled by three simple steps such as Split, Distribute and Scramble to attain secure and fast execution. The proposed work may prevent users from accessing of private images. The experimental results show that the time consumption is efficient and around 50% reduction while compared to AES and compared with various image formats such as JPEG, BMP, GIFF and PNG.
Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Purpose The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios. Design/methodology/approach In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear ( k -nearest neighbor, k-NN ) classifiers utilizing combinations of different features, sentiment scores and emotion scores. Findings Overall, the results of this study reveal that the non-linear classifier ( k -NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance. Originality/value This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.
Teaching Mode of Augmented Reality College English Listening and Speaking Supported by Wearable Technology

With the development of modern technology, wearable technology has become more and more popular in the actual college English listening and speaking teaching mode. Wearable devices, as the name suggests, are smart electronic products that can be worn on the human body, such as smart glasses, smart watches, smart rings and smart clothing. Smart watches and ordinary electronic products are very different from wearable devices. Wearable devices give us a more intelligent and technological experience. Wearable technology has brought new changes and challenges to the realistic college English listening and speaking teaching mode. Wearable technology has become a hot topic and frontier in the field of science and technology. It has been widely used in education, medical, and industrial fields. It provides unprecedented convenience for people to learn, work, and live. Augmented reality technology is a kind of technology that skillfully integrates virtual information with the real world. After the simulation of computer-generated text, images, 3D models, music, video, and other virtual information, it is applied to the real world, and the two kinds of information complement each other, thus realizing the “enhancement” of the real world. This paper gives a brief overview of the definition of wearable technology, the classification, and main features of wearable technology and combines it with college English listening and speaking teaching mode to improve the classroom learning effect. Through the questionnaire survey, we know that wearable technology is a teacher’s teaching, and students’ learning provides great convenience. Wearable technology greatly enhances the learning and listening fun of students.
COVID-19 Pandemic Response Robot

Due to an arising COVID-19 positive confirmed case in Taiwan, the screening of body temperature, mask wearing and quarantined violation is enhanced. A mobile robot that conducts this task is demanded to reduce the human labor. However, conventional robots suffer from several limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), different viewpoints, the scale of objects, low mobility, less functionality, and some environmental limitations. As for the thermal imager, it displays the current heat spectrum colors, and needs manual monitoring. This paper proposes applying Simultaneous Localization and Mapping in an unknown environment and using deep learning for detection of temperature, mask wearing, and human face on the Raspberry Pi to overcome these problems. It also uses the A* algorithm to do path planning and obstacle avoidance via 3D Light Detection and Ranging to make the robot move more smoothly. Evaluating and implementing different Simultaneous Localization and Mapping algorithms and deep learning models, then selecting the most suitable method. Root Mean Square Error of three Simultaneous Localization and Mapping algorithms are compared. The predictions of deep learning models are evaluated via the metrics (model speed, accuracy, complexity, precision, recall, precision–recall curve, F1 score). In conclusion, Google Cartographer for building a map, Convolutional Neural Network for mask wearing detection, and only looking once for human face detection achieve the best result among all algorithms.
Learning structured embeddings of knowledge graphs with generative adversarial framework

Many large knowledge graphs are now available and ready to provide semantically structured information that is regarded as an important resource for question answering and decision support tasks. However, they are built on rigid symbolic frameworks which makes them hard to be used in other intelligent systems. Knowledge graph embedding approaches are gaining increasing attention, which embeds symbolic entities and relations into continuous vector spaces. Such graph embeddings are often learned by training a model to distinguish true triples from negative ones. Unfortunately, the negative triples created by replacing their heads or tails with randomly selected entities are easily identified by the model, which makes them insufficient to train useful models. To this end, we propose a method under a generative adversarial architecture to learn graph embeddings, in which a generative network is trained to provide continually improved "plausible" triples whereas a discriminative network learns to distinguish truth triples from the others by competing with the generator in a two-player minimax game. When arriving at a convergence, the generative network recovers the training data and can be used for knowledge graph completion, while the discriminative network is trained to be a good triple classifier. Extensive experiments demonstrate our method can improve multiple graph embedding models with a significant margin on both link prediction and triple classification tasks.
A robust optimized convolutional neural network model for human activity recognition using sensing devices

Abstract Human activity recognition has become an expansive field of interest in recent years, both in academic and industrial research. Human Activity recognition (HAR) is concerning the prediction of person's movement or action such as walking, standing, sitting, up and downstairs, etc. Convolutional neural network (CNN) is a key component with deep learning. The main objective of this study is to design and implement an activity recognition algorithm in the state of the art of deep learning systems which accomplish superlative performance to detect human activities. Accordingly, an extensive comparison has been developed between different deep learning algorithms such as classical (CNN) models and Recurrent Neural Network (RNN) models with respect to the major human activities. Furthermore, a robust (CNN) deep learning model has been built up and proposed in order to enhance the recognition precision of human activities. This proposed model uses raw data acquired from a set of inertial sensor and exploring numerous human achas been built up and proposedtivities; sitting, standing, jogging, walking, and etc. Experimental results show that the precision of the proposed deep structure has achieved 97.5% with respect to the NAdam optimizer which would be considered as the most effectively recognizer compared to other deep learning architectures.
Visual Analysis of Global Carbon Mitigation Research Based on Scientific Knowledge Graphs

Global temperature change is related to the destiny of all mankind, and carbon mitigation, as well as greenhouse gases control, are key points. In order to explore the basic knowledge, research hotspots and trends in global carbon mitigation research, this paper, based on 15,304 carbon mitigation articles from Web of Science, from 1991 to 2021, conducts spatio-temporal distribution, country distribution, research hotspots and cooperation network analysis, and draws frontier knowledge graphs of carbon mitigation by using CiteSpace, Gephi and other scientific knowledge mapping and literature analysis software. The key scholars, important literature, main contribution institutions and countries/regions in the field of carbon mitigation research were extracted, and the research basis and evolution law were displayed. The study found that: (1) The research on carbon mitigation is increasing every year, which can be roughly divided into four stages: germination, low-speed development, medium-speed development and high-speed development. (2) The spatial distribution of carbon mitigation research is unbalanced, mainly showing a characteristic of "one super and many strong" centered on the United States. (3) The research hotspots of carbon mitigation have gradually evolved from phenomenon analysis, policy guidance, method exploration to mechanism improvement. Further research might focus on ocean carbon sink, carbon trading and carbon-negative technology.
Ibn Sina: A patient privacy‐preserving authentication protocol in medical internet of things

The prosperous advancement in Medical Internet of Things (MIoT) technologies has hastened the development of healthcare systems. MIoT improves the traditional medical facilities through periodically monitor of patient’s health records. Electronic Medical Records (EMRs) are sensitive private data and needs efficient secure and private schemes that interchange these EMRs between healthcare providers and patients. Most of the current privacy preserving schemes do not provide the desired privacy level and suffer from computation and communication overheads. The length of an IDentity-based Strong Designated Verifier Signature (IDSDVS) is short. IDSDVS has low communication and computational costs. Moreover, it gives the signer (the patient) a control over whom can verify his signature. This feature allows patients to choose the verifier and keep the privacy of their Electronic Medical Records (EMR). This paper presents a new privacy-preserving authentication protocol in Medical Internet of Things (MIOT) that achieves patient privacy in MIoT. Precisely, we propose two new IDSDVS schemes. Utilizing the random oracle model, we show that these two schemes satisfy the security conditions. Furthermore, we evaluate the proposed IDSDVS schemes by comparing them to related schemes in the literature. The proposed schemes achieve lower costs of communication and computation than related schemes.
A Creative Design Framework for Math Exam Questions Concerning Inequalities and Zeros of Functions with an Unknown Parameter in China National College Entrance Examination

The annual exam questions in China National College Entrance Examination (CNCEE, commonly known as Gaokao) have attracted wide attention from the entire society in China. This paper analyzes an important math exam question related to always holding inequalities with an unknown parameter from the 2020 CNCEE and builds different solutions via parameter separation or classified discussion, and then gives a systematic and effective design framework for questions related to problems of always holding inequalities and zeros of functions with an unknown parameter for the CNCEE. The framework reveals the internal mechanism of designing the math exam questions in the CNCEE. According to the design framework, two simulated math exam questions with reference solutions will be proposed.
Method of Encoding Video Frames in Infocommunication Systems

An approach to the construction of a selective algorithm for steganographic information hiding based on graphic type containers is proposed. Unlike to most of existing approaches, proposed algorithm takes into account the container fragments content features. So, in the source container, initially performs search for a transformants set suitable for modification. In the next step, within each such identified transformants, a components set that are potentially acceptable for direct modification determines. At the same time, the search parameters for both the transformants set and the components can be modified using the stego key parameters. Encapsulating the secret message bits procedure considered. In this case used an approach based on indirect encapsulation, during which the component decimal value changes. Based on the fact that the algorithm does not make changes to the container that are characteristic of most known steganographic methods, and also due to the fact that the number of encapsulated bits within the container is insignificant, the proposed approach is potentially resistant to stegoanalysis methods.
National-scale distribution of Cobalt in Papua New Guinea and its significance for mineralization potential

The concentrations, variations, distribution and anomaly of cobalt values in stream sediments from Papua New Guinea (PNG) were studied based on the results of the Papua New Guinea National-Scale Geochemical Baseline (PNG-NGB) project (2015–2018). The data were studied to understand the spatial distribution of cobalt and its prospecting potential in PNG. 1399 samples were collected covering approximately 128,000 km2 with sample density of approximately 1.1 site/100 km2. The analytical results show that the cobalt values in stream sediments of PNG ranged from 1.8 μg/g to 446 μg/g with a baseline (median) value of 27.8 μg/g which is significantly higher than the upper crustal abundance of cobalt. Parent rocks especially ultramafic–mafic rocks, are the major factor that governing the spatial distribution of cobalt values in stream sediments, while cobalt mineralization is also important for the distribution of cobalt anomalies based on their close spatial correlation. Regional cobalt geochemical anomaly correlated with the national scale geochemical anomaly as well, which suggest that the national scale geochemical anomaly can be an indicator for further prospecting. 11 Cobalt geochemical provinces and 21 cobalt geochemical anomalies are delineated for further prospecting.
Automatic Modulation Classification for Terahertz Communication

The Terahertz band of frequencies offers a new frontier for research in wireless communication. With the availability of huge bandwidth, it offers the possibility of realizing the promised potential of 6G communication. An important capability the modern communication receivers are expected to possess is automatic modulation classification (AMC). In addition to its security and military applications, AMC improves spectral efficiency. It is also important in the context of cognitive radio. In this paper, a deep learning based approach is developed for the task of AMC in the Terahertz regime. Its performance is evaluated under different SNR conditions. The simulations demonstrate that the model developed provides excellent performance over the Terahertz channel.
AI-Based Saliency-Aware Video Coding

The demand for video through over-the-top (OTT) has been constantly increasing in recent years. During the COVID-19 pandemic, demand skyrocketed, hence leading to the need for better video compression. The human visual system (HVS) can quickly select visually important regions in its visual field. These regions are captured at high resolution, while other peripheral regions receive little attention. Saliency maps are a way to imitate the HVS attention mechanism. Recently, deep learning-based saliency models have achieved tremendous improvements. This article leverages state-of-the-art deep learning-based saliency models to improve video coding efficiency. First, a saliency-based rate control scheme is integrated in a high-efficiency video encoder (HEVC). Then, a saliency-guided preprocessing filtering step is introduced. Finally, the two approaches are combined. Objective and subjective evaluations show that it can lower the bitrate from 6% to almost 30% while maintaining the same visual quality.
A multi-dimensional comparison of the effectiveness and efficiency of association measures in collocation extraction

Abstract Because of the ubiquity and importance of collocations in language use/learning, how to effectively and efficiently identify collocations has been a topic of interest. Although some studies have evaluated many of the existing association measures (AMs) used in the automatic identification of collocations, the results so far have been inconsistent and unclear due to various limitations of the existing studies. Hence, this study makes a multi-dimensional evaluation of the effectiveness and efficiency of seven major AMs in the identification of three types of collocations across five genres and seven corpora of different sizes. The results indicate that while a few AMs, such as Log Likelihood Ratio and Cubic Mutual Information (MI 3 ), are consistently more effective and efficient than the other five AMs being examined, no one AM alone may be adequate in the identification of different types of collocations across different genres and corpus sizes. Research implications are also discussed.
Remote Sensing and Natural Image Dehazing using DCP based IDERS Framework

Generally remote sensing images are in hazy conditions such as fog, snow, thin cloud, dust etc., which results in contrast degradations in image. This work is based on the Dark Channel prior (DCP) to eliminate the haze effect on remote sensing images. In this model both natural images and remote sensing images Dehazingis possible. In the enhancement of satellite image properties several steps are involved, the first step is to identify whether the image is natural image or remote sensing image and restore it for the purpose of removing haze. By using air light values further, the iteration takes place with the help of DCP to remove dust and then the haze is eliminated by applying Iterative dehazing method for remote sensing image (IDERS) model. The output image obtained after Low light image enhancement (LIME) process is free from haze, brightness is enhanced. The simulation results shows that the performance of proposed method is improved as compared to the state of art approaches.
Utilizing Logistic Map to Enhance the Population Diversity of PSO

Abstract Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.
Design of Virtual Machine Scheduling Algorithm in Cloud Computing Environment

With the application of cloud computing services in more and more fields, it will undertake more computing tasks and storage tasks. The problem of high energy consumption in data centers will become more serious. Virtualization technology is very important in cloud computing, which can improve the utilization rate of resources. At the same time, it has flexibility in resource scheduling and can integrate multiple virtual machines to achieve power efficiency. Using online virtual machine migration technology for energy-saving planning in cloud environment is a hot research topic in academic circles. The scheduling strategy proposed in this paper can reduce the server downtime and the number of server hosts, so as to achieve the maximum use of resources. The main research work of this paper includes the following aspects: Firstly, the energy consumption in the process of virtual machine energy-saving design is modeled, and the relationship between energy consumption and resource usage under different load conditions is analyzed, and the problems are abstracted, e.g., packaging box problem. Secondly, this paper uses genetic algorithm to solve the problem of high energy consumption. Finally, based on the target allocation scheme of virtual machines obtained by the above method, the migration problem of virtual machines is abstracted as the problem of finding the maximum weighted independent set of graphs. And a greedy algorithm is designed to solve this problem. In this paper, CloudSim simulation platform is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed algorithm can reduce data energy consumption and avoid frequent migration of virtual machines.