Reviewerly/exHarmony huggingface dataset:
IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II)

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.
Teaching research on visualization of uav remote sensing image teaching experiment

In the innovation and development of human space observation technology, faced with increasing spatial information, virtual reality technology, the main tool of human space cognition, should be regarded as the main direction of practical research in order to quickly analyze and express relevant content in practice development. According to the understanding and research of researchers on virtual reality technology in recent years, its application in the field of spatial information is very critical, and it is also a relatively rapid development of the emerging discipline, both theory and technology have formed a profound impact on the analysis and expression of geo-information. Therefore, on the basis of understanding the development trend of UAV remote sensing image visualization technology, this paper verifies and analyzes practical teaching experiment cases according to the practical design system, and the final results prove the effectiveness of uav remote sensing image visualization teaching experiment teaching research
Steep slope path planning for rope-tethered mobile robots in extreme terrain

As the focus of technological innovation in recent years, tethered mobile robots are intelligent organizations with perceptual decision-making and movement capabilities, which directly reflect the development level of modern information technology and artificial intelligence technology. Among them, navigation is the main direction of technical research, path planning is the basic link of navigation, and steep slope path planning is to find a suitable movement path from a given start point to the end point under special conditions to ensure that the robot can be safe and collision-free during movement. To bypass all obstacles. Nowadays, this research has received the attention of scientific research scholars and has achieved important results in the development of practice. Based on the understanding of the current application and exploration status of rope-tethered mobile robots in extreme terrain, this paper verifies and analyzes the actual path navigation effect and performance according to the path planning scheme centered on the link graph method. The final result proves that the relevant path planning scheme is correct. Tethered mobile robots with extreme terrain have a positive effect
A Robust Polyscale Length Complexity Measure for Stochastic Self-Affine Processes

Cognitive and other nonlinear systems often involve deterministic differentiable processes and stochastic non-differentiable processes. Measuring the complexity of such processes is important when extracting objective features from the processes for their classification in either reactive, or adaptive, or predictive control. This applies to classifiers based not only to the traditional neural networks, but also to deep learning systems, and particularly in cognitive systems. This paper describes a robust algorithm to measure the length complexity of a self-affine time series using multiscale and polyscale analyses, and provides new insight in the theoretical and practical aspects of extracting the measure.
Time and Actual Obligations

We explore the question how the insights of Carmo and Jones (2002, 2013) can be brought to bear if we treat deontic logic within a framework of branching time. They distinguish between primary obligations, which it may sometimes be impractical to fulfill, and actual obligations—the obligations with which we are actually confronted, given the practical limitations of our circumstances. Their semantics uses possible worlds frames, suited especially to deal with examples such as those offered by Prakken and Sergot (1994, 1996), in which time is allegedly not a consideration. However, deontic logic must also be able to deal with situations in which time is crucially involved, as for example when one ought to go help ones neighbors. For obligations to perform actions, branching time models seem suitable. Here we explore the possibility of such models.
Sampling methods with least information loss in transit videos for the reduction of manual work and computational processing

Automated object recognition in traffic videos is a complex and time-consuming task that requires not only computational processes, but also some manual labor. The amount of time spent on both processes and labor is closely related to the number of frames to be processed. In this research, various sampling methods were studied to reduce the number of frames. Systematic sampling of 29% of the frames is the one that uses the least number of frames and is also equivalent to the census in terms of recognition error.
Graph Convolutional Network-based Centre-Object Tracking for the multi-dynamic agents for Autonomous Vehicles

Multi-Object tracking goals to localize, classify and track all object instances of each class throughout an image sequence. It is very useful to understand the video scenes and very desirable for computer vision-based applications that require detailed pixel-level information, such as video editing and autonomous driving. Our method involves Detection-based tracking, which generally requires detected objects with a tracker that links the boundary boxes (bboxes) to generate target trajectory. To optimize the similarity between tracked and detected objects are the basic part of multi-object tracking (MOT) frameworks. To mark this issue, ongoing works concurrently enhance the matching technique of data modules with joint MOT structure, which has expressed improved execution. The key thought behind our GCNN approach is to take into consideration and learn the distinctive or selective features for local and global matching. It takes into account the relations between nearby objects in a frame sequence and thus provides a better tracking performance as compared to the existing models. We demonstrated our model on the MOT datasets that showed the viability of our GCNN-based joint MOT approach which includes the Hungarian algorithm to assign the identity of the object in the next frame.
A study on predicting age using Convolution Neural Network CNN

Automatic age segregation has become important in many programs, especially since the proliferation of social media and social media platforms. By reading presentations through deep-convolution neural networks (CNN) networks, significant improvements in performance can be seen in these activities. A quick and effective system for measuring gender and age based on facial expressions is developed. Following multiple tests using public face data, our proposed method proved to be have shown way better results than current methods available in literature. Training phase of our proposed model changes the assemble very rapidly of the network we use, therefore our model had achieved greater accuracy while reckoning the age using facial data in years. Our model also consumes the minimum network model area. Our work can be summarized in four main points: (1) Development of a lightweight convolution network based on mixed mixing process to find age. (2) Age ratio accuracy is high, that leads to an appropriate method of division combining and conversion is easy. (3) Image magnification is use to facilitates the face detection and adjustment of the image input, this leads to enhancement of age related features.
Contemporary Emerging Trends of Text Mining Techniques used in Social Media Websites: In-Depth Analysis

The emerging trends in social media have given a new path for information sharing for all types of internet users and online business operators. The contemporary emerging trends have been used in different forms to draw widespread attention from internet users. Different forms have been identified and inserting the data by different aspirants to attain their goals and aim to convey the information to the targeted audience. A huge amount of data is available in social media in different formats. The data analysis is mandatory to find the authenticity of the data and remove the spam data. Text mining methods have been introduced to analyze and summarize the data from numerous social media websites with distinct variables with open-source tools. The present paper is focusing on the emerging trends in social media text mining methodologies to extract the desirable data from the pool of social media text information associated with spam. This paper is rich in exploring different text mining applications, trend prediction in association with the social interaction theories. In this paper, Facebook, Twitter, Bloggers, and other predominant social media sites have been taken into consideration for extracting the experimental results.
Kinodynamic Motion Planning for Autonomous Surface Ships Using Motion Primitives

In this letter, a method of kinodynamic-feasible trajectory planning for autonomous surface ships is proposed. By sampling in the control space, the discrete control inputs are obtained. Then, under the conditions of the control inputs obtained, forward integrate the system equation of the ship over a given short time to obtain the motion primitives. optimal trajectories by exploring the map using a set of short-time motion primitives. Using a series of motion primitives to construct a state lattice graph in the state space, that can be explored using graph search algorithms. The proposed method can generate smooth trajectories under the dynamics and kinematics of the ship&#x0027;s system, which creates favorable conditions for subsequent motion control and trajectory tracking.
A Reliable Path Planning Method for Lane Change Based on Hybrid PSO-IACO Algorithm

The real-time performance of path planning algorithms and path continuity are crucial to motion planning. Thus, B-spline-based path planners have attracted extensive interest because of control flexibility and continuous curvature. However, the B-spline-based planning requires lots of resources to solve due to the multiple nonlinear constraints. Therefore, a new hybrid algorithm is proposed, which utilizes the comple- mentary advantages of particle swarm optimization (PSO) and improved ant colony optimization (IACO), called PSO-IACO. The proposed algorithm comprises two phases. First, the PSO ensures a fast convergence to a series of feasible rough paths, which are used to initialize the pheromone allocation and the position of IACO. Then, the IACO with the advantage of positive feedback help improves the quality of the path. Moreover, the main improvement of IACO from ACO is the pheromone update strategy considering the local and global search experience, which is inspired by the idea of PSO and Max-Min ant system. Simulation demonstrates that the path quality of PSO-IACO outperforms that of PSO, IACO, Midaco, and genetic algorithm(GA). It also outperforms that of Enumeration in most scenarios. The success solution rate is improved two times as compared to Midaco for some scenarios. And the execution time is reduced to 74&#x0025; in comparison with Enumeration for the large-scalescenario.
The blending of Cryptical Algorithms

In today&#x0027;s world, unreliable data is increasing which is used in communication all over the world. Therefore, the safety and security of data is the most important responsibility of internet users. The best solution to this problem is to use cryptography algorithms that encrypt the data in the ciphertext and then decrypt the data into the original form. In this paper, we have used different types of algorithms to encrypt or decrypt the data and also searched for different sites to do it.
Invasive Weed Optimization Based Sentiment Mining of Digital Review Content

Customer Feedback of any product or service plays an important role in the marketing industry to promote business with new features. Researcher are analyzing survey data, rating data to understand customer behavior. Text review is more effective when people express sentiment in words. This paper analyzes text review content of product / service to mine the customer sentiment. Input content was preprocessed by stopword technique and transform numeric data by use of term frequency feature. Invasive Weed Optimization Algorithm (IWOA) was used by the work to improve the content quality into two class either positive or negative review. Numeric conversion increases the fitness function efficiently to categorize txt feature into respective class. Convolution Neural Network was finally trained by improved BCS feature to reduce classification time. Experiment was done on real dataset of Amazon review. Results shows that proposed model IWCNSA (Invasive Weed and Convolution Neural Sentiment Analysis) has improved evaluation parameter values of precision, recall and accuracy.
A Fast Stable Lane Change Path Planning Method Based On Hybrid Intelligent Algorithms

To improve the solving speed and success rate of the lane change path planning problem for autonomous vehicles, a fast and stable planning method (FSDETS) is proposed in this paper. This lane change path planning scheme is formulated as an optimal control problem according to the three-segment lane change model. Then the B-spline curve is applied to smooth the path. The core contribution is that a method based on hybrid intelligent algorithms is proposed to solve the optimal control problem fast and stably. First, an extended differential evolution is proposed to quickly provide a tough initial guess for the subsequent search. Then two criteria are set to judge whether the tabu search can start. After satisfying them, tabu search seeks a better solution based on the initial guess. Finally, a 1000 cycles simulation experiment is run to verify its stability and real-time performance. Simulation results show that the average time consumption is 0.7417ms, the success rate is 99.97&#x0025; and the average maximum curvature is 0.13947m<sup>&#x2212;1</sup>. Besides, it is also compared with other intelligent optimization algorithms as well as the optimization solver MIDACO. The result shows that our method surpasses other algorithms in comprehensive performances.
Trajectory PHD Filter with Unknown Detection Profile and Clutter Rate

In this paper, we derive the robust TPHD (R-TPHD) filter, which can adaptively learn the unknown detection profile history and clutter rate. The R-TPHD filter is derived by obtaining the best Poisson posterior density approximation over trajectories on hybrid and augmented state space by minimizing the Kullback-Leibler divergence (KLD). Because of the huge computational burden and the short-term stability of the detection profile, we also propose the R-TPHD filter with unknown detection profile only at current time as an approximation. The Beta-Gaussian mixture model is proposed for the implementation, which is referred to as the BG-R-TPHD filter and we also propose a L-scan approximation for the BG-R-TPHD filter, which possesses lower computational burden.
Research on Signal Modulation Type Recognition Based on Cyclic Spectrum in Internet of Things

Identifying signal modulation type in the perception supervision mechanism in the power Internet of Things environment is the primary content. Because the wireless communication channel environment is complex and challenging to predict, especially in substantial noise interference, many parameter estimations and modulation type identification methods are challenging to achieve the desired accuracy. In this paper, based on the apparent differences between the cyclic spectrum of different modulation types, related features are extracted. Some recognition techniques are used to distinguish these features. The identification of signal modulation based on the cyclic spectrum has the characteristics of strong anti-interference ability. It can also be applied well in the case of a low signal-to-noise ratio.
Comparative Analysis of Multi-objective Algorithms for Imbalanced Biomedical Data Classification with Tuned Classifier

Classification of cancer with microarray data is a challenging task because of the high dimensionality and imbal-anced characteristics of the dataset. Selection of few contributing relevant feature genes and classification of biomedical data with an optimally tuned classifier enhances the accuracy of disease detection. Generally, choice of parameters of the classifier is done by trial method and a value is kept fixed for the classification. But in this study during the search process, the best parameters of the classifier are adapted. Five well established and robust algorithms in the multi-objective domain are chosen for the feature selection and for the tuning of parameters of the support vector machine (SVM). We have performed experiments on 12 microarray datasets (both binary class and multi-class). Not only the effect of classifier tuning is studied but also a thorough comparison is performed amongst different multi-objective algorithms. The comparative study can be helpful to make an informed choice for selection of an appropriate algorithm in the field of multi-objective feature selection. The experiments show that MODE provides promising results for highly imbalance data having higher number of classes. For binary and multiclass data with imbalance ratio within the range of 2–5, NSGA-II is performing better. For the dataset having low imbalance ratio, the performance of the multi-objective algorithms are quite competitive.
BlockAudit 2.0: PoA blockchain based solution for secure Audit logs

Audit logs serve as a crucial constituent for every organization that has a digital presence and are used to audit, store, and track modifications made to the data for efficient business intelligence. However, the audit logs are unfortified to attacks that delegate the adversaries to alter data and corresponding audit logs without being disclosed. Two prominent attacks are the authorized access attack, in which the admin privileges are exploited to destabilize the system and the online attack, in which the known techniques like SQL injection is used, that tinkers the databases and hence requires detection. This paper presents BlockAudit: An extensible and tamper-proof system that provides fundamental designing features of audit logs and immutable properties of blockchain that facilitates having a secured and reliable audit log. Towards this, we implement our design schema for BlockAudit and outline its utility and operative procedures. The design is accomplished on a custom-built Proof-of-Authority blockchain and we analyze its performance using various factors like transaction rate, network size, latency, and payload size. Results reveal that typical audit logs can seamlessly transition into BlockAudit to attain more powerful security and safeguard against the investigated attacks on audit logs.
On Post Quantum Wireless Communication Security

In this modern era, when data communication is a necessity of life, data secrecy and privacy is very important. The concern increases when we communicate over insecure wireless channels because they are vulnerable to various security attacks. The expectation from cryptography is to deliver safe and reliable communication. With the emergence of quantum computers, we can say that quantum computers can perform cryptanalysis calculations much better in comparison with existing system. Quantum computers can crack all the existing security algorithms and protocols. Keeping this in mind, it is the need of the hour to develop new procedures for data security so that intruders remain surprised. So in this paper we want to discuss these issues in brief with some solutions to overcome the related challenges.
Rule based Approach to convert abbreviation into Phrases

User creativity and individuality of language create problems in transferring knowledge, which can cause disastrous mistranslations and miscommunication in professional fields. Abbreviated words are extensively used in social media, emails, and many more platforms for communication and transferring information. An abbreviation is a short form for writing a word to save time but slang in nature. Users on their social media are very casual, but this is not acceptable in their professional fields like defence, medical, and many more. Frequently use the abbreviated word. To avoid these informal English abbreviations, we proposed the idea of converting them into standard phrases. We use a dataset, which contains approximately 700 common abbreviations used and their different meaning. This dataset comprises almost all commonly used abbreviations. We achieved this project through python and its libraries. This script is not getting integrated with messaging apps, but it will demonstrate how to convert it into its respective phrase. We have classified our database into different categories of abbreviations and performed our experiment. This paper also includes the experimental results performed on the given dataset and the proposed approach used to execute it successfully.
Security Aspects

This section discusses security aspects in high-speed networks. The section pays particular attention to operational security, which addresses potential attackers attempting unauthorized access, introducing malware into devices, and conducting denial of service (DoS) attacks. The chapter describes router’s access-control, firewalls, intrusion prevention systems, and intrusion detection systems.
Categorization of Task Scheduling Algorithms used in Cloud Environment

Cloud computing is a new paradigm in computing that enables the availability of various resources at one place on the user's demand. In cloud computing, a cloud is a unit containing various resources like hardware, software, etc. In contrast, computing refers to the way or mechanism to access the available resources from the cloud. Numerous challenges such as cloud security, performance, task scheduling etc., are observed during the adaption of cloud computing. Task scheduling aims at the optimized resource allocation to the various tasks. The performance of any system is directly dependent on the scheduling process. More effective task scheduling will undoubtedly lead to the high performance of the system. This paper highlights the various task scheduling algorithms and their advantages and disadvantages in the cloud environment. Further, A novel effort is made to categorize existing task scheduling algorithms based on their working concepts.
Anomaly Detection Based Image Feature Matching Under Vehicle-Infrastructure Viewpoint

Matching local features on two or more images is fundamental and critical for many applications in the field of computer vision. The putative correspondences often contaminated by mismatches when applying feature matching methods in real-world settings. Therefore, the paper focuses on removing mismatches from putative matches by investigating the idea of motion consistency. This paper converts the feature matching into an outlier detection problem based on the converted spatial distance matrix. The classic anomaly detection method LOF is adopted to detect the outliers so that the false matches can be identified and removed. The images for experiments are obtained from cameras mounted on a vehicle and roadside infrastructure. Experimental results demonstrate that the proposed method achieves 88.14&#x0025; average precision, which is the highest among the other two methods.