Reviewerly/exHarmony huggingface dataset:
Research on Static/Dynamic Global Path Planning Based on Improved A<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mi>∗</mi> </msup> </math> Algorithm for Mobile Robots

In view of the problems of A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm in path planning, such as collision risk, the path is not necessarily optimal, and there are numerous turning nodes. Therefore, this study proposes an improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm to improve the quality of the planned path. First, the 8-neighborhood children of the parent node are generated, and the security of the planned path is improved by further investigating the properties of the neighbors of these children one by one to reasonably set virtual obstacles. Second, the heuristic function of A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm is ameliorated to make it closer to the actual cost, so as to enhance the accuracy of the planned path; finally, the planned path is smoothed by using the cubic uniform B-spline curve to eliminate corner sharp points in the path. The simulation results show that the improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm can not only ensure the safety and smoothness of the planned path but also obtain the shortest planned path in the static environment with different obstacle rates. In addition, we combine the improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm with the dynamic window algorithm to enable mobile robots to realize real-time dynamic obstacle avoidance while ensuring the optimality of global path planning.
Finding Debt Cycles: QUBO Formulations for the Maximum Weighted Cycle Problem Solved Using Quantum Annealing

The problem of finding the maximum weighted cycle in a directed graph map to solve optimization problems is NP-hard, implying that approaches in classical computing are inefficient. Here, Quantum computing might be a promising alternative. Many current approaches to the quantum computer are based on a Quadratic Unconstrained Binary Optimization (QUBO) problem formulation. This paper develops four different QUBO approaches to this problem. The first two take the starting vertex and the number of vertices used in the cycle as given, while the latter two loosen the second assumption of knowing the size of the cycle. A QUBO formulation is derived for each approach. Further, the number of binary variables required to encode the maximum weighted cycle problem with one or both assumptions for the respective approach is made explicit. The problem is motivated by finding the maximum weighted debt cycle in a debt graph. This paper compares classical computing versus currently available (hybrid) quantum computing approaches for various debt graphs. For the classical part, it investigated the Depth-First-Search (DFS) method and Simulated Annealing. For the (hybrid) quantum approaches, a direct embedding on the quantum annealer and two types of quantum hybrid solvers were utilized. Simulated Annealing and the usage of the hybrid CQM (Constrained Quadratic Model) had promising functionality. The DFS method, direct QPU, and hybrid BQM (Binary Quadratic Model), on the other hand, performed less due to memory issues, surpassing the limit of decision variables and finding the right penalty values, respectively.
Accumulated micro-motion representations for lightweight online action detection in real-time

In the last decade, the explosive growth of vision sensors and video content has driven numerous application demands for automating human action detection in space and time. Aside from reliable precision, vast real-world scenarios also mandate continuous and instantaneous processing of actions under limited computational budgets. However, existing studies often rely on heavy operations such as 3D convolution and fine-grained optical flow, therefore are hindered in practical deployment. Aiming strictly at a better mixture of detection accuracy, speed, and complexity for online detection, we customize a cost-effective 2D-CNN-based tubelet detection framework coined Accumulated Micro-Motion Action detector (AMMA). It sparsely extracts and fuses visual-dynamic cues of actions spanning a longer temporal window. To lift reliance on expensive optical flow estimation, AMMA efficiently encodes actions’ short-term dynamics as accumulated micro-motion from RGB frames on-the-fly. On top of AMMA’s motion-aware 2D backbone, we adopt an anchor-free detector to cooperatively model action instances as moving points in the time span. The proposed action detector achieves highly competitive accuracy as state-of-the-arts while substantially reducing model size, computational cost, and processing time (6 million parameters, 1 GMACs, and 100 FPS respectively), making it much more appealing under stringent speed and computational constraints. Codes are available on https://github.com/alphadadajuju/AMMA.
MoCoUTRL: a momentum contrastive framework for unsupervised text representation learning

This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP). Firstly, MoCoUTRL employs multi-granularity semantic contrastive learning objectives, enabling a more comprehensive understanding of the semantic features of samples. Secondly, MoCoUTRL uses a dynamic dictionary to act as the approximately ground-truth representation for each token, providing the pseudo labels for token-level contrastive learning. The MoCoUTRL can extend the use of pre-trained language models (PLM) and even large-scale language models (LLM) into a plug-and-play semantic feature extractor that can fuel multiple downstream tasks. Experimental results on several publicly available datasets and further theoretical analysis validate the effectiveness and interpretability of the proposed method in this paper.
Quantifying Uncertainty using Bayesian Deep Learning and Deep Ensembles

Deep Learning algorithms have achieved success in predictive accuracy, but they still have shortcomings that restrict their use in high-risk industries like medicine, finance, etc. Quantifying uncertainty in these situations can help users properly account for the risk of incorrect predictions, which is especially important in high-risk situations. Both individual Bayesian Deep Learning (BDL) models and ensembles of standard deep neural networks have been used to estimate uncertainty. In this project, Bayesian deep learning models were trained and ensembles of those models were implemented that outperform the individual models in both accuracy and uncertainty quantification. Models were tested on out-of-distribution datasets obtained by rotating the existing datasets. This approach was chosen since, in realworld situations, most of the predictions are to be made on data that does not match the distribution of the training data.
D0L-System Inference from a Single Sequence with a Genetic Algorithm

In this paper, we proposed a new method for image-based grammatical inference of deterministic, context-free L-systems (D0L systems) from a single sequence. This approach is characterized by first parsing an input image into a sequence of symbols and then, using a genetic algorithm, attempting to infer a grammar that can generate this sequence. This technique has been tested using our test suite and compared to similar algorithms, showing promising results, including solving the problem for systems with more rules than in existing approaches. The tests show that it performs better than similar heuristic methods and can handle the same cases as arithmetic algorithms.
Detection and Analysis of Fraud Phone Calls using Artificial Intelligence

With an increase advancement of technology, fraud phone calls, including spams and malicious calls have become a major concern in telecommunication industry and causes millions of global financial losses every year. Fraudulent phone calls or scams and spams via telephone or mobile phone have become a common threat to individuals and organizations. Artificial Intelligence (AI) and Machine Learning (ML) has emerged as powerful tools in detecting and analyzing fraud or malicious calls. This paper presents an overview of AI-based fraud or spam detection and analysis techniques, along with its challenges and potential solutions. The novel fraud call detection approach is proposed that achieved high accuracy and precision. The Proposed approach was evaluated using a dataset of real-world fraudulent calls. And results demonstrate that the approach achieved high accuracy in detecting malicious calls and identifying potential indicators of frauds or spams. The analysis of fraud calls also provided insights into the tactics and methods employed by fraudsters, which can be used to develop countermeasures.
Improving Multi-Document Summarization with GRU-BERT Network

Multi-document summarization has been a challenging task due to the difficulties in capturing essential information from multiple sources and generating coherent and non-redundant summaries. In this proposed model, we address these challenges by leveraging the power of two popular natural language processing techniques, Bidirectional Encoder Representations from Transformers (BERT) and Gated Recurrent Unit (GRU). The Document Understanding Conference (DUC) dataset, a widely recognized benchmark dataset for multi-document summarization, was used to train and evaluate the model. By using BERT to generate contextual embeddings and GRU to capture sequence information, the proposed method outperforms previous methods in terms of summarization quality metrics such as ROUGE (RecallOriented Understudy for Gisting Evaluation). The proposed model has significant potential for use in various applications, such as news summarization, document summarization, and automated content creation. This study demonstrates that combining BERT and GRU models can effectively capture the contextual and sequential information in multi-document summarization, leading to high-quality summaries that overcome the limitations of previous methods.
Employing an ECC-Based Hybrid Data Encryption Method to Improve Multitenancy Security in Cloud Computing

Due to reduced key size, Elliptic Curve Cryptography (ECC) was utilized in this study for tenant authentication, data encryption, and decryption. The proposed ECC-based authentication technique efficiently defends against many related threats while allowing authorized users access to confidential data. The authors choose the exact and ideal values of elliptic curves to provide more secure data encryption by combining nature-inspired optimizations like the Moth Search Algorithm (MSA) with ECC. The DNS encryption and the ECC encryption technique are combined in the proposed encryption and decryption method. Analysis of the proposed method's security shows that it is effective against certain types of attacks, including denial-of-service attacks, spoofing attacks, reaction attacks, and simple both the selected ciphertext attack and text attacks.
Modelling of aircraft trajectories for emergency landing using kinematoid chains

Abstract Most methods that compute trajectories for un- or low-powered flight operate under simplifying assumptions such as constant curve radii and wind conditions. Likewise, changes of air density with altitude that lead to significant differences between equivalent airspeed (EAS) und true airspeed (TAS) are often not considered. Some approaches are based on Dubins paths, which are introduced in Dubins LE (Am J Math 79: 497, 1957). They combine three sections to form a trajectory, which is the shortest from a given start to an end position (In the original work, the position extended by the heading, is referred to as configuration . Since configuration has a different connotation in our context, we use the term state , which can contain other parameters in addition to the position and the heading, e.g. orientation, configuration of landing flaps, landing gear, etc.). A maximum distant landing spot can be reached this way. Often, the targeted landing spot is closer to the aircraft. If it is approached using a Dubins Path, the excess height must be dealt with. Here, we present a method addressing the problem directly, namely finding a trajectory which reduces the excess height over its entire length. Furthermore, it takes spatial and temporal changes of wind and air density into account. Several conditions influence the final shape of the trajectory. For example, avoidance of obstacles and predefined areas is easily achieved. Our method is motivated by kinematic chains, which are used in robotics and computer animation. We extend and modify this principle by incrementally transferring start and end states of the trajectory, modelled as state vectors, into each other. The resulting intermediate states form ends of chain links. To connect initial and final states through the resulting chain, we solve the inverse kinematic problem known from robotics. We extend it by several conditions, which are derived from the flight mechanical characteristics of the modeled aircraft on the one hand and from the desired properties of the trajectory on the other. Using practical examples, we will show the performance of this method, which we have efficiently implemented on off-the-shelf hardware. The method is suitable for systems that assist in the event of engine failures as well as for modeling planned un- or low-powered flights like continuous descent approaches or return flights of space gliders.
Fused image robust video watermarking technique using LWT, SVD and SWT

In today’s era medical video transactions have made healthcare better and economical. But there are concerns related to its security and a risk of misdiagnosis due to deterioration of some parameters of the video during transmission. In this scheme it is proposed to embed two watermarks into a medical video,one watermark is photo of the patient and another is the basic initial diagnosis by the doctor A unique video watermarking scheme is introduced which uses middle and low frequency bands of cover video and water mark. Two level LWT and SVD are used for embedding and extraction purpose. SWT is usedforfusion of watermarks.Several attacks have been applied to the watermarked video for extensive evaluation in terms of PSNR and correlation coefficient (CC).
Analysis of urban drainage system to avoid rain water overflow on the access road to Juanda Airport

Abstract The access road to Juanda Airport from Jalan Raya Waru (Aloha Resto) Sidoarjo is frequently flooded when it rains with a height of more than 30 cm along 1.6 km. This greatly annoys the flow of traffic to the airport. The airport can only be passed through this street and the Juanda Airport toll road. This research was conducted by observing and recording conditions during the occurrence of floods and analyzing the drainage facilities in the research area based on the 5-years design discharge because this location is a densely populated residential and industrial area. The results showed that the addition of channel capacity using U-ditch was not able to accommodate the 5-year planned discharge of approximately 2.670 m 3 /sec. Since around the industrial location, there is a natural water reservoir that can keep water up to 3.526 m 3 /sec, it is necessary to add a water pump to drain water to the temporary water reservoir location so that water does not stagnate on the access road to Juanda Airport.
Metaverse - A Forthcoming Lifestyle

In this article we have given a brief information which is directly helpful to anyone looking forward to gaining a commendable knowledge in the field of Metaverse. As the 3rd gen revolution of world technological advancements are happening with the flow of time, it is critical for an individual and the community to adapt with the growing technology, this article will assist those categories of people with a very good foundational information. The role of Blockchain for developing secure platform for deployment, Web 3.0 leading path towards higher interactive websites and data security and 5G and coming gen of networks providing ideal bandwidth becomes very important and critical as they are considered as the steppingstones for the evolution of metaverse and with the extensive aid of Virtual “ Mixed ” Augmented Reality, deployment of metaverse to the real world will be more effective and efficient.
Three-stage multi-objective feature selection with distributed ensemble machine and deep learning for processing of complex and large datasets

The need for analytical solutions using machine learning (ML) and deep learning (DL) has risen in this era of industry-wide informed decision-making. Data is now readily available and moving at fast speeds due to the maturation of data processing technologies and the development of new technologies in the Big Data, streaming, and IoT sectors. As a result, a transformation is required in how analytical systems handle data. A novel approach to learning, distributed learning, maybe a wise choice for managing huge and high-speed data while overcoming the difficulties presented by noisy and dynamic data sources. Data scientists can considerably increase their productivity using distributed learning by 1) distributing concurrent experiments across several devices and 2) significantly reducing training time by distributing a network's training across several devices. A learning strategy takes a long time, and the model's performance suffers in large datasets due to redundant features and the dimensionality curse. But most feature selection methods are unstable, selecting different subsets of features for different training datasets, resulting in varying levels of classification accuracy. A novel ensemble machine and deep learning technologies are also needed to improve accuracy in distributed learning. To analyze complex and large datasets, this paper proposed a three-stage multi-objective feature selection (TMFS) with distributed ensemble machine and deep learning (DEMDL). The proposed TMFS technique selects the best feature subset by combining multiple feature subsets. To achieve these goals, the TMFS technique employs 5 feature selection strategies in 3 stages: correlation coefficient, Fisher score, information gain, mean absolute deviation, and min-max normalization. SVM is used for machine learning, and Dl4jMlpCassifier is used for deep learning. This SVM and Dl4jMlpClassifier are combined in DEMDL using a stacking classifier. SVM and Dl4jMlpClassifier are utilized as base classifiers in this stacking classifier, and the RIPPER classifier is employed as the meta-classifier. The experimental finding demonstrates that compared to existing classification techniques, the TMFS with DEMDL technique offers higher accuracy, Precision, Recall, and F-measure.
Design of blockchain-enabled secure smart health monitoring system and its testbed implementation

Smart healthcare technology is transforming from the traditional healthcare system in every manner conceivably. Smart healthcare provides several advantages over the existing approaches. However, it suffers from healthcare data security and privacy issues. As the Internet attackers may get access to sensitive healthcare data through the use of various types of cyber attacks. In this paper, an architecture of a blockchain-enabled secure smart health monitoring system has been presented (in short, it is called as BSSHM). BSSHM consists of various health data monitoring sensors, i.e., temperature, heartbeat, etc., which monitor the real time health data of the different patients. The healthcare data of the patients can be transmitted to the connected health servers in a secure way, where this data can be stored securely for its various uses. The formal security verification of the proposed BSSHM is also done through the widely-accepted Scyther tool. It has been proved that BSSHM is able to defend various potential attacks.
Preliminary Perspectives on Information Passing in the Intelligence Community

Analyst sensemaking research typically focuses on individual or small groups conducting intelligence tasks. This has helped understand information retrieval tasks and how people communicate information. As a part of the grand challenge of the Summer Conference on Applied Data Science (SCADS) to build a system that can generate tailored daily reports (TLDR) for intelligence analysts, we conducted a qualitative interview study with analysts to increase understanding of information passing in the intelligence community. While our results are preliminary, we expect that this work will contribute to a better understanding of the information ecosystem of the intelligence community, how institutional dynamics affect information passing, and what implications this has for a TLDR system. This work describes our involvement in and work completed during SCADS. Although preliminary, we identify that information passing is both a formal and informal process and often follows professional networks due especially to the small population and specialization of work. We call attention to the need for future analysis of information ecosystems to better support tailored information retrieval features.
Non-Linear Optimization Method for Maximum Point Search in Functions with Corner or Cusp Points

A function is non-differentiable when there is a cusp or a corner point in its graph. To solve this problem, we propose a nonlinear optimization model whose objective function is the Euclidean distance function. To identify the maximum points of a function that has corner or cusp points, according to the proposed model, a series of segments are generated which are measured through the Euclidean distance, which are all perpendicular to the abscissa axis. Therefore, by maximizing the Euclidean distance, it is possible to identify the segment whose points represent the maximum of the function and its projection on the abscissa axis. The proposed model therefore could be an alternative to maximum point search methods in the presence of functions that have points of non-derivability.
Combined Gaussian Mixture Model and Pathfinder Algorithm for Data Clustering

Data clustering is one of the most influential branches of machine learning and data analysis, and Gaussian Mixture Models (GMMs) are frequently adopted in data clustering due to their ease of implementation. However, there are certain limitations to this approach that need to be acknowledged. GMMs need to determine the cluster numbers manually, and they may fail to extract the information within the dataset during initialization. To address these issues, a new clustering algorithm called PFA-GMM has been proposed. PFA-GMM is based on GMMs and the Pathfinder algorithm (PFA), and it aims to overcome the shortcomings of GMMs. The algorithm automatically determines the optimal number of clusters based on the dataset. Subsequently, PFA-GMM considers the clustering problem as a global optimization problem for getting trapped in local convergence during initialization. Finally, we conducted a comparative study of our proposed clustering algorithm against other well-known clustering algorithms using both synthetic and real-world datasets. The results of our experiments indicate that PFA-GMM outperformed the competing approaches.